System and method for identifying infection risk in dialysis patients

Abstract

A method and system for determining a risk of an infection for a patient is disclosed. In one embodiment, the system and method includes extracting patient data from one or more databases corresponding to a pool of patients under treatment; using the extracted patient data with one or more predictive models to generate a respective patient risk score for each patient in the pool of patients for developing an infection within a selected time period; generating a report including at least a portion of an identified subset of the pool of patients and their respective patient risk scores; and transmitting the report to one or more healthcare institutions that further identify one or more patients from the portion of the identified subset of the pool of patients for an intervention treatment, consultation, training, or a combination thereof.

Description

[0001] Cross Reference to Related Applications

[0002] This application is a non-provisional of, and claims priority to, pending U.S. Provisional Patent Application No. 62 / 692,198, filed June 29, 2018, entitled “Systems and Methods for Identifying Risk of Infection in Dialysis Patients,” and is a non-provisional of, and claims priority to, pending U.S. Provisional Patent Application No. 62 / 716,031, filed August 8, 2018, entitled “Systems and Methods for Identifying Risk of Infection in Dialysis Patients,” and is a non-provisional of, and claims priority to, pending U.S. Provisional Patent Application No. 62 / 836,822, filed April 22, 2019, entitled “Artificial Intelligence & Predictive Medicine in Dialysis,” the entire contents of which are expressly incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates generally to healthcare related systems, devices, and methods. BACKGROUND

[0004] Traditional healthcare systems are based on a fee-for-service model, whereby healthcare providers can get compensated per treatment or per service provided. In this model, as the number of treatments or services provided increases, the compensation for the medical service provider also increases. Thus, there is no financial incentive for such providers to efficiently manage the number of services / procedures provided, nor is there any financial incentive related to the overall health outcomes of the patients. Such traditional systems result in ever-increasing healthcare costs and inefficiencies, which hinder the quality of overall healthcare for patients.

[0005] Further, many patients, particularly those with chronic conditions, are in contact with a variety of different entities and healthcare professionals during their diagnosis, treatment, and long-term healthcare management, including hospitals, clinics, laboratories, pharmacies, physicians, clinicians, and / or other specialists. A patient's treatment information can be scattered among multiple entities, repositories, and healthcare professionals, which can result in a lack of or poor communication among the various related entities, which can adversely affect the patient's treatment and health, and can even result in life-threatening treatment conditions. Moreover, this uncoordinated data handling and the patient's overall treatment results in inefficiencies, which can result in an increase in overall healthcare costs. In this regard, the traditional fee-for-service healthcare model is far from ideal in terms of healthcare quality and economics. This situation is evidenced by the unsustainable rise in healthcare costs in the United States under the fee-for-service model.

[0006] For these and other considerations, improvements can be useful. SUMMARY

[0007] The following presents a summary to provide a basic understanding of some aspects of the disclosure. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure. The disclosure can include various aspects and embodiments.

[0008] According to one example embodiment of the present disclosure, a method for determining a risk of an infection occurring in a patient is disclosed. In one embodiment, the method includes extracting patient data from one or more databases corresponding to a pool of patients under treatment; using the extracted patient data with one or more predictive models to generate a respective patient risk score for each patient in the pool of patients for an infection occurring within a selected time period; generating a report including at least a portion of an identified subset of the pool of patients and their respective patient risk scores; and transmitting the report to one or more healthcare institutions that further identify one or more patients from the portion of the identified subset of the pool of patients for intervention treatment, consultation, training, or a combination thereof.

[0009] In this and other embodiments, the method further includes the one or more predictive models being arranged and configured to: analyze the extracted patient data to identify patient characteristics common to patients having a previously recorded report of an infection; and identify the patient characteristics for each patient in the pool of patients when generating the patient risk score for an infection occurring within the selected time period.

[0010] In this and other embodiments, the method further includes the one or more predictive models being arranged and configured to be capable of: analyzing the extracted patient data to identify patient characteristics common to those patients for which no previously documented infection report.

[0011] In this and other embodiments, the method further includes the one or more predictive models being arranged and configured to be capable of: identifying characteristics of patients previously diagnosed with an infection; and analyzing the extracted patient data according to the common characteristics.

[0012] In this and other embodiments, the method further includes the intervening treatment, consultation, or training including: sending a survey to one or more patients to obtain additional information about the patient's dialysis performance; contacting one or more patients to determine appropriate interventions to help minimize the risk of developing an infection; contacting one or more patients to assess the patient's dialysis treatment; changing one or more conditions related to the patient's dialysis performance; or dispatching a medical professional to one or more patients in the portion of the subset of the identified pool of patients for an in-home visual assessment; or combinations thereof.

[0013] In this and other embodiments, the method further includes the report being generated at a predetermined periodicity.

[0014] In this and other embodiments, the method further includes the subset of the identified pool of patients including patients in a similar geographic area, patients assigned to a dialysis clinic, or a group of patients receiving healthcare from a single medical professional, or combinations thereof.

[0015] In this and other embodiments, the method further includes the report including a subset of the pool of patients for which a respective patient risk score is above a predetermined threshold.

[0016] In this and other embodiments, the method further includes the predetermined threshold being determined by the one or more predictive models based on historical data.

[0017] In this and other embodiments, the method further includes the report including all patients associated with a particular medical group.

[0018] In this and other embodiments, the method further includes the report including one or more associated causes for each patient.

[0019] In this and other embodiments, the method further includes the extracted patient data including patient demographic characteristics, laboratory values, documented information, physician notes, or treatment data, or combinations thereof.

[0020] In this and other embodiments, the method further includes that the patient demographic characteristic includes gender, race, age, or marital status, or a combination thereof.

[0021] In this and other embodiments, the method further includes that the laboratory value includes an albumin level of the patient, a calcium level of the patient, a chloride level of the patient, a creatinine level of the patient, or a transferrin saturation (TSAT) level of the patient, or a combination thereof.

[0022] In this and other embodiments, the method further includes that the laboratory value includes a time period in which the patient has been receiving dialysis treatment, a time period in which the patient was last diagnosed with an infection, a total number of previous infections of the patient, or a distance of the patient’s home from a dialysis facility, or a combination thereof.

[0023] According to one example embodiment of the present disclosure, a system for determining a risk of a patient developing an infection is disclosed. In one embodiment, the system includes an integrated healthcare system configured to: extract patient data from one or more databases corresponding to a pool of patients receiving treatment; use the extracted patient data using one or more predictive models to generate, for each patient in the pool of patients, a respective patient risk score of developing an infection within a selected time period; generate a report including at least a portion of an identified subset of the pool of patients and their respective patient risk scores; and transmit the report to one or more healthcare facilities that further identify one or more patients from the portion of the identified subset of the pool of patients for intervention treatment, counseling, training, or a combination thereof.

[0024] In this and other embodiments, the system includes that the one or more predictive models are arranged and configured to: analyze the extracted patient data to identify patient characteristics common to patients having previously recorded infection reports; and identify the patient characteristics of each patient in the pool of patients when generating the patient risk score of developing an infection within the selected time period.

[0025] In this and other embodiments, the system includes that the one or more predictive models are arranged and configured to: analyze the extracted patient data to identify patient characteristics common to those patients having no previously recorded infection reports.

[0026] In this and other embodiments, the system includes that the one or more predictive models are arranged and configured to: identify characteristics of patients previously diagnosed with an infection; and analyze the extracted patient data according to the common characteristics.

[0027] In this and other embodiments, the system includes the intervention therapy, consultation, or training includes: sending a survey to one or more patients to obtain additional information about the patient's dialysis performance; contacting one or more patients to determine appropriate interventions to help minimize the risk of infection; contacting one or more patients to assess the patient's dialysis treatment; changing one or more conditions related to the patient's dialysis performance; or dispatching a medical professional to one or more patients in the portion of the subset of the identified pool of patients to conduct an in-home visual assessment; or a combination thereof.

[0028] In this and other embodiments, the system includes the report is generated at a predetermined periodicity.

[0029] In this and other embodiments, the system includes the subset of the identified pool of patients includes patients in a similar geographic region, patients assigned to a dialysis clinic, or a group of patients receiving healthcare from a single medical professional, or a combination thereof.

[0030] In this and other embodiments, the system includes the report includes a subset of the pool of patients having a respective patient risk score above a predetermined threshold.

[0031] In this and other embodiments, the system includes the predetermined threshold is determined by the one or more predictive models based on historical data.

[0032] In this and other embodiments, the system includes the report includes all patients associated with a particular medical group.

[0033] In this and other embodiments, the system includes the report includes one or more associated causes for each patient.

[0034] In this and other embodiments, the system includes the extracted patient data includes patient demographic characteristics, laboratory values, recorded information, physician annotations, or treatment data, or a combination thereof.

[0035] In this and other embodiments, the system includes the patient demographic characteristics include gender, race, age, or marital status, or a combination thereof.

[0036] In this and other embodiments, the system includes the laboratory values include a patient's albumin level, a patient's calcium level, a patient's chloride level, a patient's creatinine level, or a patient's transferrin saturation (TSAT) level, or a combination thereof.

[0037] In this and other embodiments, the system includes the laboratory values include a time period in which the patient has been receiving dialysis treatment, a time period in which the patient was last diagnosed with an infection, a total number of previous infections of the patient, or a distance of the patient's home from a dialysis facility, or a combination thereof. Other features and aspects are described in further detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0038] Embodiments of the disclosed methods and apparatus will now be described, by way of example only, with reference to the attached figures, wherein:

[0039] Figure 1A is a flowchart illustrating one example embodiment of a method for determining and managing infection risk in dialysis patients according to the present disclosure;

[0040] Figure 1B is a flowchart illustrating one example embodiment of a process for determining and managing infection risk in dialysis patients according to the present disclosure;

[0041] Figure 1C is one example embodiment of a patient data chart for determining and managing infection risk in dialysis patients according to the present disclosure;

[0042] Figure 1D is a flowchart illustrating one example embodiment of a process for determining and managing infection risk in dialysis patients according to the present disclosure;

[0043] Figure 2A is a diagram illustrating one example embodiment of a system for providing coordinated healthcare according to the present disclosure;

[0044] Figure 2B is a diagram illustrating one example embodiment of a system for assessing and treating diseases including kidney disease according to the present disclosure;

[0045] Figure 3 is a block diagram illustrating one example embodiment of a comprehensive healthcare system according to the present disclosure;

[0046] Figure 4 is a block diagram illustrating one example embodiment of an operating environment according to the present disclosure;

[0047] Figure 5 is a block diagram illustrating one example embodiment of another operating environment according to the present disclosure;

[0048] Figures 6-10 is a diagram illustrating one example embodiment of components of a system for providing coordinated healthcare according to the present disclosure;

[0049] Figure 11is a diagram illustrating an exemplary embodiment of a healthcare coordination component of a system for providing coordinated healthcare according to the present disclosure;

[0050] Figure 12 A schematic diagram of an exemplary embodiment of a dialysis machine is shown;

[0051] Figures 13A-13B An exemplary embodiment of a dialysis system according to the present disclosure is shown;

[0052] Figure 14 is a diagram illustrating another exemplary embodiment of a dialysis system according to the present disclosure; and

[0053] Figure 15 is a block diagram illustrating one exemplary embodiment of a computing architecture according to the present disclosure. DETAILED DESCRIPTION

[0054] The present embodiments will now be described more fully below with reference to the accompanying drawings, in which several exemplary embodiments are shown. However, the subject matter of the present disclosure can be implemented in many different forms and types of methods and devices for dialysis machines and other potential medical devices, diagnostics, and treatments for various diseases, and should not be construed as being limited to only the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and are intended to convey the scope of the subject matter to those skilled in the art. In the drawings, the same or similar reference numerals represent the same or similar elements throughout.

[0055] The exemplary embodiments described herein are suitable for implementing value-based care, an alternative to the fee-for-service healthcare model. Under a value-based healthcare system (also known as a "pay-for-performance" model), healthcare providers receive financial incentives tied to the quality and efficiency of care and patient outcomes.

[0056] Some exemplary embodiments are configured to provide coordinated care to a patient population with chronic conditions such as chronic kidney disease (CKD). CKD is a progressive disease characterized by decreased kidney function. Once kidney function declines below a threshold, the patient is considered to have kidney failure or end-stage renal disease (ESRD). ESRD is the final stage of CKD and requires dialysis treatment for the patient's lifetime (without a transplant).

[0057] One model of value-based healthcare in which the exemplary embodiments described herein can be implemented is the Comprehensive ESRD Care (CEC) model, which is an Accountable Care Organization (ACO) model developed under the auspices of the Center for Medicare and Medicaid Innovation in the United States. To implement the CEC model, an ESRD Seamless Care Organization (ESCO) is formed. The ESCO is an ACO formed by healthcare providers and suppliers coming together voluntarily. The resulting ESCO is a legal entity that provides coordinated care for ESRD beneficiaries through the CEC model.

[0058] Under the ESCO model, the ESCO shares in the savings and losses brought about by the Centers for Medicare and Medicaid Service (CMS) for the beneficiaries of the ESCO. The savings or losses are determined by CMS based on an expenditure benchmark, which is derived from a baseline that reflects historical expenditure data for similar or comparable beneficiaries. This benchmark is compared to the actual Fee-For-Service (FFS) Part A and Part B expenditures for the patient population adjusted over the performance year. The savings will also be adjusted based on quality performance. Any reduction in costs translates directly into increased shared savings (profit) because costs are measured against a predetermined benchmark. Healthcare quality is incentivized by adjusting the calculated shared savings for quality performance.

[0059] The ESCO is responsible for the overall healthcare of each patient, which extends beyond dialysis treatment. For example, if a patient is hospitalized for any reason (e.g., infection, vascular dialysis access complications, and / or cardiac complications), the cost of the hospitalization is counted against the annual savings calculation. Since hospitalization costs are particularly expensive, it is financially advantageous for the ESCO to keep patients out of the hospital. The exemplary embodiments described herein implement an overall approach to oversee and manage all aspects of patient well-being, which improves healthcare quality while increasing the efficiency of healthcare resources and overall cost efficiency.

[0060] Some of the exemplary embodiments described herein analyze medical data for the applicable patient population in order to reduce the likelihood of hospitalization through intervention for high-risk patients. Some exemplary embodiments analyze patient data to predict when a patient can experience a particular health-related event or a particular stage of disease progression, and provide / adjust treatment accordingly.

[0061] According to example embodiments, patient information can be sent to, managed within, and / or accessible by a coordinated healthcare system so that patients can receive high quality, efficient, coordinated healthcare within a managed system that is capable of intelligently managing and coordinating a patient's overall healthcare. Incorporation of a coordinated healthcare system can, for example, better control healthcare costs by replacing fee-for-service healthcare with value-based healthcare for patients. For example, as noted above, the patient population diagnosed with ESRD is increasing over time, often due to a variety of other diseases including, but not limited to, diabetes, hypertension, and / or glomerulonephritis. Due to the nature of the disease, patients with ESRD can face additional challenges. For example, necessary lifestyle changes can lead to deterioration in mental health. Furthermore, in-home treatment can lead to increased isolation from medical professionals. With the changing landscape of healthcare, the opportunity to provide patients with resources for coordinated treatment can lead to additional patient health benefits beyond dialysis treatment.

[0062] While example embodiments described herein relate to kidney disease, it is understood that the coordinated healthcare system and infrastructure described herein can be applicable to other chronic diseases in addition to, or instead of, kidney disease. As non-limiting examples, such other diseases can include cardiovascular-related diseases, pulmonary, gastrointestinal, neurological, urological, or gynecological diseases, diabetes, circulatory system diseases, Alzheimer's disease or other dementia, asthma, COPD, emphysema, cancer, obesity, smoking, cystic fibrosis, or combinations thereof. Furthermore, while some examples are described with respect to implementation in a kidney-related ACO such as an ESCO, it is understood that examples described herein can be similarly implemented in other ACOs for other diseases or patient populations and / or any other suitable value-based healthcare model.

[0063] Patients with CKD and / or ESRD are receiving long-term care for kidney disease, for example, through dialysis treatment. For example, some patients can receive dialysis treatment through peritoneal dialysis (see also Figures 13A-13B As described below, patients can need to change one or more dialysate bags during peritoneal dialysis treatment, during which time a catheter used to access the patient's peritoneum can become contaminated and lead to infection, such as peritonitis. Peritonitis, an inflammation of the peritoneum often caused by bacterial or fungal infection, can cause a patient to have to modify current peritoneal dialysis treatment, receive retraining in peritoneal dialysis treatment methodology, or switch dialysis modalities, for example, the patient is no longer a candidate for peritoneal dialysis. The patient can be switched to a hemodialysis procedure, which is typically implemented in a clinical setting (see Figure 14). For less active patients, patients living in rural areas, and / or patients with other ailments, it can be difficult to coordinate appointments for regular dialysis at a clinic facility, such that the patient can miss receiving critical dialysis treatments.

[0064] According to example embodiments of the present disclosure, a healthcare architecture can be configured to identify and treat patients at risk of infection, e.g., peritonitis. Large amounts of patient data can provide information to the system, such that a machine learning model can be trained to determine patient characteristics from recorded peritonitis reports and historical patient data without a definitive infection diagnosis to identify patients at risk of developing peritonitis within a selected time period in the future, so that the patient can be provided with an intervention treatment and / or training to minimize the risk of infection, or reduce the extent, severity, and / or frequency of future infections. By minimizing or avoiding infection, the patient can continue to receive peritoneal dialysis treatment in a home environment, with the greatest reduction in possible missed treatments and other advantages. It will also be appreciated that the system and methods according to the present disclosure can be used to provide similar methods for identifying and / or treating patient infection risks from treatments other than dialysis.

[0065] For example, in various embodiments, the healthcare architecture can compare historical patient data that developed an infection within a selected time period to patient data. The healthcare architecture can also evaluate the patient data for respective infection risks and / or identify patient data that is at risk of infection. The healthcare architecture can further generate an intervention medical treatment to the patient based on the identified infection risk. In various embodiments, the risk of developing an infection includes the patient receiving dialysis treatment in a first modality of peritoneal dialysis. The intervention treatment can include retraining the patient regarding the first modality of peritoneal dialysis, modifying the patient’s peritoneal dialysis treatment, and / or transitioning the patient from the first modality to a second modality of hemodialysis. Other interventions can include changing how and / or under what conditions the patient performs peritoneal dialysis treatment. Examples of such changes can include: (a) modifying equipment settings and / or performance of peritoneal dialysis operations to conform to guidelines, (b) modifying equipment storage practices to conform to guidelines, (c) reducing or eliminating instances of manipulating catheters without proper hand washing, (d) increasing utilization of contamination prevention supplies such as gloves and masks, and (e) eliminating or reducing environmental sources of equipment contamination. Such interventions can be implemented, for example, by educating / guiding the patient (and / or other individuals that can be involved in the treatment or the environment in which the treatment occurs) and / or providing appropriate equipment and / or supplies.

[0066] In an embodiment, patient demographics, data (e.g., laboratory test results), and recorded information (e.g., treatment notes) for patients receiving dialysis treatment may be analyzed by one or more programs and / or algorithms to determine the patient's risk of developing an infection within a future time period. In some embodiments, the risk level may be assessed to determine the likelihood of an infection developing within a month. In other embodiments, the time period may be less than a month and / or greater than a month. The healthcare architecture may be configured to execute a method for determining a patient's risk of developing an infection (e.g., peritonitis) to generate a report by analyzing the risk level of a selected patient. For example, Figure 1A A flow chart of an exemplary method 100 for determining a patient's risk of developing an infection is shown. At step 105, data may be extracted. Figures 6-11 As shown, data from selected patients may be extracted by the integrated healthcare system or healthcare analysis and guidance system from other clinical systems, external systems, and / or other databases.

[0067] As mentioned above, in Figure 1A In step 105, the integrated healthcare system may utilize many variables including patient demographics, laboratory values, treatment data, and comprehensive assessments. For example, Figure 1BAn exemplary embodiment of a chart 130 is shown of variables 135a, 135b, ..., 135n that may be included to determine a patient's risk of developing an infection (e.g., peritonitis). It should be understood that any number "n" of variables may be used to determine patient risk. Patient demographics may include gender and race, as well as age, marital status, and the like. Laboratory values ​​may include patient data such as albumin, calcium, chloride, creatinine, transferrin saturation (TSAT), and the like. Furthermore, laboratory data may be calculated in various ways, such as to provide averages, maximums, minimums, peaks, and / or valleys, standard deviations, and trend values ​​over a specified time period for analysis by the integrated healthcare system. Additional variables may also be included, such as the time period (e.g., total number of days) that the patient has received or self-administered home dialysis treatment, the time period (e.g., total number of days) when the patient was last diagnosed with an infection, the total number of previous infections the patient has had, and / or the distance of the patient's home from the dialysis facility. In some embodiments, the patient data elements used in the predictive model can be sourced from a data warehouse, knowledge center, and / or central data repository that can be periodically (e.g., daily) updated with new patient information from one or more case data collection systems used by dialysis patient care providers (e.g., nurses, physicians, nutritionists, social workers, etc.). The data warehouse can also include other patient parameters such as lifestyle and other community-level psychosocial indicators from third-party vendors that are matched to the patient. For example, community-level metrics can be matched to the patient based on geographic location, e.g., a geographic location defined by an associated zip code.

[0068] It should be understood that raw data variables can be transformed through feature engineering to create additional variables and / or features based on analysis of data patterns and clinical experience. The data may include a patient's vital signs, such as blood pressure, weight, pulse, temperature, respiratory rate, etc. The data can also be calculated in various ways, for example, to provide average, maximum, minimum, peak and / or trough values, and trend values ​​over a specified time period for analysis by integrated healthcare systems. Similar to laboratory values, raw data variables can be transformed through feature engineering to create additional variables and / or features based on analysis of data patterns and clinical experience.

[0069] Return Reference Figure 1A , in step 110, for example, referring to Figure 1B The extracted patient data described in step 105 may be processed by the integrated healthcare system. The patient data for processing may be measured data, calculated data, written notes, etc. Some of the extracted data (e.g., measured data, calculated data) may be numerical, while some (e.g., clinician's notes) may be text and / or graphical.

[0070] In some examples, at processing step 110, some or all of the patient data in the form of digital data may be processed into a more suitable form for further analysis by the integrated healthcare system. This processing may include, for example, scaling and conversion. In some examples, this processing may include filtering to ensure that the data is within a feasible range, thereby filtering out or identifying erroneous data. In some examples, some or all of the extracted digital data remains in its original, unmodified form by processing step 110.

[0071] According to some examples, also at step 110, the extracted graphical data (e.g., handwritten clinician annotations) is converted to text data before further analysis. In some examples, this can be achieved by applying word2vec and / or convolutional neural networks, but other suitable algorithms may be used instead of or in addition to these algorithms. The word2vec algorithm may include CBOW (continuous bag of words) and / or a distributed representation of words.

[0072] Analyzing textual data, including any text identified from the graphics as described above, for the presence of words, phrases, and / or word approximations that indicate conditions and / or events corresponding to those observed by the patient and / or treating particular clinician. In some examples, the conditions and events are identified by the integrated healthcare system from a set of conditions and events stored in a database. When these conditions and events are associated via textual analysis, the integrated healthcare system creates or modifies one or more numerical identifiers to reflect these conditions and events. In some examples, when the text identifying the condition indicates a degree or level of severity (e.g., "pain level 7.5 / 10" or "high," "low," etc.), the integrated healthcare system can generate or modify the numerical identifier so that it reflects the degree or severity. The numerical identifier allows further analysis of the presence and / or severity of one or more conditions by the integrated healthcare system.

[0073] In some embodiments, as Figure 1A As shown in step 115 in the illustrated example, the processed data (including any numerical identifiers corresponding to the textual data) can be sent to one or more algorithms of the integrated healthcare system for analysis and prediction. As described above, the integrated healthcare system can use historical patient data to train a machine learning model to analyze current patient data to identify the risk of future infection.

[0074] For example, at step 115, one or more algorithms may analyze selected patient data to determine the patient's risk of developing an infection such as peritonitis within a selected time period (e.g., one month). As described above, the integrated healthcare system may utilize Figure 1BThe patient data described herein are used to determine a risk score for each selected patient to develop an infection within the next month. In some examples, the algorithm (e.g., gradient boosting architecture and / or extreme gradient boosting tree algorithm) can analyze the data of a particular patient in the context of corresponding data from a pool of other patients.

[0075] In some examples, the integrated healthcare system's analytical / predictive algorithm examines and analyzes the entire patient pool to assign each patient a corresponding risk score for developing an infection within a preselected time period. However, it should be understood that in some examples, the algorithm may also be performed on a single patient or any suitable number of patients selected from the pool.

[0076] At step 120, a document may be generated by the healthcare framework, which may be used by the healthcare navigation unit 230. Referring now to Figure 1C, the report 140 can be generated by the integrated healthcare system for a selected group of patients and identify patients at risk of developing an infection within a selected time period. The report 140 can be generated by the integrated healthcare system by periodically running the selected patient data against the trained machine learning model (e.g., the report 140 can be generated monthly for identifying patients at risk of developing peritonitis within the next month). In some embodiments, the selected group of patients can be patients in a similar geographic region, patients assigned to a dialysis clinic for treatment, a group of patients receiving healthcare from an individual medical professional, and / or other groupings of patients. In some embodiments, the report 140 can indicate the patient (e.g., name, medical record number, or other identifying characteristic such as a social security number), a risk score associated with developing an infection (e.g., peritonitis) within a selected time period (e.g., within one month), and associated causes for each patient. For example, patient John Smith can be determined by the integrated healthcare system to be at risk of developing an infection with a calculated risk score of approximately 82.67%. The associated causes determined by the integrated healthcare system can include laboratory value abnormalities (e.g., John Smith’s albumin, chloride, and TSAT levels were determined to be outside of acceptable ranges), and the patient last had an infection 45 days prior to the report generation. It should be understood that the causes associated with the risk score determination for John Smith can include written notes of the patient’s healthcare by a medical professional, and / or the integrated healthcare system can calculate one or more parameters (e.g., albumin, chloride, TSAT, etc.) based on, for example, patient vital signs measured at a dialysis treatment. The report 140 can also include additional patients, such as Jane Doe, who can also be identified as being at risk of developing an infection within a future time period. For example, Jane Doe can have a risk score of 81.75% of developing an infection (e.g., peritonitis) within a selected time period (e.g., within one month) based on laboratory values such as levels of creatinine, calcium, and chloride, and a prior infection 37 days prior to the report generation.

[0077] In embodiments, the report 140 can be generated so as to rank the patients according to their associated calculated probabilities. This report 140 can be provided to the healthcare coordination unit 125 (e.g., healthcare navigation unit) so that a medical professional can evaluate the patient parameters and recommend a treatment intervention. Referring back to Figure 1A At step 125, the report 140 can be used for intervention and patient treatment. Device (e.g., catheter) contamination is one factor that can lead to a patient developing an infection as the patient inserts a catheter into the abdomen for peritoneal dialysis treatment. Furthermore, since peritoneal dialysis procedures are typically performed in a home environment, the patient can not be supervised and / or can inadvertently contaminate the device. Referring now to Figure 1DThe flowchart 150 illustrates one exemplary embodiment of a process for processing the report 140 and intervening patient treatment to minimize the risk of a patient developing an infection, such as peritonitis, for a healthcare architecture 200.

[0078] At step 152, the patients identified on the report 140 can receive additional counseling and intervening treatment. In some embodiments, the report 140 can identify only patients that are deemed to be at risk of infection in the next month. In some embodiments, the report 140 can identify an entire selected patient population, such as all patients associated with a medical professional (e.g., a primary care physician) and / or associated with a selected geographic region. At step 154, a patient can be identified for further evaluation by a medical professional if the patient risk score is greater than a predetermined threshold "R PS ". In some embodiments, the predetermined threshold R PS may be any determined risk score level that is deemed to place a patient at risk. For example, the analysis of the patient population can determine that the predetermined threshold R PS is 50%. In some embodiments, the predetermined threshold can be determined by the analysis to be any risk score between approximately 50% and 80%. If the patient risk score is less than the predetermined threshold, the patient can continue to be monitored at step 156, for example, the integrated healthcare system or the healthcare analysis and guidance system can continue to run the patient data against the machine learning model for evaluation in the next month.

[0079] The patients identified at step 154 as being at risk of developing an infection can initially receive a questionnaire or other type of survey at step 158 regarding additional information about their preparation and performance of home peritoneal dialysis, which can be directed to a medical professional for evaluation and / or included in the integrated healthcare system 220. The questionnaire can include general questions about the patient's preparation and performance of their home dialysis. For example, the patient can be storing dialysis supplies and / or equipment in an area that is accessible to pets, which can lead to contamination. In some embodiments, the patient can be forgetting to wash their hands or not washing their hands thoroughly before handling dialysis supplies, or the patient can not be wearing safety protective devices, such as gloves and / or masks, to minimize the risk of contamination, which can be quickly determined by the patient questionnaire. If the patient is properly preparing and performing dialysis at home and adhering to the instructions to minimize contamination, additional follow-up by a medical professional can not be needed (see step 160), and at step 162, the patient can be provided with additional educational resources to remain infection free.

[0080] In some embodiments, based on the survey, the patient can be identified as benefiting from additional intervention by a medical professional at step 160. At step 164, a nurse, clinician, or other medical professional can contact the identified patient to determine appropriate intervention measures to help minimize or eliminate the risk of developing or continuing an infection. In some embodiments, the medical professional can contact the patient for an assessment of dialysis treatment, such as a phone call, to discuss the patient’s in-home dialysis procedure. The medical professional can be able to verbally assess whether the patient is following recommended equipment storage guidelines, hand washing policies before handling the catheter, and / or other standard procedures for minimizing the risk of catheter contamination. The medical professional can also verify whether the patient has the appropriate equipment and anti-contamination supplies, such as gloves and / or masks, whether the patient has one or more pets that can potentially contaminate the equipment, and / or whether another family member or caregiver can have inadvertently caused equipment contamination.

[0081] At step 166, the medical professional can assess whether the additional factors identified by the patient can be related to an increased risk of developing an infection. For example, the patient can have indicated confusion related to the setup and / or performance of the dialysis procedure, and / or the patient can have indicated a complex home environment that can increase their risk of infection from equipment contamination. As described above, patients with kidney disease can have mobility issues and can not have sufficient activity to maintain the level of cleanliness required in the home for proper dialysis preparation and performance.

[0082] If no additional factors are identified at step 166, the patient can be provided with additional educational resources that highlight best practices and / or standardized instructions for preparing and performing dialysis at home at step 162. The patient can then continue to receive monthly monitoring to determine the risk of future infections.

[0083] If the verbal assessment with the patient indicates additional factors that increase the risk of contamination, the medical professional can be sent to the patient’s home for a home visit and visual assessment at step 168. In some embodiments, the medical professional can be sent to the patient’s home to identify and potentially improve areas of concern for sources of infection, such as contamination. For example, a home nurse can visually observe the patient’s peritoneal dialysis preparation and performance and determine areas for improvement to reduce the risk of component contamination. Patients with a history of infection can be more susceptible to future infections. Therefore, patients who continue to use peritoneal dialysis as a dialysis treatment modality can require higher standards of cleanliness in an effort to reduce potential contamination.

[0084] In some embodiments, after the visual assessment of the patient's peritoneal dialysis self-administration at step 168, the medical professional can determine that the patient can continue peritoneal dialysis with retraining. For example, the patient can benefit from additional prompts from the standard procedure to avoid component contamination, thereby minimizing the risk of developing peritonitis. In some embodiments, the visual assessment can cause the medical professional to assess that the risk of developing an infection is so great that the patient can no longer benefit from this type of dialysis modality (e.g., home peritoneal dialysis). The medical professional can switch the patient from home peritoneal dialysis to hemodialysis, either administered at home and / or administered in a clinic setting, to further minimize and / or avoid developing infections such as peritonitis.

[0085] In embodiments, by managing the risk of developing peritonitis, the visual assessment can allow more patients to continue receiving peritoneal dialysis treatment for longer periods of time. As described above, patients can be less inclined to go to a clinic for a hemodialysis appointment due to transportation issues, mobility issues, and / or other medical conditions and / or difficulties. However, missing a dialysis appointment can lead to additional complications and / or the development of other medical conditions related to kidney disease. Thus, according to the present disclosure, it can be advantageous to keep a patient on peritoneal dialysis as long as the patient is not unnecessarily at risk of developing peritonitis. It should also be appreciated that peritoneal dialysis, as a modality for administering dialysis treatment, can be more advantageous than other modalities for other medical reasons.

[0086] Reference Figure 2A According to one example of the present disclosure includes a coordinated health architecture 200 for treating a patient or patient population 240. The overall health of the patient / population 240 is overseen and coordinated by a health coordination system 210. The health coordination system 210 includes a health analysis and guidance system 220 (which can be referred to interchangeably herein as a "comprehensive health system") that receives, analyzes, and creates data for coordinating the health of the patient / population 240. The health coordination system 210 utilizes a care navigation unit (CNU) 230 that implements coordinated care according to data received from the health analysis and guidance system 220. To manage the overall health and well-being of the patient / population 240, the health coordination system 210 communicates with a number of related entities and components. In Figure 2A In

[0087] In Figure 2AIn the illustrated example, the coordinated health system 210 coordinates health care for the patient 240 among entities including a chronic health center or clinic 241, physicians 242 (which can include nephrologists, in particular for kidney patients), nurses 243, laboratories 244 (e.g., blood laboratories or other diagnostic laboratories), pharmacies 245, hospitals 246, medical devices 247 (e.g., dialysis machines or other medical treatment / monitoring devices), urgent care clinics 248, specialty services 249, counseling and mental health services 250, nutritionists / dietitians 251, transportation services 252, providers of medical equipment and supplies 253, ambulatory surgical centers (ASC) 254, additional services 255, medical records 256, financial and billing records 257, and payers 258 (e.g., CMS or private insurance companies).

[0088] It should be appreciated that some example embodiments can include other entities not shown, and / or can exclude some of the entities shown. Moreover, it should be appreciated that the communication channels shown are not exclusive, and that the various entities can also communicate directly or indirectly among each other and / or the patient 240 as appropriate. In some examples, communication between the coordinated health system 210 and one or more of the other entities can be indirect, through one or more intermediary entities. For example, coordination of the nurses 243 can be conducted directly between the coordinated health system 210 and the nurses 243, or via an intermediary channel such as the clinics 241, 248, the hospitals 246, or any other suitable channel.

[0089] According to some examples, Figure 2A The architecture 200 can be used to treat diseases such as progression of kidney disease, for example, end-stage renal disease (ESRD) and / or chronic kidney disease (CKD). A patient with ESRD is a patient who is receiving long-term care for kidney disease, for example, through dialysis treatment. The health care architecture 200 can identify a risk score for a patient who is developing an infection, for example, peritonitis, over a predetermined period of time. Monitoring health condition trends for dialysis patients can present challenges. For example, patients can exhibit varying degrees and irregularities of functional / cognitive impairment, and can be accompanied by complex clinical abnormality phenomena that are not related to the length of time the patient is on dialysis. By identifying a risk score, the patient can receive an intervention treatment that can change or reduce the risk of developing an infection. For example, proactively addressing underlying issues can reduce or even eliminate the risk of the patient developing an infection. According to example embodiments of the present disclosure, the coordinated health architecture 200, including the health care analytics and guidance system 220, can utilize one or more procedures and / or algorithms to identify a risk of infection for a patient and provide intervention treatment options to reduce and / or eliminate the risk of infection.

[0090] The Care Analysis and Guidance System (IACS) 220 may include and implement various healthcare-related models and / or programs. In some examples, these models and / or programs are specifically adapted for implementing or executing a particular value-based care framework (e.g., the ESCO model, other ACO models, Chronic Special Needs Plans (C-SNPs), etc.), while other examples may include models / programs that are generally applicable to multiple value-based care frameworks. It should also be understood that other types of value-based care models may be provided for other chronic conditions, including, but not limited to, chronic kidney disease or one or more of the other chronic diseases and conditions listed above. These healthcare models may improve the provision of value-based care to patients, for example, by more efficiently managing patient care within a designated structure, and may replace traditional fee-for-service (FFS) models. Fee-for-service models may often focus on quantity rather than quality of personalized patient care, with little incentive to improve the overall health of patients, and may be less efficient and less effective than value-based models.

[0091] Transforming patient care from a traditional fee-for-service model to a value-based healthcare model can improve the care patients receive, reduce overall costs, and improve the management of a large number of patient populations diagnosed with the same chronic disease. For example, as described above, a value-based healthcare model can pay providers based on the quality of care patients receive (e.g., clinical outcomes, meeting specific performance standards, etc.), and providers and patients can benefit from being committed to addressing and improving the overall health of patients. For example, CMS can set a budget for patient care for a diagnosed disease (e.g., ESRD), thereby incentivizing healthcare providers to innovate to reduce the cost of providing treatment for the disease. In some embodiments, payments can be associated or negotiated through a "shared risk" contract, in which the costs associated with the coordinated care of the disease and the patient and the savings are borne jointly by the provider and the payer. This arrangement exists in the ESCO model described in more detail above.

[0092] In some embodiments, the health coordination system can identify, test, and / or evaluate innovations through the CEC / ESCO architecture to improve patient health care for Medicare beneficiaries diagnosed with ESRD. The health coordination system can provide a connected structure for dialysis clinics, nephrologists or other specialists, and / or other providers to coordinate health care for respective beneficiaries. Value-based health care models can incentivize providers based on health care quality of services provided. For example, the health coordination system can include incentives for improved health care coordination, individualized patient health care, and / or improved long-term health outcomes for patient populations. The health coordination system can also coordinate Medicare A portion (e.g., hospital insurance) and B portion (e.g., medical insurance) expenditures, including outcomes measured in relation to expenditures for dialysis services for respective ESRD beneficiaries, e.g., clinical quality, financial, etc. It should be appreciated that some value-based health care models can also include Medicare D portion (e.g., prescription drug insurance) expenditures.

[0093] The integrated health system 220 can form part of a clinical system for diagnosing and treating patients in all aspects of health care. The integrated health system 220 can be connectable to additional clinical systems, including but not limited to pharmacies, CKD / ESRD data registries, etc. For example, the integrated health system can automatically send prescriptions and other patient information to a pharmacy based on information provided by medical professionals, and can send and receive data and information to and from a CKD / ESRD data registry for comparison with other patients and to predict future treatments. The integrated health system can determine events associated with CKD / ESRD and take appropriate measures, including but not limited to notifying the patient, notifying the clinician when a particular intervention is needed, and / or reminding the clinician of upcoming important intervention dates.

[0094] One or more outside or external systems can also be connected to the integrated health system 220. For example, the external systems can include one or more of diagnostic and / or treatment equipment such as dialysis machines, laboratories, doctor's offices, hospitals, and / or electronic medical records. Patient information can be sent and received between the integrated health system and the external systems so that patient health care can be more efficient, standardized, and consistent across multiple functions. For example, the integrated health system 220 (see Figure 2A ) can receive information from a patient's electronic medical record, thereby accessing historical information. The dialysis unit or dialysis machine, doctor's office, laboratory, and hospital can send information to and receive information from the integrated health system based on patient treatment, diagnosis, or information outside of treatment or diagnosis.

[0095] As discussed below with reference to Figures 12-14In some embodiments, the health coordination system can provide information to the dialysis machines 1200, 1300, 1400 for use in dialysis treatment. In some embodiments, the integrated health system can send a prescription for a prescribed dialysis treatment from a medical professional to the dialysis machines 1200, 1300, 1400, in which case the integrated health system can receive the prescription from a doctor's office or hospital. The integrated health system can also verify the prescribed treatment against the patient's lab work or medical history, and in some cases, can program the prescription remotely onto the patient's dialysis machine, or forward the prescription to the machine for local setup. In this way, it can be ensured that the patient receives the necessary and correct treatment, and that the patient can be prevented from administering or receiving an improper amount of dialysis treatment, thereby reducing human error and improving patient health. The integrated health system 220 can also be able to notify relevant medical professionals based on information received from these external systems, as well as additional clinical systems, for example, to provide appropriate medical treatment to the patient and / or to indicate to the medical professional when the patient can need retraining and / or additional treatment supervision.

[0096] Figure 2B is another illustration of a health coordination architecture. Figure 2B The coordinated health architecture 200' of Figure 2A shares features described herein unless otherwise noted. The coordinated health architecture 200' described in this example is provided for integrating patient health into treatment of kidney disease, for example, showing ESRD and / or CKD (but it can also apply to other chronic conditions similar to Figure 2A The health coordination system 210' can coordinate at least certain aspects of patient health with the integrated health system 220' (which can include and execute health care related models and / or programs 260) to support patient health. Various components can participate within the health coordination system 210' to provide complete patient health via the health architecture. For example, any number of integrated health components can send information to or receive information from the integrated health system 220', including but not limited to ancillary services components 265, data creation and / or management components 270, health provider components 275, equipment and / or supply components 280, and regulatory components 285. In some embodiments, the health coordination system 210' can cooperate with third party resources including but not limited to laboratory services, research, etc. In some embodiments, the health architecture can include or be implemented by or associated with a health navigation unit 230'. In the example of Figure 2B it is noted that the health navigation unit 230' is indicated as a separate entity from the health coordination system 210', but it should be understood that in other examples (see, e.g.,Figure 2A ), a healthcare navigation unit may also be included as part of the healthcare coordination system.

[0097] As described above, each component of the integrated healthcare system (eg, healthcare analysis and guidance system) 220, 220' may include one or more units, including internal services and support and external services and support. Figure 6 As shown, the auxiliary services component 265 may include any number "n" of services 605a, 605b, ..., 605n related to auxiliary patient services. For example, the auxiliary services may include a laboratory 605a, personalized healthcare 605b, and / or a pharmacy 605c. Each of the auxiliary services 605a, 605b, ..., 605n may send and receive patient information to the integrated healthcare system 220, 220' for compilation and analysis. For example, the laboratory may automatically send patient blood test results and other test results to the integrated healthcare system 220, 220'. Furthermore, the integrated healthcare system 220, 220' may automatically send test instructions to the laboratory to perform selected tests on patient samples based on determinations from medical professionals and / or other information collected by the healthcare coordination system 210' via the healthcare architecture. Similarly, the integrated healthcare system 220, 220' may automatically send prescriptions and dosage instructions to the pharmacy based on the patient's test results and other factors determined by the integrated healthcare system 220, 220'. The pharmacy may also send information related to other patient prescriptions to the integrated healthcare system 220, 220' for potential adverse drug interactions, how to refill prescriptions in a timely manner, and / or patient-pharmacist interactions, etc.

[0098] In some embodiments, patients can benefit from the care of a nutritionist and / or dietitian 605d to adjust dietary restrictions as part of their healthcare. For example, ESRD patients may already be prescribed dietary requirements as part of receiving hemodialysis and other treatments for their kidney disease. Patients can benefit from consultations with a nutritionist and / or dietitian to develop a healthier dietary lifestyle and other potential health-related benefits. Fluid management 605e can also be managed for the patient to ensure they are receiving the appropriate amount and type of fluids. Patients with CKD and / or ESRD may have fluid restrictions to achieve better dialysis results. Some patients may have difficulty understanding fluid intake and / or may not be able to reliably track their fluid intake. In some embodiments, fluid management can be managed by a nutritionist and / or dietitian, but it should be understood that in other embodiments, the patient's fluid intake can be managed by another medical professional. In some embodiments, patients can benefit from the care of a mental health professional 605f, such as a psychologist, psychiatrist, and / or other counseling services. As mentioned above, a patient's mental health can be affected by disease progression and may otherwise be overlooked by other medical professionals during treatment. Therefore, connecting patients with mental health professionals and providing them with services can improve their overall health.

[0099] Now refer to Figure 7 As described above, the data creation / management component 270 may include one or more units related to the creation and / or management of patient data, including internal services and support as well as external services and support. For example, the data creation / management component 270 may include any number "n" of services 705a, 705b, ..., 705n. Figure 7 As shown, an electronic medical record (EMR) 705a, a data registry 705b, and clinical information 705c can receive, store, and / or transmit patient data records determined by the healthcare analysis and guidance systems 220 and 220'. For example, a patient's medical record can be automatically updated upon receipt of laboratory results, treatment information, and / or comments from a medical professional. The healthcare analysis and guidance systems 220 and 220' can utilize the patient's medical record to identify trends or trigger events so that the healthcare coordination system 210' can provide relevant information to the medical professional to recommend treatment and other healthcare options, as well as to schedule and coordinate various possible interventions. In some embodiments, the healthcare analysis and guidance systems 220 and 220' can analyze multiple patients as part of the data registry to identify global trends and analyze data at a macro level.

[0100] Figure 8An example health provider component 275 is shown, including one or more units that provide patient care, as indicated by reference numerals 805a, 805b,..., 805n. Any number "n" of units can be included in the provider component 275. In some embodiments, the health providers can include physicians and / or physician groups 805a (e.g., primary care physicians (PCPs) and specialists, such as nephrologists), practice management systems 805b, hospitals 805c, and / or clinics / centers 805d, although additional or alternative health providers are also contemplated. The integrated health system 220, 220' can send information to and receive information from the health providers for patient treatment. For example, the integrated health system 220, 220' can receive physician records of patient exams, hospitalization information, and the like, and can send computed information and other determined factors based on the received other patient data. For example, the integrated health system 220, 220' can send estimates and treatment recommendations based on all received patient data and assessments thereof to identify, reduce, avoid, and / or eliminate aspects of kidney disease or patient risks of multiple impacts and / or multiple aspects of kidney disease treatment for providing treatment to a patient (e.g., to identify, treat, and minimize risks of infections such as peritonitis).

[0101] Figure 9 An example equipment and / or supply component 280 for individual patients is shown, e.g., treatment supplies, which can include any number "n" of services 905a, 905b,..., 905n. In some embodiments, the integrated health system 220, 220' can send and receive information related to disposable medical equipment 905a, information technology (IT) technical support 905b, inventory control 905c, and / or dialysis units 905d or kits of dialysis machines in a clinic. As described above, many patients receive treatment at home, such as home dialysis, which requires a continuous supply of disposable medical supplies for each treatment. The delivery of supplies and / or dialysis equipment can be monitored, replenished, and / or inventoried automatically by the integrated health system 220, 220' to ensure proper machine function and a steady supply of materials and resources, thereby ensuring that patients receive all prescribed treatments.

[0102] Figure 10An example regulatory component 285 is shown that can include any number "n" of services 1005a, 1005b,..., 1005n related to government and regulatory requirements. For example, certain state and federal regulations and regulatory agencies can be involved in insurance and / or Medicaid and Medicare services (CMS) hub 1005a, public product approval (e.g., Food and Drug Administration (FDA)) 1005b, and billing 1005c. The integrated healthcare system 220, 220' can send information to and receive information from each of these units to ensure proper billing coding, regulatory approval, and / or insurance payment.

[0103] As introduced above, the healthcare navigation unit 230, 230' can supervise and coordinate patient healthcare based on analysis and calculations determined by the integrated healthcare system 220, 220' from data and information from any of the components 265, 270, 275, 280, 285, and the healthcare coordination system 210'. For example, the healthcare navigation unit 230' can coordinate healthcare for a patient to intervene with treatment, address functional and / or cognitive patient impairments over time, improve comorbidity management, and help drive high value healthcare choices and timing of treatment decisions for a patient over time. As Figure 11 As shown, the healthcare navigation unit 230' can include different aspects of healthcare coordination indicated by reference numerals 1105a, 1105b,..., 1105n, including but not limited to counseling 1105a, treatment transition 1105b, scheduling 1105c, patient monitoring 1105d, tracking 1105e, transportation 1105f, and / or discharge healthcare 1105g. For example, the integrated healthcare system 220, 220' can determine that a patient needs transportation to and from a treatment center and can automatically schedule transportation, e.g., public transportation, ride share, taxi, ride share bike, etc., so that the patient does not miss scheduled treatments. In addition, the integrated healthcare system 220, 220' can send patient results to relevant healthcare providers, e.g., medical specialists, doctors, and / or nurses, for monitoring and / or to give treatment recommendations. The healthcare navigation unit 230' can provide services to patients to address their complete healthcare needs related to their kidney disease.

[0104] The health navigation unit 230, 230' can include a treatment transition 1105b for the integrated health system 220, 220' to coordinate patient health through progression of kidney disease. For example, a patient can initially be diagnosed with chronic kidney disease (CKD). However, over time, the patient can progress to end-stage renal disease (ESRD) if no intervening treatment (e.g., kidney transplant) or improvement in kidney function occurs. As the patient's kidney disease progresses, the patient can require additional services, support, and / or medical care, which can be overseen and / or managed by the health navigation unit 230' via the integrated health system 220, 220' and through the health architecture of the health coordination system 210, 210' under the health architecture 200'.

[0105] Referring now to Figure 3 An integrated health system, such as the integrated health system 220, 220', can include a controller 305, a processor 310, and a memory 320. The controller 305 can automatically control signals received and sent to other systems, for example, additional clinical systems, external systems, and practice management and billing systems. Communication between the controller 305 and other systems can be bidirectional, whereby the system can acknowledge control signals, and / or can provide information associated with the system and / or requested operation. In addition, a user input interface 315 and a display 302 can be provided to receive and / or display input from a user, for example, a patient or a medical professional such as a doctor, nurse, technician, or the like. Examples of components that can be employed within the user input interface 315 include a keypad, buttons, a microphone, a touch screen, gesture recognition devices, a display screen, and a speaker. In some embodiments, the integrated health system 220, 220' can be a server, computer, or other device for storing and processing data and controlling signals to other systems. A power source 325 can allow the integrated health system 220, 220' to receive power, and in some embodiments can be a separate power source.

[0106] The processor 310 can be configured to execute an operating system, which can provide platform services to application software, for example, for operating the integrated healthcare system 220, 220'. These platform services can include interprocess and network communications, file system management, and standard database operations. One or more of a number of operating systems can be used, and examples are not limited to any particular operating system or operating system characteristics. In some examples, the processor 310 can be configured to execute a real-time operating system (RTOS), such as RTLinux, or a non-real-time operating system, such as BSD or GNU / Linux. According to various examples, the processor 310 can be a commercially available processor, such as those manufactured by INTEL, AMD, MOTOROLA, and FREESCALE. However, the processor 310 can be any type of processors, multi-processors, or controllers, whether commercially available or specially manufactured. For example, according to one example, the processor 310 can comprise an MPC823 microprocessor manufactured by MOTOROLA.

[0107] The memory 320 can include a computer-readable and writeable nonvolatile data storage medium configured to store non-transitory instructions and data. Additionally, the memory 320 can include a processor memory that stores data during operation of the processor 310. In some examples, the processor memory includes relatively high performance volatile random access memory, such as dynamic random access memory (DRAM), static memory (SRAM), or synchronous DRAM. However, the processor memory can include any device for storing data used by or generated by the processor 310, including non-volatile storage such as flash memory, as long as the sufficient throughput and storage capacity is available. Moreover, examples are not limited to a particular memory system or data storage system.

[0108] The instructions stored on the memory 320 can include executable programs or other code that can be executed by the processor 310. The instructions can be persistently stored as encoded signals, and the instructions can make the processor 310 perform the functions described herein. The memory 320 can include information recorded thereon or therein as computer-readable media, and the information can be processed by the processor 310 during execution of the instructions. The memory 320 can further include, for example, data records, timing for therapy and / or operation, historical information, statistical information, and databases of information for therapy. The databases can be stored in the memory 320 of the integrated healthcare system 220, 220' and can be accessed by the processor 310 and the controller 305. For example, historical data of patient information can be extracted from various databases in the integrated healthcare system 220, 220', including but not limited to patient laboratory results, therapy data, technician data (nurse annotations) during therapy, etc.

[0109] Patient population data in the integrated healthcare system 220, 220' can be analyzed to train a machine learning model to identify patients at risk of developing an infection. The integrated healthcare system 220, 220' can evaluate a large amount of patient data, for example, an entire patient population in a home setting, for example, receiving dialysis treatment through peritoneal dialysis. Extracted historical data can be used to train a machine learning model, for example, to evaluate factors that lead to reported instances of peritonitis. For example, the machine learning model can identify characteristics of patients previously diagnosed with an infection, such as peritonitis, within a selected time period and analyze the patient's laboratory results, treatment data, nurse annotations, and the like to find commonalities that can contribute to their prediction. The machine learning model can also receive historical data extracted for patients who did not develop an infection within the selected time period. In some embodiments, the machine learning model can exclude some patient data that includes outliers. In this way, the machine learning model can be trained to identify data associated with or indicative of an infection diagnosis and data associated with or indicative of no infection diagnosis. In some examples, an algorithm can be utilized to learn from historical data (raw data or pre-processed data) and make predictions based on the historical data. For example, a gradient boosting architecture and / or extreme gradient boosting tree algorithm can be utilized. Some example implementations utilize XGBoost. The medium can be, for example, an optical disc, a magnetic disc, or a flash memory, among others, and can be permanently attached to the controller 305 or removable from the controller 305.

[0110] The integrated healthcare system 220, 220' can include a communication link 306 so that other systems can connect to the integrated healthcare system 220, 220'. For example, additional clinical systems, external systems, and practice management and billing systems can connect to the integrated healthcare system 220, 220' to send and receive data and information associated with providing patient healthcare. In some embodiments, the communication link 306 can be wireless so that the systems can be remote or the integrated healthcare system 220, 220' and / or one or more of the systems 265, 270, 275, 280, 285, 230' can reside in and operate in a cloud-based architecture.

[0111] One or more algorithms can utilize a model of historical data to analyze patient data as it is input and / or collected by the integrated healthcare system 220, 220'. The algorithms can analyze the patient data based on the historical data to identify patients at risk of future infections. The integrated healthcare system 220, 220' can also generate reports identifying high-risk patients for follow-up treatment and / or retraining. For example, once a patient is identified as at risk of an infection in the future, a medical professional can consult with the patient to understand the patient's in-home dialysis treatment and procedures, and / or supervise the performance of the dialysis to minimize the likelihood of an infection occurring or persisting.

[0112] The integrated healthcare system 220, 220' can also be wirelessly connected via the antenna 345 for remote communication. For example, the integrated healthcare system 220, 220' can determine one or more patient parameters by the controller 305, processor 310, and / or memory 320, and can access other patient parameters stored by external systems, for example, on a server or database stored at a location remote from the system or machine or from an electronic medical record of a patient from a laboratory or hospital information. It can be advantageous for the integrated healthcare system 220, 220' to access other patient parameters that can otherwise be unknown or undeterminable to provide a complete healthcare analysis of the patient. As described above, patient data can be transmitted to and / or accessed by the integrated healthcare system 220, 220'. The controller 305, processor 310, and memory 320 can receive, store, and / or determine relevant demographic characteristics and laboratory values or other data for computation.

[0113] The integrated healthcare system 220, 220' can then use the computation to determine the risk of the patient developing an infection, such as peritonitis. In some embodiments, as patient parameter information is updated, for example, data points are included in the system, the corresponding future or predicted patient parameters can be updated and adjusted accordingly. In embodiments, any number of variables can be extracted to determine the risk of the patient developing an infection (see Figure 1C ). Further, annotations can be included in determining the risk of the patient, for example, annotations from medical professionals. The one or more algorithms can generate a risk score of the patient developing an infection based on the extracted variables and historical data, and in some embodiments, can identify a dominant factor related to the generated risk score. By determining the risk and cause of the patient developing an infection, a medical professional is able to develop individualized patient interventions to minimize and / or eliminate the risk of developing an infection, such as peritonitis, allowing the patient to remain in the same dialysis modality (e.g., home peritoneal dialysis).

[0114] Referring now to Figures 4-5 , one exemplary embodiment of an operating environment of a healthcare system (e.g., coordinated healthcare architecture 200, 200') including an integrated healthcare system (healthcare analysis and guidance system) 220, 220' is described. Figure 4 One example of an operating environment 400 that can be representative of some embodiments is shown. As Figure 4As shown, operating environment 400 may include system 405 for treating a patient, such as a patient with a chronic condition. In various embodiments, system 405 may include computing device 410. Computing device 410 may include processing circuitry 420, memory unit 430, transceiver 450, and / or display 452. Processing circuitry 420 may be communicatively coupled to memory unit 430, transceiver 450, and / or display 452. It should be understood that in some embodiments, system 405 may include coordinated care architecture 200, 200', and in some embodiments, system 405 may include other systems and / or architectures.

[0115] In some embodiments, computing device 410 may be connected to network 460 via transceiver 450. Network 460 may include nodes 462a-n, eg, remote computing devices, data sources 464, and / or the like.

[0116] According to some embodiments, processing circuitry 420 may include and / or have access to various logic for executing a process. Processing circuitry 420 or portions thereof may be implemented in hardware, software, or a combination thereof. As used herein, the terms "logic," "component," "layer," "system," "circuit," "decoder," "encoder," and / or "module" are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are represented by Figure 15 15. The exemplary computing architecture 1500 of FIGURE 15 is provided. For example, logic, circuitry, or layers may be and / or may include, but are not limited to, a process running on a processor, a processor, a hard drive, multiple storage drives (of optical and / or magnetic storage media), an object, an executable file, a thread of execution, a program, a computer, a hardware circuit, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a system on a chip (SoC), a memory cell, a logic gate, a register, a semiconductor device, a chip, a microchip, a chipset, a software component, a program, an application, firmware, a software module, computer code, combinations of any of the foregoing, and / or the like.

[0117] It should also be understood that components of processing circuitry 420 may reside within an accelerator, a processor core, an interface, a separate processor die, be fully implemented as a software application, and / or the like.

[0118] The memory unit 430 can include various types of computer-readable storage media in the form of one or more higher speed memory units including read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), double-data-rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, array-based drives such as Redundant Array of Independent Disks (RAID) drives, solid state memory devices (e.g., USB memory, solid state drives (SSD) and any other type of storage media suitable for storing information. In addition, the memory unit 430 can include various types of computer-readable storage media in the form of one or more lower speed memory units including internal (or external) hard disk drives (HDD), magnetic floppy disk drives (FDD) and optical disk drives (ODD) which read from or write to a removable optical disk, such as a CD-ROM or DVD. The memory unit 430 can also include solid-state drives (SSD) and / or similar storage media.

[0119] The memory unit 430 can store various information, for example, one or more programs to perform various functions to identify and treat patients with CKD and / or ESRD. In some embodiments, the memory 430 can include logic with an application programming interface (API) and / or a graphical user interface (GUI) to read, write, and / or otherwise access information, for example, via the display 452, a web interface, a mobile application (“mobile application,” “mobile app,” or “app”), and / or the like. In this way, in some embodiments, an operator can search, visualize, read, add, or otherwise access information associated with a patient population for identifying and treating CKD and / or ESRD.

[0120] In some embodiments, the memory unit 430 can store various information associated with a patient population for use in identifying and treating CKD and / or ESRD. In some embodiments, the information stored in the memory unit 430 can be retrieved from and / or moved to a data source 464, including but not limited to a hospital information management system (HIMS), a laboratory information management system (LIMS), a health information system (HIS), an electronic medical record (EMR), a clinical trial database, and / or the like.

[0121] Figure 5 An example of an operating environment 500 that may represent some embodiments is shown. Figure 5 As shown, operating environment 500 may include platform 505, such as a healthcare exchange platform. In some embodiments, platform 505 may be used to provide exchange of clinical data and / or clinical trial information between interested entities. In various embodiments, platform 505 may include an application platform for identifying patient populations and utilizing services between nodes 560a-n and 570a-n to treat CKD and / or ESRD. In an exemplary embodiment, platform 505 may be a software platform, suite, protocol set, and / or the like provided to a customer by a manufacturer and / or developer ("developer") associated with medical devices, healthcare services, clinical research services, laboratory services, clinical trial services, and / or the like.

[0122] For example, a developer may provide the platform 505 as a data exchange interface for use by various entities, including government entities (e.g., the FDA) and other stakeholders (e.g., drug manufacturers, medical device manufacturers, and / or the like). Entities such as hospitals, dialysis clinics, healthcare providers, government entities, regulatory entities, drug manufacturers, medical device manufacturers, and / or the like, which provide and / or receive clinical trial services via developer-provided nodes 570a-n, may use the platform 505 to implement processes according to some embodiments. Other entities may access the platform 505 via a GUI such as a client application, a web interface, a mobile application, and / or the like, for example, to perform functions associated with the storage. In some embodiments, at least a portion of the platform 505 may be hosted in a cloud computing environment.

[0123] Nodes 570a-n may be data producers for the memory, while nodes 560a-n may be data consumers for the memory. For example, nodes 570a-n may include entities that provide clinical data, model information, and / or the like used by the memory to generate, execute, and / or evaluate patient populations. Nodes 560a-n may include third-party applications, decision makers, analytical processes, regulators, and / or other data consumers that may be interested in generating, executing, and / or evaluating patient population outcomes. An entity may be both a data producer and a data consumer.

[0124] For example, node 560a can be a healthcare provider (node ​​560b) that provides treatment to a patient based on an analysis of a patient population including medical records, laboratory data, pharmacies, etc. (node ​​570a). Data producers 570a-n can provide analytical data to platform 505 in the form of records in, for example, a HIMS, LIMS, EMR, etc., in accordance with a license. Data consumers 560a-n can access analytical data via platform 505 (e.g., through a HIMS, LIMS, EMR, and / or the like and / or a local copy of such records) in accordance with a license.

[0125] In some embodiments, the platform 505 can operate according to a cloud-based model and / or an "as a service" model. In this manner, the platform 505 can provide a service that operates as a single central platform that allows entities to access clinical data, model information, simulation results, and / or the like.

[0126] In some embodiments, one of the recommended treatments and / or services may be a change or modification of the dialysis treatment prescription for the patient. Figures 12-14 As shown and described below, dialysis machines 1200, 1300, 1400, such as, for example, peritoneal dialysis machines or hemodialysis machines, can be connected to integrated healthcare systems 220, 220' for sending and receiving dialysis information to provide appropriate healthcare to the patient. The hemodialysis machine can be located in a renal clinic, such as a renal care clinic, a dialysis clinic, or other third-party healthcare provider. In some embodiments, the peritoneal dialysis machine and / or the hemodialysis machine can be a home-use machine, such as one that allows treatment to be administered in the patient's home. As described above, the integrated healthcare system can be applicable to other chronic diseases and can be connected to machines related to those diseases, including, but not limited to, chronic kidney disease or one or more of the other chronic diseases and conditions described above.

[0127] As reference Figures 12-14The different dialysis modalities described can have some advantages over others. For example, hemodialysis performed in a clinical setting can be easier to keep clean and sterile for the area in which dialysis is performed so that there is a lower chance of the patient getting an infection from contamination. Dialysis machines are known for treating kidney disease. Two main dialysis methods are hemodialysis (HD) and peritoneal dialysis (PD). During hemodialysis, a patient's blood is passed through a dialyzer of a hemodialysis machine while dialysis fluid will also pass through the dialyzer. A semi-permeable membrane in the dialyzer separates the blood from the dialysis fluid within the dialyzer and allows diffusion and osmosis exchanges to occur between the dialysis fluid and the blood stream. During peritoneal dialysis, dialysis fluid or dialysis solution is periodically infused into a patient's peritoneal cavity. The membranous lining of the patient's peritoneum acts as a natural semi-permeable membrane allowing diffusion and osmosis exchanges to occur between the solution and the blood stream. Automated peritoneal dialysis machines, also known as PD cyclers, are designed to control the entire peritoneal dialysis process so that it can be performed at home, typically overnight without the involvement of clinical personnel.

[0128] Reference is made to Figure 12 , showing a schematic diagram of one exemplary embodiment of a dialysis machine 1200 and a controller 1205 according to the present disclosure. The machine 1200 can be a dialysis machine for performing a dialysis treatment on a patient, for example, a peritoneal dialysis machine or a hemodialysis machine (see Figures 12-14 ). The controller 1205 can automatically control the performance of treatment functions during the course of a dialysis treatment. For example, the controller 1205 can control the dialysis treatment based on information received from the healthcare analytics and guidance system 220, 220'. The controller 1205 can be operatively connected to the sensors 1240 and transmit one or more signals to perform one or more treatment functions, or treatment processes associated with various treatment systems. While Figure 12 The components shown integrated into the dialysis machine 1200, at least one of the controller 1205, the processor 1210, and the memory 1220 can be configured to be external and wired or wirelessly connected to the dialysis machine 1200 as a separate component of the dialysis system. In some embodiments, the controller 1205, the processor 1210, and the memory 1220 can be remote from the dialysis machine and configured to wirelessly communicate.

[0129] In some embodiments, the controller 1205, processor 1210, and memory 1220 of the system or machine 1200, 1300, 1400 can receive signals indicative of one or more patient parameters from the sensors 1240. Communication between the controller 1205 and the treatment system can be bidirectional, whereby the treatment system acknowledges control signals, and / or can provide status information associated with the treatment system and / or requested operations. For example, system status information can include status associated with a particular operation to be performed by the treatment system (e.g., triggering a pump for delivery of dialysis fluid, triggering a pump for delivery of filtered blood, and / or a compressor, etc.) and status associated with the particular operation (e.g., ready to perform, performing, completed, successfully completed, queued for performance, waiting for a control signal, etc.).

[0130] The dialysis system or machine 1200, 1300, 1400 can also include at least one pump 1250 operatively connected to the controller 1205. The controller 1205 can also be operatively connected to one or more speakers 1230 and one or more microphones 1235 disposed in the system or machine 1200, 1300, 1400. The user input interface 1215 can include a combination of hardware and software components that allow the controller 1205 to communicate with external entities such as a patient or other user. These components can be configured to receive information from actions such as body movements or gestures and vocal inflections. In embodiments, the components of the user input interface 1215 can provide information to external entities. Examples of components that can be employed within the user input interface 1215 include keypads, buttons, microphones, touch screens, gesture recognition devices, display screens, and speakers.

[0131] As Figure 12As shown, the dialysis machine 1200 can include a sensor 1240 for detecting and monitoring one or more parameters, and the sensor 1240 can be operatively connected to at least the controller 1205, the processor 1210, and the memory 1220. The processor 1210 can be configured to execute an operating system that can provide platform services to, for example, application software for operating the dialysis machine 1200. These platform services can include interprocess and network communications, file system management, and standard database operations. One or more of a number of operating systems can be used, and the examples are not limited to any particular operating system or operating system characteristics. In some examples, the processor 1210 can be configured to execute a real-time operating system (RTOS), such as RTLinux, or a non-real-time operating system, such as BSD or GNU / Linux. According to various examples, the processor 1210 can be a commercially available processor, such as those manufactured by INTEL, AMD, MOTOROLA, and FREESCALE. However, the processor 1210 can also be any type of processor, multi-processor, or controller, whether commercially available or specially manufactured. For example, according to one example, the processor 1210 can comprise an MPC823 microprocessor manufactured by MOTOROLA.

[0132] The memory 1220 can include a computer-readable and writeable nonvolatile data storage medium configured to store non-transitory instructions and data. Additionally, the memory 1220 can include a processor memory that stores data used by the processor 1210 during operation. In some examples, the processor memory includes a relatively high performance volatile memory, such as a dynamic random access memory (DRAM), static memory (SRAM), or synchronous DRAM. However, the processor memory can also include any device or devices that store data used by the processor 1210 during operation, such as a non-volatile memory that is fast enough to be considered a processor memory. Moreover, the examples are not limited to a particular memory, memory system, or data storage system.

[0133] The instructions stored on the memory 1220 can include executable programs or other code that can be executed by the processor 1210. The instructions can be persistently stored as encoded signals, and the instructions can make the processor 1210 perform the functions described herein. The memory 1220 can include information recorded thereon or therein as instructions, data, or a combination thereof. The information can be processed by the processor 1210 during execution of the instructions. The memory 1220 can also include, for example, specifications for user timing requirements, timing for treatment and / or operation, historical sensor information, and other databases, etc. The medium can be, for example, an optical disk, a magnetic disk, or flash memory, and can be permanently, removably, or removably attached to the controller 1205.

[0134] The pressure sensor can be included for monitoring fluid pressure of the system or machine 1200, 1300, 1400, but the sensor 1240 can also include any of a heart rate sensor, a respiration sensor, a temperature sensor, a weight sensor, a video sensor, a thermal imaging sensor, an electroencephalogram sensor, a motion sensor, an audio sensor, an accelerometer, or a capacitive sensor. It will be appreciated that the sensor 1240 can include sensors with varying sampling rates, including wireless sensors. Based on data monitored by the sensor 1240, patient parameters such as heart rate and respiration rate can be determined by the controller 1205.

[0135] The controller 1205 can be disposed in the machine 1200, 1300, 1400 or can be coupled to the machine 1200, 1300, 1400 via a communication port or wireless communication link, illustratively shown as a communication element 1206. For example, the communication element 1206 can connect the dialysis machine 1200, 1300, 1400 to the healthcare analytics and guidance system 220, 220' or another remote system such as an external system or other clinical system. The dialysis machine 1200, 1300, 1400 can be connected to the integrated healthcare system 220, 220' via the communication element 1206 so that the controller 1205 can send and receive information and other signals to the healthcare analytics and guidance system 220, 220'. As discussed above, the healthcare analytics and guidance system 220, 220' can directly communicate prescribed dialysis treatments to the dialysis machine based on information received from other systems, such as external systems, clinical systems, to ensure that the patient receives the correct treatment. The dialysis machine can also send data and other information to the healthcare analytics and guidance system 220, 220' so that the healthcare analytics and guidance system 220, 220' can ensure that any changes do not adversely affect the health of the patient if the dialysis treatment needs to be adjusted.

[0136] As a component disposed within the machine 1200, 1300, 1400, the controller 1205 can be operatively connected to any one or more of the sensor 1240, the pump 1250, the pump heads 1404, 1406, etc. The controller 1205 can communicate control signals or trigger voltages to components of the system or machine 1200, 1300, 1400. As discussed, an exemplary embodiment of the controller 1205 can include a wireless communication interface. The controller 1205 can detect remote devices to determine if any remote sensors are available to augment any sensor data being used to assess the patient.

[0137] As Figure 12As shown, a power source 1225 can be included, for example, to allow the machine to receive power, and in some embodiments can be a separate power source. A display 1202 can also be included. The display 1202 can serve to provide information to a patient and an operator of the dialysis machine. For example, the display 1202 can show information related to a dialysis treatment to be applied to a patient, including information related to a prescription. The dialysis machine can also be wirelessly connected via an antenna 1245 for remote communication.

[0138] Figures 13A-13B One example of a peritoneal dialysis (PD) system 1301 is shown, which is configured in accordance with one example embodiment of the systems described herein. In some embodiments, the PD system 1301 can be a home PD system, e.g., a PD system configured for use in a patient’s home. The dialysis system 1301 can include a dialysis machine 1300 (e.g., a peritoneal dialysis machine 1300, also referred to as a PD cycler), and in some embodiments, the machine can sit on a cart 1334.

[0139] The dialysis machine 1300 can include a housing 1306, a door 1308, and a cassette interface including pump heads 1342, 1344 for contacting a disposable cassette or cartridge 1315, where the cartridge 1315 is located within a compartment (e.g., a cavity 1305) formed between the cassette interface and the closed door 1308. Fluid lines 1325 can be coupled to the cartridge 1315 in a known manner, e.g., via connectors, and can also include valves for controlling the flow of fluid into and out of fluid bags including fresh dialysate and warming fluid. In another embodiment, at least a portion of the fluid lines 1325 can be integral with the cartridge 1315. Prior to operation, a user can open the door 1308 to insert a new cartridge 1315 and remove a used cartridge 1315 after operation.

[0140] The cartridge 1315 can be placed in the cavity 1305 of the machine 1300 for operation. During operation, dialysis fluid can be flowed into a patient’s abdomen via the cartridge 1315, and used dialysate, waste, and / or excess fluid can be removed from the patient’s abdomen via the cartridge 1315. The door 1308 can be securely closed onto the machine 1300. Peritoneal dialysis for a patient can include a total treatment volume of approximately 10 to 30 liters of fluid, where approximately 2 liters of dialysate fluid is pumped into a patient’s abdomen and held for a period of time, e.g., about an hour, and then pumped out of the patient’s body. This process is repeated until the total treatment volume is reached, and is typically performed during an overnight period while the patient is asleep.

[0141] A heater tray 1316 can be positioned on top of the housing 1306. The heater tray 1316 can have any size and shape to accommodate a bag of dialysate (e.g., a 5 L dialysate bag) for batch heating. The dialysis machine 1300 can also include a user interface, such as a touchscreen 1318 and control panel 1320, operable by a user (e.g., a caregiver or patient) to allow, for example, setting up, initiating, and / or terminating a dialysis treatment. In some embodiments, the heater tray 1316 can include a heating element 1335 for heating the dialysate prior to delivery to the patient.

[0142] The dialysate bags 1322 can be hung from hooks on the side of the cart 1334, and the heater bag 1324 can be positioned in the heater tray 1316. Hanging the dialysate bags 1322 can improve air management, as the contained air can be deployed to the top portion of the dialysate bags 1322 by gravity. While four dialysate bags 1322 are shown in Figure 13B While four dialysate bags 1322 are shown in FIG. 13, any number "n" of dialysate bags can be connected to the dialysis machine 1300 (e.g., 1 to 5 bags, or more), and the reference to first and second bags does not limit the total number of bags used in the dialysis system 1301. For example, the dialysis machine can have dialysate bags 1322a,..., 1322n connectable in the system 1301. In some embodiments, connectors and tube ports can connect the dialysate bags 1322 and tubing for transferring dialysate. Dialysate from the dialysate bags 1322 can be transferred in batches to the heater bag 1324. For example, a batch of dialysate can be transferred from the dialysate bags 1322 to the heater bag 1324, where the dialysate is heated by the heating element 1335. When the batch of dialysate has reached a predetermined temperature (e.g., approximately 98°-100°F, 37°C), the batch of dialysate can be flowed into the patient. The dialysate bags 1322 and the heater bag 1324 can be connected to the cassette 1315 via dialysate bag tubing or tubing 1325 and heater bag tubing or tubing 1328, respectively. The dialysate bag tubing 1325 can be used to transfer dialysate from the dialysate bags 1322 to the cassette during use, and the heater bag tubing 1328 can be used to transfer dialysate back and forth between the cassette and the heater bag 1324 during use. In addition, a patient tubing 1336 and an effluent tubing 1332 can be connected to the cassette 1315. The patient tubing 1336 can be connected to the patient's abdomen via a catheter, and can be used to transfer dialysate back and forth between the cassette and the patient's peritoneal cavity via the pump heads 1342, 1344 during use. The effluent tubing 1332 can be connected to an effluent system or effluent container, and can be used to transfer dialysate from the cassette to the effluent system or effluent container during use.

[0143] While in some embodiments, the dialysate can be batch heated as described above, in other embodiments, the dialysis machine can also heat the dialysate through online inline heating, e.g., continuously flowing the dialysate through a warmer bag positioned between heating elements prior to delivery to the patient. For example, instead of placing a heater bag for batch heating on a heater tray, one or more heating elements can be provided inside the dialysis machine. The warmer bag can be inserted into the dialysis machine via an opening. It should also be understood that the warmer bag can be connected to the dialysis machine via tubing (e.g., tubing 1325) or fluid lines, via a cassette. The tubing can be connectable so that dialysate can flow from a dialysate bag, through the warmer bag for heating, and to the patient.

[0144] In such online inline heating embodiments, the warmer bag can be configured so that dialysate can continuously flow through the warmer bag (rather than being batch transferred for batch heating) to reach a predetermined temperature prior to flowing into the patient. For example, in some embodiments, the dialysate can continuously flow through the warmer bag at a rate of between about 100-300 mL / min. Internal heating elements (not shown) can be positioned above and / or below the opening so that when the warmer bag is inserted into the opening, the one or more heating elements can affect the temperature of the dialysate flowing through the warmer bag. In some embodiments, the internal warmer bag can instead be a portion of tubing in the system that is configured to pass through, around, or relative to the heating elements.

[0145] The touchscreen 1318 and control panel 1320 can allow an operator to input various treatment parameters to the dialysis machine 1300 and otherwise control the dialysis machine 1300. In addition, the touchscreen 1318 can serve as a display. The touchscreen 1318 can function to provide information to a patient and an operator of the dialysis system 1301. For example, the touchscreen 1318 can display information related to a dialysis treatment to be applied to a patient, including information related to a prescription.

[0146] The dialysis machine 1300 can include a processing module 1302 resident within the dialysis machine 1300 that is configured to communicate with the touchscreen 1318 and control panel 1320. The processing module 1302 can be configured to receive data from the touchscreen 1318, control panel 1320, and sensors, e.g., weight, air, flow, temperature, and / or pressure sensors, control the dialysis machine 1300 based on the received data. For example, the processing module 1302 can adjust operational parameters of the dialysis machine 1300.

[0147] The dialysis machine 1300 can be configured to connect to a network 1303. The connection to the network 1303 can be via wired and / or wireless connections. The dialysis machine 1300 can include connection means 1304 configured to facilitate the connection to the network 1303. The connection means 1304 can be a transceiver for wireless connections and / or other signal processors for processing signals sent and received through wired connections. Other medical devices (e.g., other dialysis machines) or means can be configured to connect to the network 1303 and communicate with the dialysis machine 1300.

[0148] User interface portions, such as the touch screen 1318 and / or the control panel 1320, can include one or more buttons for selecting and / or inputting user information. The touch screen 1318 and / or the control panel 1320 can be operatively connected to a controller (not shown) and disposed in the machine 1300 for receiving and processing the input to operate the dialysis machine 1300.

[0149] In some embodiments, the machines 1200, 1300, 1400 can wirelessly transmit (e.g., via a wireless internet connection), alternately or simultaneously or in coordination, information to the integrated healthcare system 220, 220', to a remote location, including but not limited to a physician’s office, a hospital, a call center, and technical support, or an alert. For example, the machines 1200, 1300, 1400 can provide real-time remote monitoring of machine operation and patient parameters. The memory 1220 of the machine 1200 can store the data, or the machines 1200, 1300, 1400 can transmit the data to a local or remote server at predetermined intervals. For example, the machines 1200, 1300, 1400 can send patient data to the integrated healthcare system 220, 220’ for calculating a risk score that a patient is at risk of developing an infection (e.g., peritonitis).

[0150] Figure 14A diagram illustrating one exemplary embodiment of a dialysis system 1400 according to the present disclosure is shown. The dialysis system 1400 can be configured to provide hemodialysis treatment to a patient 1401. A fluid reservoir 1402 can deliver fresh dialysate to a dialyzer 1404 via a tube 1403, and once the dialysate has passed through the dialyzer 1404, a reservoir 1406 can receive the used dialysate via a tube 1405. The hemodialysis operation can filter particulates and / or contaminants from the patient's blood through an extracorporeal filtration device, e.g., the dialyzer 1404. As the dialysate passes through the dialyzer 1404, the patient's unfiltered blood also enters the dialyzer via a tube 1407, and the filtered blood is returned to the patient via a tube 1409. Arterial pressure can be monitored via a pressure sensor 1410, inflow pressure via a sensor 1418, venous pressure via a pressure sensor 1414. An air trap and detector 1416 can ensure that air is not introduced into the patient's blood as it is filtered and returned to the patient 1401. The flow of blood and the flow of dialysate are controlled via respective pumps, including a blood pump 1412 and a fluid pump 1420. Heparin 1422, a blood thinner, can be used in conjunction with saline 1424 to ensure that blood clots do not form or block the flow of blood through the system.

[0151] In some embodiments, the dialysis system 1400 can include a controller 1450, which can be similar to the controller 1205 described above in connection with the dialysis machine 1200. The controller 1450 can be configured to monitor fluid pressure readings to identify fluctuations indicative of patient parameters such as heart rate and / or respiratory rate. In some embodiments, the patient heart rate and / or respiratory rate can be determined from fluid pressure in the fluid flow lines and fluid bags. The controller 1450 can also be operatively connected to and / or in communication with additional sensors or sensor systems, although the controller 1450 can also use any available data regarding the patient's biological functioning or other patient parameters.

[0152] Figure 15 One embodiment of an exemplary computing architecture 1500 suitable for implementing various embodiments as previously described is shown. In various embodiments, the computing architecture 1500 can comprise or be part of an electronic device. In some embodiments, the computing architecture 1500 can be representative of, for example, the computing device 410 and / or the platform 505 and / or components of the integrated healthcare system 220, 220'. Embodiments are not limited in this regard.

[0153] As used in this application, the terms "system" and "component" and "module" mean the computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, exemplified by, but not limited to, exemplary computing architecture 1500. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and / or magnetic storage medium), an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and / or thread of execution, and a component can be localized, co-resident, and / or distributed amongst one or more computers. Also, a component can be a software description obtained from a server and downloaded via, for example, the Internet, or from another network. Although an exemplary component can be a process that runs on a processor and / or executable, this component need not take such a form. There can be an exemplary component that has

[0154] Computing architecture 1500 includes various common computing elements, such as one or more processors, multi-core processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, and so forth. As will be appreciated by one skilled in the art, embodiments can be implemented using any combination of

[0155] As shown in Figure 15 FIG. 10, computing architecture 1500 includes a processing unit 1504, a system memory 1506, and a system bus 1508. The processing unit 1504 can be any of various commercially available processors, including without limitation and processors; application, embedded and special-purpose processors; and and processors; IBM and Cell processors; Core (2) and processors; and the like. Dual microprocessors, multi-core processors, and other multi-processor architectures can also be employed as the processing unit 1504.

[0156] The system bus 1508 provides an interface for system components including, but not limited to, a system memory 1506 to the processing unit 1504. The system bus 1508 can be any of several types of bus structures including, but not limited to, a memory bus with memory controller, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters can connect to the system bus 1508 via an appropriate socket architecture. Example socket architectures can include, but are not limited to, an Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, Personal Computer Memory Card International Association (PCMCIA) interface, and the like.

[0157] The system memory 1506 can include various types of computer-readable storage media in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, array-based Figure 15 In the illustrated embodiment, the system memory 1506 can include non-volatile memory 1510 and / or volatile memory 1512. A basic input / output system (BIOS) can be stored in the non-volatile memory 1510.

[0158] The computer 1502 may include various types of computer-readable storage media in the form of one or more lower-speed memory units, including an internal (or external) hard disk drive (HDD) 1514, a magnetic floppy disk drive (FDD) 1516 that reads from or writes to a removable disk 1518, and an optical drive 1520 that reads from or writes to a removable disk 1522 (e.g., a CD-ROM or DVD). The HDD 1514, FDD 1516, and optical drive 1520 may be connected to the system bus 1508 by a HDD interface 1524, an FDD interface 1526, and an optical drive interface 1528, respectively. The HDD interface 1524 for an external drive implementation may include at least one or both of Universal Serial Bus (USB) and IEEE 884 interface technologies.

[0159] The drives and associated computer-readable media provide volatile and / or nonvolatile storage of data, data structures, computer-executable instructions, etc. For example, a number of program modules may be stored in the drives and memory units 1510, 1512, including an operating system 1530, one or more application programs 1532, other program modules 1534, and program data 1536. In one embodiment, the one or more application programs 1532, other program modules 1534, and program data 1536 may include, for example, various applications and / or components of the systems and / or devices 200, 200', 220, 220', 400, 500.

[0160] A user can enter commands and information into the computer 1502 through one or more wired / wireless input devices, such as a keyboard 1538 and a pointing device such as a mouse 1540. Other input devices may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, a game pad, a stylus, a card reader, a dongle, a fingerprint reader, a glove, a graphics tablet, a joystick, a keyboard, a retina reader, a touch screen (e.g., capacitive, resistive, etc.), a trackball, a trackpad, sensors, a stylus, and the like. These and other input devices are typically connected to the processing unit 1504 through an input device interface 1542 coupled to the system bus 1508, but may be connected through other interfaces such as a parallel port, an IEEE 894 serial port, a game port, a USB port, an IR port, and the like.

[0161] A monitor 1544 or other type of display device is also connected to the system bus 1508 via an interface, such as a video adapter 1546. The monitor 1544 may be internal or external to the computer 1502. In addition to the monitor 1544, computers typically include other peripheral output devices, such as speakers and printers.

[0162] The computer 1502 can operate in a networked environment using logical connections via wired and / or wireless communication to one or more remote computers, such as a remote computer 1548. The remote computer 1548 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 1502, although, for purposes of brevity, only a memory / storage device 1550 is illustrated. The logical connections depicted include wired / wireless connectivity to a local area network (LAN) 1552 and / or larger networks, e.g., a wide area network (WAN) 1554. Such LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

[0163] When used in a LAN networking environment, the computer 1502 is connected to the LAN 1552 through a wired and / or wireless communication network interface or adapter 1556. The adapter 1556 can facilitate wired and / or wireless communication to the LAN 1552, which can also include a wireless access point disposed thereon for communicating with the wireless functionality of the adapter 1556.

[0164] When used in a WAN networking environment, the computer 1502 can include a modem 1558, or be connected to a communications server on the WAN 1554, or have other mechanisms for establishing communications over the WAN 1554, such as by way of the Internet. The modem 1558, which can be internal or external and a wired and / or wireless device, is connected to the system bus 1508 via the input device interface 1542. In a networked environment, program modules depicted relative to the computer 1502, or portions thereof, can be stored in the remote memory / storage device 1550. It will be appreciated that the network connections shown are exemplary and other approaches can be used to establish a communications link between the computers.

[0165] The computer 1502 is operable with wired and wireless devices or entities using the IEEE 802 family of standards, including Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™, among others. This includes use of wireless devices compliant with the IEEE 802.16 family of standards, sometimes referred to as WiMax (Worldwide Interoperability for Microwave Access), which is a standards-based broadband wireless technology that provides high- speed wireless data TMWireless technology, etc. Thus, the communication can be a predefined structure as with traditional networks, or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies such as the Wireless Fidelity (or Wi-Fi), which is a wireless networking technology that provides a "wireless" connection between two devices, using any of the IEEE 802.11 or High- speed 802.11 standards, or others like it. A Wi-Fi network can be designed to interwork with Ethernet networks, or it can be a stand-alone network. Wi-Fi networks are mobile and can provide a range of mobility for users. Some examples of Wi-Fi networks include connection to the Internet via a computer with a wireless adapter card, or connection of other devices (e.g., electronic devices, mobile devices, etc.) using any of the IEEE 802.11 standards.

[0166] Some embodiments of the disclosed system can be implemented, for example, using a storage medium, a computer-readable medium, or an article of manufacture that can store instructions or sets of instructions that, if executed by a machine (i.e., a processor or microcontroller), can cause the machine to perform the methods and / or operations according to embodiments of the present disclosure. Furthermore, a server or database server can include a machine-readable medium configured to store machine-executable program instructions. Such a machine can include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, or the like, and can be implemented using hardware, software, firmware, or any suitable combination thereof, and can be used in a system, subsystem, component, or subcomponent. The computer-readable medium or article of manufacture can include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and / or storage unit, for example, memory (including temporary memory), removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R) storage, Compact Disk Rewriteable (CD-RW) storage, optical disk, magnetic media, magneto-optical media, removable memory cards or disks, various types of Digital Versatile Disk (DVD), a tape, a cassette, or the like. The instructions can include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, and the like, implemented using any suitable high-level, low-level, object-oriented, visual, compiled, or interpreted programming language.

[0167] Many specific details have been set forth herein to provide a thorough understanding of the embodiments. It will be appreciated, however, that the embodiments can be practiced without these specific details. In other instances, well-known operations, components, and circuits have not been described in detail in order to avoid obscuring the embodiments. It will be appreciated that the specific structural and functional details disclosed herein are representative and do not necessarily limit the scope of the embodiments.

[0168] Some embodiments can be described using the expression “coupled” and “connected” along with their derivatives. These terms are not intended as synonyms for each other. For example, some embodiments can be described using the terms “connected” and / or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, can also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

[0169] Unless specifically stated otherwise, it can be appreciated that terms such as “processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulates and / or transforms data represented as physical quantities (e.g., electronic data) within the computer's or computing system's registers and / or memories into other data similarly represented as physical quantities within the computer's or computing system's memories, registers, or other such information storage, transmission, or display devices. Embodiments are not limited in this regard.

[0170] It should be noted that the methods described herein do not have to be performed in the order described or in any particular order. Further, the various activities described with respect to the methods described herein can be executed in serial or parallel.

[0171] While specific embodiments have been shown and described in detail to illustrate the application, it will be understood by those skilled in the art that any

[0172] While the subject matter has been described above in terms of specific embodiments, it is not intended to be limited to the embodiments described. Rather, it is

[0173] As used herein, an element or operation recited in the singular and preceded with the word “a” or “an” should be understood as not excluding plural elements or operations, unless explicitly stated that “only one” is intended. Further, the use of “one embodiment” or “an implementation” throughout is not intended to mean the same embodiment or implementation unless explicitly so defined.

[0174] As used in this specification and claims, recitations in the general form of "at least one of [a] and [b]" should be interpreted as disjunctive. For example, recitation of "at least one of [a], [b], and [c]" would include [a] alone, [b] alone, [c] alone, or any combination of [a], [b], and [c].

[0175] The present disclosure is not limited in scope by the specific embodiments described herein. In fact, in addition to those embodiments described herein, various other embodiments and modifications of the present disclosure will be apparent to those of ordinary skill in the art based on the foregoing description and drawings. Therefore, such other embodiments and modifications are intended to fall within the scope of the present disclosure. In addition, although the present disclosure has been described herein in the context of specific embodiments in a specific environment for a specific purpose, those of ordinary skill in the art will recognize that its practicality is not limited thereto, and the present disclosure can be advantageously implemented in any number of environments for any number of purposes.

Claims

1. A system for determining a patient's risk of developing an infection, the system comprising: An integrated healthcare system that is configured to: extracting patient history data from one or more databases corresponding to a pool of patients receiving home peritoneal dialysis treatment, the pool of patients comprising at least one first training set of patients previously diagnosed with an infection within a selected time period and at least one second training set of patients who did not develop an infection within the selected time period; training one or more predictive models using the at least one second training set to determine factors associated with a diagnosis of no infection; Analyzing the patient data of the patient via the one or more predictive models to: determining a patient risk score for the patient for developing an infection within the selected time period; and determining at least one reason associated with a patient risk score that identifies a predisposing factor for developing an infection; In response to the patient risk score being above a predetermined threshold, determining at least one personalized intervention treatment for the patient based on the patient risk score and the at least one cause to reduce the patient's risk of developing an infection; generating a report comprising the patient risk score and the at least one personalized intervention treatment; and The report is transmitted to one or more healthcare facilities associated with the patient.

2. The system according to claim 1, wherein: The one or more predictive models are arranged and configured to: Extracted patient data were analyzed to identify patient characteristics shared by patients with previously documented reports of infection; as well as When generating a patient risk score for developing an infection within a selected time period, patient characteristics are identified for each patient in the patient pool.

3. The system according to claim 2, wherein: The one or more predictive models are arranged and configured to: Extracted patient data were analyzed to identify patient characteristics shared by those patients without previously documented reports of infection.

4. The system according to claim 1, wherein: The one or more predictive models are arranged and configured to: Identify characteristics of patients previously diagnosed with infection; and The extracted patient data were analyzed based on common characteristics.

5. The system according to claim 1, wherein: The interventional treatments include: sending a questionnaire to one or more patients to obtain additional information regarding the patient's dialysis practice; Contact one or more patients to determine appropriate interventions to help minimize the risk of developing infection; Contacting one or more patients to evaluate the patient's dialysis treatment; Change one or more conditions concerning the patient's dialysis administration; or dispatching a medical professional to one or more patients in the identified subset of the patient pool to perform an in-home vision assessment; or A combination of them.

6. The system according to claim 1, wherein: The report is generated at a predetermined period.

7. The system according to claim 1, wherein: The identified subset of the patient pool includes patients in a similar geographic area, patients assigned to a dialysis clinic, or a group of patients receiving care from a single medical professional, or a combination thereof.

8. The system according to claim 1, wherein: The report includes a subset of the patient pool whose corresponding patient risk scores are above a predetermined threshold.

9. The system according to claim 1, wherein: The predetermined threshold is determined by the one or more prediction models based on historical data.

10. The system according to claim 1, wherein: The report includes all patients associated with a particular medical group.

11. The system according to claim 1, wherein: The report includes one or more associated reasons for each patient.

12. The system according to claim 1, wherein: The extracted patient data includes patient demographics, laboratory values, recorded information, physician notes, or treatment data, or a combination thereof.

13. The system according to claim 12, wherein: The patient demographic characteristics include gender, race, age, or marital status, or a combination thereof.

14. The system according to claim 12, wherein: The laboratory value includes the patient's albumin level, the patient's calcium level, the patient's chloride level, the patient's creatinine level, or the patient's transferrin saturation (TSAT) level, or a combination thereof.

15. The system according to claim 14, wherein: The laboratory values ​​include the period of time the patient has been receiving dialysis treatment, the period of time the patient was last diagnosed with an infection, the total number of previous infections the patient has had, or the distance of the patient's home from the dialysis facility, or a combination thereof.

16. A method for determining a patient's risk of developing an infection, the method comprising: extracting patient history data from one or more databases corresponding to a pool of patients receiving home peritoneal dialysis treatment, the pool of patients comprising a first pool of patients previously diagnosed with an infection within a selected time period and a second pool of patients not having developed an infection within the selected time period; forming a training set representing at least a portion of the first patient pool and a portion of the second patient pool; training one or more predictive models using the training set to determine factors associated with a diagnosis of infection and factors associated with a diagnosis of no infection; Analyzing the patient data of the patient via the one or more predictive models to: determining a patient risk score for the patient for developing an infection within the selected time period; and determining at least one reason associated with a patient risk score that identifies a predisposing factor for developing an infection; and In response to the patient risk score being higher than a predetermined threshold, implementing at least one personalized intervention treatment for the patient based on the patient risk score and the at least one cause to reduce the patient's risk of developing an infection, generating a report comprising the patient risk score and the at least one personalized intervention treatment; and The report is transmitted to one or more healthcare facilities associated with the patient.

17. The method according to claim 16, wherein: The one or more predictive models are arranged and configured to: Extracted patient data were analyzed to identify patient characteristics shared by patients with previously documented reports of infection; as well as When generating a patient risk score for developing an infection within a selected time period, patient characteristics are identified for each patient in the patient pool.

18. The method according to claim 17, wherein The one or more predictive models are arranged and configured to: Extracted patient data were analyzed to identify patient characteristics shared by those patients without previously documented reports of infection.

19. The method according to claim 16, wherein The one or more predictive models are arranged and configured to: Identify characteristics of patients previously diagnosed with infection; and The extracted patient data were analyzed based on common characteristics.

20. The method according to claim 16, wherein The interventional treatments include: sending a questionnaire to one or more patients to obtain additional information regarding the patient's dialysis practice; Contact one or more patients to determine appropriate interventions to help minimize the risk of developing infection; Contacting one or more patients to evaluate the patient's dialysis treatment; Change one or more conditions concerning the patient's dialysis administration; or dispatching a medical professional to one or more patients in the portion of the identified subset of the patient pool for an in-home vision assessment; or A combination of them.

21. The method according to claim 16, wherein The report is generated at a predetermined period.

22. The method according to claim 16, wherein The identified subset of the patient pool includes patients in a similar geographic area, patients assigned to a dialysis clinic, or a group of patients receiving care from a single medical professional, or a combination thereof.

23. The method according to claim 16, wherein The report includes a subset of the patient pool whose corresponding patient risk scores are above a predetermined threshold.

24. The method according to claim 16, wherein The predetermined threshold is determined by the one or more prediction models based on historical data.

25. The method according to claim 16, wherein The report includes all patients associated with a particular medical group.

26. The method according to claim 16, wherein The report includes one or more associated reasons for each patient.

27. The method according to claim 16, wherein The extracted patient data includes patient demographics, laboratory values, recorded information, physician notes, or treatment data, or a combination thereof.

28. The method according to claim 27, wherein The patient demographic characteristics include gender, race, age, or marital status, or a combination thereof.

29. The method according to claim 27, wherein The laboratory value includes the patient's albumin level, the patient's calcium level, the patient's chloride level, the patient's creatinine level, or the patient's transferrin saturation (TSAT) level, or a combination thereof.

30. The method according to claim 29, wherein The laboratory values ​​include the period of time the patient has been receiving dialysis treatment, the period of time the patient was last diagnosed with an infection, the total number of previous infections the patient has had, or the distance of the patient's home from the dialysis facility, or a combination thereof.

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