Systems and methods for identifying comorbidities

Abstract

A method and system for identifying undocumented comorbid conditions in a patient. In one embodiment, the method includes: extracting patient data from one or more databases corresponding to a pool of patients receiving treatment; using one or more predictive models in conjunction with the extracted patient data to generate a corresponding patient risk score for each patient in the patient pool for having an undocumented comorbid condition; identifying a subset of the patient pool having a corresponding patient risk score above a predetermined threshold; and identifying one or more patients based on the identified subset of the patient pool for additional evaluation, additional review, or a combination thereof.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a non-provisional application of and claims the benefit of the filing dates of pending U.S. Provisional Patent Application No. 62 / 680,938, filed on June 5, 2018, entitled “Systems and Methods for Identifying Comorbidities,” and is a non-provisional application of and claims the benefit of the filing dates of pending U.S. Provisional Patent Application No. 62 / 716,032, filed on August 8, 2018, entitled “Systems and Methods for Identifying Comorbidities,” and is a non-provisional application of and claims the benefit of the filing dates of pending U.S. Provisional Patent Application No. 62 / 836,822, filed on 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 systems, devices, and methods related to medical care. Background Art

[0004] The traditional healthcare system is based on a fee-for-service model whereby healthcare providers are compensated on a per-treatment or per-service basis. Under this model, as the number of treatments or services provided increases, the healthcare provider's compensation also increases. Therefore, there is no financial incentive for such providers to efficiently manage the number of services / procedures provided, nor is there any financial incentive tied to the overall health outcomes of the patient. This traditional system has resulted in escalating healthcare costs and inefficiencies, which hinders the quality of overall care for patients.

[0005] Furthermore, many patients—particularly those with chronic conditions—engage with a variety of different entities and healthcare professionals during the diagnosis, treatment, and long-term care management process, including hospitals, clinics, laboratories, pharmacies, physicians, clinicians, and / or other specialists. Patient treatment information may be scattered across multiple entities, repositories, and medical professionals, which may result in a lack of or poor communication between the various entities involved, which may adversely affect the patient's treatment and health, and may even result in life-threatening treatment conditions. Furthermore, this uncoordinated handling of data and the overall treatment of patients leads to inefficiencies that may result in increased overall healthcare costs. In this regard, the traditional fee-for-service healthcare model is far from ideal in terms of both quality of care and affordability. The latter is evidenced by the continued growth in healthcare costs in the United States that are difficult to sustain under the fee-for-service model.

[0006] With regard to these and other considerations, the present improvements may be useful. Summary of the invention

[0007] The present invention summary is provided to introduce some concepts in a simplified form, and these concepts will be further described in the following detailed description. The present invention summary is not intended to necessarily identify the key features or essential features of the present disclosure. The present disclosure may include the following various aspects and embodiments.

[0008] According to an exemplary embodiment of the present disclosure, a method for identifying undocumented comorbid conditions 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 receiving treatment; using one or more predictive models together with the extracted patient data to generate a corresponding patient risk score for each patient in the patient pool for having an undocumented comorbid condition; identifying a subset of the patient pool having a corresponding patient risk score above a predetermined threshold; and identifying one or more patients based on the identified subset of the patient pool for additional evaluation, additional review, or a combination thereof.

[0009] In this and other embodiments, the method also includes: performing a review of patient records based on a subset of the identified patient pool, wherein the review of patient records includes reviewing patient records for bill corrections, patient data corrections, providing clinical interventions, changing the patient's treatment plan, or a combination thereof.

[0010] In this and other embodiments, the method also includes: performing a review of patient records based on a subset of the identified patient pool, wherein the review of patient records includes identifying any overlooked, unreported or unrecorded conditions, correcting any overlooked, unreported or unrecorded conditions, correcting billing errors, providing clinical decision support, or a combination thereof.

[0011] In this and other embodiments, the patient risk score includes an aggregated risk score from an aggregated comorbidity model, or one or more individual risk scores from one or more individual models, or a combination thereof.

[0012] In this and other embodiments, using one or more predictive models to generate a patient risk score includes: using one or more separate predictive models to generate separate patient risk scores associated with separate conditions; and inputting the separate patient risk scores into an aggregated comorbidity model for calculating the patient risk score.

[0013] In this and other embodiments, one or more individual predictive models are selected from one of the following models: a gastrointestinal (GI) bleeding model, a pericarditis model, a myelodysplasia model, a hemolytic anemia model, a sickle cell anemia model, or a clustered comorbidity model, or a combination thereof.

[0014] In this and other embodiments, the method further includes generating a report including at least a portion of the identified subset of the patient pool and their corresponding patient risk scores; and transmitting the report to one or more medical care organizations.

[0015] In this and other embodiments, the one or more health care organizations also identify one or more patients for additional review from a portion of the subset of the identified patient pool; and for each patient identified for additional review, perform one or more of the following operations: review the patient records to identify any overlooked, unreported and / or unrecorded conditions; obtain additional information or documentation, or a combination thereof; update the patient record to reflect the diagnosis, or the possibility of one or more co-morbid conditions, or a combination thereof; or compare the billing request items to the corresponding patient risk scores.

[0016] In this embodiment and other embodiments, the predetermined threshold is a predetermined threshold percentage, and the predetermined threshold percentage is equal to or greater than 50%.

[0017] In this and other embodiments, the method further includes, prior to using the one or more predictive models, analyzing the extracted patient data to qualify one or more patient records for inclusion in the one or more predictive models.

[0018] In this and other embodiments, qualifying one or more patient records includes removing any outlier patients, patients with predetermined clinical indicators, or patients associated with the Centers for Medicaid & Medicare Services, or a combination thereof.

[0019] In this and other embodiments, qualifying one or more patient records includes including any patient having a predetermined clinical indicator including a documented GI bleed.

[0020] In this and other embodiments, the extracted patient data includes laboratory values, hospitalization history, hospitalization records, pharmacy prescription history, other recorded comorbid conditions, patient demographic information, medical care provider notes, or keywords, or a combination thereof.

[0021] In this and other embodiments, the extracted patient data includes laboratory values, including hemoglobin level, PT, platelet count, transferrin saturation (TSAT), or calcium level, or a combination thereof. The laboratory values ​​include standard deviation, time-weighted average, maximum value, minimum value, trend value, or data peak value, or a combination thereof.

[0022] In this and other embodiments, the method further includes transmitting the updated patient information to one or more medical care facilities to inform the one or more medical care facilities of the patient's co-morbid condition.

[0023] According to an exemplary embodiment of the present disclosure, a system for determining undocumented comorbid conditions in a patient is disclosed. In one embodiment, the system includes an integrated care system configured to: extract patient data from one or more databases corresponding to a pool of patients receiving treatment; use one or more predictive models together with the extracted patient data to generate a corresponding patient risk score for each patient in the patient pool for having an undocumented comorbid condition; identify a subset of the patient pool having a corresponding patient risk score above a predetermined threshold; and identify one or more patients based on the identified subset of the patient pool for additional evaluation, additional review, or a combination thereof.

[0024] In this and other embodiments, the integrated care system is configured to be able to perform a review of patient records based on a subset of the identified patient pool, wherein the review of patient records includes reviewing patient records for billing corrections, patient data corrections, providing clinical interventions, changing a patient's treatment plan, or a combination thereof.

[0025] In this and other embodiments, the integrated care system is configured to be able to perform a review of patient records based on a subset of the identified patient pool, wherein the review of patient records includes identifying any overlooked, unreported, or unrecorded conditions, correcting any overlooked, unreported or unrecorded conditions, correcting billing errors, providing clinical decision support, or a combination thereof.

[0026] In this and other embodiments, the patient risk score includes an aggregated risk score from an aggregated comorbidity model, or one or more individual risk scores from one or more individual models, or a combination thereof.

[0027] In this and other embodiments, using one or more predictive models to generate a patient risk score includes: using one or more separate predictive models to generate separate patient risk scores associated with separate conditions; and inputting the separate patient risk scores into an aggregated comorbidity model for calculating the patient risk score.

[0028] In this and other embodiments, the one or more separate predictive models are selected from one of the following models: a gastrointestinal (GI) bleeding model, a pericarditis model, a myelodysplasia model, a hemolytic anemia model, a sickle cell anemia model, or a clustered comorbidity model, or a combination thereof.

[0029] In this and other embodiments, the integrated care system is configured to: generate a report including at least a portion of the identified subset of the patient pool and their corresponding patient risk scores; and transmit the report to one or more medical care organizations.

[0030] In this and other embodiments, the one or more health care organizations also identify one or more patients for additional review from a portion of the subset of the identified patient pool; and for each patient identified for additional review, perform one or more of the following operations: review the patient records to identify any overlooked, unreported and / or unrecorded conditions; obtain additional information or documentation, or a combination thereof; update the patient record to reflect the diagnosis, or the possibility of one or more co-morbid conditions, or a combination thereof; or compare the billing request items to the corresponding patient risk scores.

[0031] In this and other embodiments, the integrated care system is configured to analyze the extracted patient data prior to using one or more predictive models to qualify one or more patient records for inclusion in the one or more predictive models.

[0032] In this and other embodiments, the extracted patient data includes laboratory values, hospitalization history, hospitalization records, pharmacy prescription history, other recorded comorbid conditions, patient demographic information, medical care provider notes, or keywords, or a combination thereof.

[0033] In this and other embodiments, the extracted patient data includes laboratory values, including hemoglobin level, PT, platelet count, transferrin saturation (TSAT), or calcium level, or a combination thereof.

[0034] In this and other embodiments, the integrated care system is configured to transmit updated patient information to one or more medical care organizations to inform the one or more medical care organizations of the patient's co-morbid conditions.

[0035] Other features and aspects are described in further detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By way of example, embodiments of the disclosed method and apparatus will now be described with reference to the accompanying drawings, in which:

[0037] Figure 1A is a schematic diagram showing an exemplary embodiment of a comorbidity model according to the present disclosure;

[0038] Figure 1B is a flow chart illustrating one exemplary embodiment of a process for identifying patient comorbidities according to the present disclosure;

[0039] Figure 1C is an exemplary embodiment of a chart of patient data for identifying co-morbidities according to the present disclosure;

[0040] Figure 1D is a flow chart illustrating one exemplary embodiment of a process for identifying patient comorbidities according to the present disclosure;

[0041] Figure 2A is a schematic diagram illustrating an exemplary embodiment of a system for providing coordinated medical care according to the present disclosure;

[0042] Figure 2B is a schematic diagram illustrating one exemplary embodiment of a system for assessing and treating diseases including kidney disease according to the present disclosure;

[0043] Figure 3 is a block diagram illustrating an exemplary embodiment of an integrated care system according to the present disclosure;

[0044] Figure 4 is a block diagram illustrating an exemplary embodiment of an operating environment according to the present disclosure;

[0045] Figure 5 is a block diagram illustrating an exemplary embodiment of another operating environment according to the present disclosure;

[0046] Figure 6-10 is a schematic diagram illustrating an exemplary embodiment of components of a system for providing coordinated medical care according to the present disclosure;

[0047] Fig.11 is a schematic diagram illustrating an exemplary embodiment of a care coordination component of a system for providing coordinated medical care according to the present disclosure;

[0048] Fig.12 A schematic diagram of an exemplary embodiment of a dialysis machine is shown;

[0049] Figures 13A-13B An exemplary embodiment of a dialysis system according to the present disclosure is shown;

[0050] Fig.14 is a diagram illustrating another exemplary embodiment of a dialysis system according to the present disclosure; and

[0051] Fig.15 is a block diagram illustrating one exemplary embodiment of a computing architecture according to the present disclosure. DETAILED DESCRIPTION

[0052] The present embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which a number of exemplary embodiments are shown. However, the subject matter of the present disclosure may be embodied in many different forms and types of methods and devices for dialysis machines and other potential medical devices, diagnosis and treatment for various diseases, and should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided so that the present 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, like reference numerals represent like elements throughout.

[0053] The example embodiments described herein are suitable for implementing value-based care, which is an alternative to the fee-for-service medical care model. Under a value-based medical care system (also known as a "pay-for-performance" model), medical care providers receive financial incentives related to the quality and efficiency of care and patient outcomes.

[0054] Some example embodiments are configured to provide coordinated care to a patient population with a chronic disease such as chronic kidney disease (CKD). CKD is a progressive disease characterized by decreased kidney function. Once kidney function drops 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 (without transplantation) for the rest of the patient's life.

[0055] In the United States, one model of value-based care that can implement the example embodiments described herein is the Comprehensive ESRD Care (CEC) model, which is an Accountable Care Organization (ACO) model developed under the authorization of the Center for Medicare and Medicaid Innovation in the United States. In order to implement the CEC model, an ESRD Seamless Care Organization (ESCO) was formed. An ESCO is an ACO formed by voluntarily coming together of healthcare suppliers and providers. The resulting ESCO is a legal entity that provides coordinated care for ESRD beneficiaries through the CEC model.

[0056] Under the ESCO model, the ESCO shares the savings and losses of the Centers for Medicare and Medicaid Services (CMS) for the ESCO's beneficiaries. Savings or losses are determined by CMS based on a spending benchmark, which is derived from a baseline that reflects historical spending data for similar or comparable beneficiaries. This benchmark is compared to the actual Medicare Fee-For-Service (FFS) Part A and B spending for the participating patient population during the performance year. Savings will also be adjusted based on quality performance. Any reduction in costs translates directly into increased shared savings (benefits) because expenses are measured against a predetermined benchmark. Quality of care is incentivized by applying quality performance adjustments to the calculated shared savings.

[0057] The ESCO is responsible for the overall care of each patient, which goes beyond the scope of 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 will be included in the annual savings calculation. Since hospitalization is particularly expensive, it is very beneficial for the ESCO to avoid patient hospitalization from a financial perspective. The example embodiments described herein implement a holistic approach to oversee and manage all aspects of patient well-being, which improves the quality of care while improving the efficiency of medical resources and overall cost efficiency.

[0058] Some example embodiments described herein analyze medical data of applicable patient populations to intervene in high-risk patients to reduce the likelihood of hospitalization. Some examples analyze patient data to predict when a patient is likely to experience a specific health-related event or disease process stage and provide / adjust treatment accordingly.

[0059] According to an example embodiment, patient information can be sent to a coordinated care system, managed in a coordinated care system and / or accessible by a coordinated care system, so that patients can receive high-quality, efficient, coordinated medical care in a management system that can intelligently manage and coordinate the overall care of patients. The merged coordinated care system can, for example, replace fee-for-service care by providing value-based care to patients, thereby better controlling medical care costs. For example, as described above, the patient population diagnosed with ESRD increases over time, usually caused by 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 may face additional challenges. For example, necessary lifestyle changes may cause mental health to deteriorate. In addition, treatment at home may cause increased isolation from medical professionals. As the medical care landscape changes, providing patients with the opportunity to use resources for coordinated treatment may bring additional patient health benefits beyond dialysis treatment.

[0060] Although the exemplary embodiments described herein are related to kidney disease, it should be understood that the coordinated care system and infrastructure described herein can be substituted for kidney disease or applied to other chronic diseases in addition to kidney disease. As non-limiting examples, such other diseases can include cardiovascular-related diseases, lung, 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 a combination thereof. In addition, although some examples are described with respect to kidney-related ACOs, such as implementation in ESCOs, it should be understood that the examples described herein can be similarly implemented in other ACOs with respect to other diseases or patient populations and / or any other suitable value-based medical care models.

[0061] Some exemplary embodiments of value-based medical care models can implement systems and methods for identifying comorbidities. As described below, some comorbidities may be eligible for reimbursement. CMS can provide reimbursement for treatment and / or other active care to patients and / or medical care providers. Therefore, it may be advantageous to correctly identify the condition of the patient being treated, for example, for the purpose of reimbursement and / or for the comorbidity condition of the purpose of clinical decision support for patient treatment. However, ESRD patients may be transitioning to and / or may visit multiple different medical care providers, so that their records and data may be incomplete or ignored. For example, patients who transition to dialysis clinics can register by entering their historical data into the integrated care system. Data can be received in different formats, including but not limited to doctor's notes, laboratory values, check boxes, etc. Patients may have been previously diagnosed with comorbidities, but may be ignored, unreported and / or unrecorded on their records so that new dialysis clinics may not be properly informed of complete medical records. If a co-morbid condition is not reported and / or recorded, inappropriate or missing billing criteria may be associated with the patient's treatment, which may result in the patient and / or the healthcare provider losing reimbursement, and / or losing the opportunity to use the co-morbid condition to provide clinical decision support for the patient's treatment. Systems and methods according to the present disclosure can be used to provide similar methods for identifying and / or correcting any overlooked, unreported, and / or unrecorded conditions, correcting billing errors based on these conditions, and / or using the co-morbid condition to provide clinical decision support for the patient's treatment.

[0062] like Figure 1D As shown, a flowchart 170 is shown, which illustrates an exemplary embodiment of a process for identifying comorbidities. As described above, at step 172, patient population data in an integrated care system or a care analytics and guidance system can be analyzed to train a machine learning model for identifying patient comorbidities. The integrated care system can evaluate a large amount of patient data, for example, an entire selected patient population treated at an associated institution (e.g., a patient population treated at a dialysis clinic). At step 173, preselected criteria can be analyzed to qualify each patient record for inclusion in the model. For example, outlier patients, patients with predetermined clinical indicators, and / or patients associated with Medicaid and the Centers for Medicare and Medicaid Services (CMS) may be excluded (or included) in the population used to train the model.

[0063] At step 174, predetermined clinical indicators can further refine the patient population. For example, clinical indicators associated with recorded GI bleeding can be analyzed. Thus, patients who are not diagnosed as having GI bleeding can be removed from the patient population to train the model. Patient records that meet the diagnostic criteria for GI bleeding can be included, so the model can analyze commonalities between patients with respect to laboratory values, hospitalization records, physician / nurse notes, prescriptions, other diagnosed comorbidities, etc. to refine the risk score calculation for patients with associated comorbidities. In an embodiment, the risk score calculation can be one or more probabilities, or other statistical calculations. At step 176, new patient records can be run through the machine learning model to identify overlooked, unreported and / or unrecorded comorbidities in the patient, such as with respect to Figure 1A as described.

[0064] As described above, patient data may be sent to and / or accessed by an integrated care system. An integrated care system or a care analysis and guidance system may receive, store, and / or determine relevant demographic information and laboratory values ​​or other data for use in calculations. The integrated care system may then use these calculations to identify unreported and / or undocumented patient comorbidities. Figure 1A , a schematic diagram 100 of a comorbidity model according to the present disclosure is shown. Patient data may be fed into a plurality of separate models 105a, 105b, ..., 150n associated with separate comorbidity calculations, "n" being any number of separate models / comorbidities. For example, separate comorbidity models 105a, 105b, ..., 105n may be associated with specific comorbid conditions. Patient records, including billing records, may be corrected to reflect one or more of the associated separate comorbid conditions. Figure 1AAs shown, the separate models may include a gastrointestinal (GI) bleeding model 105a, a pericarditis model 105b, a myelodysplastic model 105c, a hemolytic anemia model 105d, and / or a sickle cell anemia model 105e. The patient may have been diagnosed with one or more of these comorbidities, but through non-standardized records and transfers to different medical care providers, the diagnosis may not have been correctly entered into the integrated care system, for example, so that the diagnosis is overlooked, unreported, and / or unrecorded. Therefore, the separate models 105a, 105b, ..., 105n can calculate a risk score that the patient may have an associated comorbid condition based on an analysis of the provided records. The separate models 105a, 105b, ..., 105n can be an implementation of any standard machine learning technique (e.g., a random forest model, logistic regression, etc.). In some embodiments, a logistic regression model with L2 regularization can be utilized, and as described above, a machine learning model trained to associate patient data with known comorbid conditions can be utilized. For example, each comorbid condition to be predicted may have a machine learning model specifically designed and trained to predict that comorbid condition.

[0065] Each individual model 105a, 105b, ..., 105n can calculate an individual risk score for a comorbid condition that the patient may have associated. For example, laboratory values ​​associated with comorbid conditions can be used in individual models, for example, hemoglobin levels can be analyzed in anemia model 105d. In some embodiments, individual models 105a, 105b, ..., 105n can include patient data from laboratory values ​​for the previous 3, 6 and / or 12 months, for example, including but not limited to hemoglobin (Hgb), PT, platelet count, transferrin saturation (TSAT) and / or calcium levels. Laboratory values ​​can also be calculated values, for example, standard deviation, time-weighted average, maximum, minimum, trend value and / or data peak, for analysis by the integrated care system.

[0066] In some embodiments, the individual models 105a, 105b, ..., 105n may also include, for example, the patient's hospitalization history from the previous 3, 6, and / or 12 months. In some embodiments, pharmacy data and other information such as medication prescriptions may be included in the individual models 105a, 105b, ..., 105n for analysis. For example, patient records indicating past or current prescriptions for treating symptoms associated with individual comorbidities may be included in the individual models used to calculate the risk score.

[0067] Additionally, the patient data may also be evaluated for other documented comorbidities that may be associated with an individual comorbidity. For example, a patient with a documented comorbidity of hemolytic anemia may have a higher risk score for also having a comorbidity of sickle cell anemia. The individual models 105a, 105b, ..., 105n may also include patient demographic information, including but not limited to age, ethnicity, gender, race, geographic location, etc. Some patients may have higher comorbidity risk scores based on demographic information, which may be included in the model used to evaluate the risk score.

[0068] In some embodiments, the individual models 105a, 105b, ..., 105n may also include physician / nurse notes or other care provider notes. A list of keywords may be associated with each individual model, and the keywords may be individual words, phrases, and / or may include wildcards, modifiers, or combinations thereof. In some embodiments, the keywords may include an explanation of the symptoms. In some embodiments, the keywords may also include "missing," for example, a word that may indicate that the patient does not have a separate comorbid condition. These "missing" keywords may be included in the risk score calculation by lowering the patient's risk score. Natural language processing techniques may be used to process patient record annotations, including but not limited to "bag of words" techniques and doc2vec.

[0069] For example, the GI bleeding model 105a may include keywords such as "blood", "bleeding", "gi", "stomach", "ulcer", "diverticulum", "angioproliferative disease", "no bleeding", "w / out bleeding", "unclassified", "nec", "blood transfusion", "rectal", "rectal", "anal", "melasma", "gi history", and / or "gastrointestinal history", or a combination thereof. The pericarditis model 105b may include keywords such as "pericarditis", "acute pericarditis", "cardiac tuberculosis", "atypical chest pain", "pericardial friction rub", and / or "pericardial effusion", or a combination thereof. The myelodysplasia model 105c may include keywords such as "thrombocythemia", "refractory anemia", "cytopenia (refractory)", "myelodysplasia", "chronic myeloproliferative disease", "dysplasia", "bone marrow puncture and biopsy", "blood transfusion", and / or "MDS", or a combination thereof. The hemolytic anemia model 105d and / or the sickle cell anemia model 105e may include keywords such as "sickle cell disease," "sickle cell disease crisis," "peripheral smear," "thalassemia," "Hb-SS disease," "sickle cell disease," "anemia," and / or "blood transfusion," or combinations thereof.

[0070] As described above, each model 105a, 105b, ..., 105n can analyze associated patient data, including laboratory values, hospitalization history, pharmacy prescription history, other documented comorbid conditions, patient demographic information, and / or keywords to calculate a risk score for the patient with each individual comorbid condition. As described below, a report can be generated that includes patient identification information, clinic information, and a risk score for each comorbid condition. If the risk score is above a predetermined percentage (e.g., equal to or greater than 50%), the identified patient can be sent to a representative for a full record review, e.g., for purposes of bill correction and / or utilizing the risk score for the comorbid condition for purposes of clinical decision support for the treatment of the patient, among other purposes.

[0071] Return to reference Figure 1A , when the individual models 105a, 105b, ..., 105n have calculated risk scores associated with individual comorbid conditions, they can then be fed into the aggregate comorbidity model 110 for calculating risk scores for patients with any comorbid condition. It should also be understood that the additional model may include any subset or combination of the individual models.

[0072] Individual models 105a, 105b, ..., 105n and comorbidity model 110 may be Figure 1B The prediction model is run in step 124, Figure 1B An exemplary embodiment of a method for identifying comorbidities in a patient according to the present disclosure is shown. Figure 1B As shown, method 120 may include automatically generating a population of newly added patients to the integrated care system from the previous month at step 122. For example, the integrated care system may generate a population of new patients for review during the first week of each month.

[0073] As described above, because these patients have been transferred from various healthcare providers to the integrated care system, their records may include overlooked, unreported, and / or undocumented co-morbid conditions. The integrated care system can analyze the patient data and correct the patient records as identified, which may be beneficial for providing appropriate clinical intervention (e.g., affecting patient treatment) and / or for billing purposes (e.g., appropriate coding for reimbursement). Additionally, it should be appreciated that correct patient data in the integrated care system can improve internal research programs, predictive modeling, etc.

[0074] At step 126, after running the individual models 105a, 105b, ..., 105n and the aggregate model 110, the risk scores associated with the comorbid conditions in the patient may be output in a report (see Figure 1C). For example, each institution (e.g., a clinic, hospital, physician's office, and / or other care provider) can receive a list of associated patients and their risk scores for comorbid conditions. The healthcare provider can evaluate the report and flag the patient for additional review. As shown in step 126, for example, the healthcare provider can compare the request items, such as billing request items, to the modeled risk scores. In response to identifying a patient (e.g., Medical Record Number (MRN)) associated with a comorbid condition that is overlooked, unreported, and / or unrecorded in the patient record, the patient record can be flagged for additional review for purposes of the examples explained herein involving patient treatment, billing, research planning, improved modeling, etc.

[0075] At step 128, the clinician, care provider, coder, or other representative or associate may receive a list of all patients that have been flagged for evaluation or additional review. Each flagged patient record may then undergo a comprehensive review of all documentation received and information associated with the patient. In this manner, co-morbid conditions that may have previously been overlooked, unreported, and / or undocumented may be identified. In some embodiments, at step 134, the associate may work with the associated facility (e.g., clinic) where the flagged patient received care to obtain additional information and / or other existing or missing documentation. The flagged patient may then undergo further review by a healthcare provider, as shown in step 126, to compare the requested items to the modeled risk score. In some embodiments, step 134 may be combined with clinical review 178 (see Figure 1D ) in which a coder or associate can request records from the corresponding agency regarding one or more patients marked for additional review.

[0076] The patient record can then be updated in the integrated care system to reflect their diagnosis, potential co-morbid conditions, etc. For example, in a billing context, a request item correction can be generated and submitted to CMS at step 130. In some embodiments, the integrated care system can automatically generate correction codes and can be configured to interact with the CMS system. This may be beneficial in further reducing the possibility of human error and may simplify the billing process and speed up reimbursement turnaround time.

[0077] Simultaneously with step 130 and / or subsequent to step 130, at step 136, the corrected patient data may be sent to the respective agencies so that the relevant healthcare providers may be informed of the patient's comorbid conditions. Additionally, the agencies may be updated with the corrected patient data to ensure that the correct claim item coding continues to be used for patient treatment. In some embodiments, additional review of the patient data may result in refinement of one or more of the individual models 105a, 105b, ..., 105n and / or the comorbidity model 110. The model may be updated or refined, as shown in step 132, which may be fed back to the model run for the CMS patient in step 124.

[0078] In some embodiments, at step 138, the generated request item corrections or missing or overlooked request items can be analyzed as key performance indicators (KPIs) for tracking and improvement. This can encourage continuous improvement of the method 120 and internal tracking of request item reimbursement, and / or track status for treatment purposes and clinical decision support. Return to reference Figure 1D , at step 180, the request terms may be adjusted based on the corrected co-morbid condition diagnosed for the patient. The corrected information may be provided to the appropriate billing personnel and / or respective facilities (e.g., clinics) so that future billing for the selected patient may be correct. Because new patients may be evaluated on a monthly basis, unreported and / or unrecorded co-morbid conditions may be quickly identified, thereby improving the reimbursement process with insurance providers and regulators such as CMS, as well as providing accurate patient records to healthcare providers to administer appropriate and timely treatment to patients.

[0079] As described above, a report can be generated that includes the patient for further review. Figure 1C, shows an exemplary embodiment of a report 140 according to the present disclosure. The report may identify one or more patients 142, for example, by name and / or medical record number (MRN). It should be understood that the report 140 may include any number "n" of patients 142, 142b, ..., 142n. Additional identification information may also be included in the report 140, including but not limited to the patient's date of birth (DOB: Date Of Birth) 144 (e.g., 144a, 144b, ..., 144n), social security number (SSN: Social Security Number), address, etc. This may provide additional verification of each patient to ensure that the correct patient record is evaluated. The report 140 may further include the patient's associated institution or clinic 146 (e.g., 146a, 146b, ..., 146n). In some embodiments, the clinic address may be utilized, while in other embodiments, a unique identification number may be utilized. For each patient 142, a risk score 148 (e.g., 148a, 148b, ..., 148n) may also be included in the report 140. In an embodiment, the risk score 148 may be one or more of the calculated scores of the aggregated comorbidity model 110 and / or the individual models 105a, 105b, ..., 105n, or a combination thereof. Based on the risk score, the patients 142a, 142b, ..., 142n may be marked for additional review 150, for example, by indicating "yes" or "no" for each patient. It should be understood that the report 140 may be generated based on any combination of patient data. For example, the report 140 may be generated for all new patients in a selected institution (e.g., a clinic), all new patients of the same age or other demographic information, and / or all patients whose risk scores exceed a predetermined amount.

[0080] refer to Figure 2A , according to an example of the present disclosure includes a care coordination framework 200 for treating a patient or patient population 240. The overall care of the patient / population 240 is overseen and coordinated by a care coordination system 210. The care coordination system 210 includes a care analysis and guidance system 220 (which is interchangeably referred to herein as an "integrated care system"), which receives, analyzes, and creates data for coordinating the care of the patient / population 240. The care coordination system 210 utilizes a Care Navigation Unit (CNU) 230, which implements coordinated care based on the data received from the care analysis and guidance system 220. To manage the overall health and well-being of the patient / population 240, the care coordination system 210 communicates with many related entities and components. In Figure 2A In the diagram, double-arrow lines graphically represent communication and interaction flows / channels.

[0081] exist Figure 2A In the example shown, the care coordination system 210 coordinates care for a patient 240 between entities including a chronic care center or clinic 241, physicians 242 (who may include nephrologists, particularly for kidney patients), nurses 243, laboratories 244 (e.g., blood labs or other diagnostic labs), 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, medical equipment and supply providers 253, outpatient surgical centers (ASCs) 254, additional services 255, medical records 256, financial and billing records 257, and payers 258 (e.g., CMS or private insurance companies).

[0082] It should be understood that some example embodiments may include other entities not shown, and / or may exclude some of the entities shown. In addition, it should be understood that the communication channels shown are not exclusive, and the various entities may also communicate directly or indirectly between each other and / or the patient 240 as appropriate. In some examples, communication between the care coordination system 210 and one or more of the other entities may be indirect, passing through one or more intermediate entities. For example, the coordination of the nurse 243 may be conducted directly between the care coordination system 210 and the nurse 243, or via an intermediate channel such as the clinic 241, 248, the hospital 246, or any other suitable channel.

[0083] According to some examples, Figure 2AThe framework 200 can be used to treat diseases such as the progression of kidney disease, such as end-stage renal disease (ESRD) and / or chronic kidney disease (CKD). Patients with ESRD are patients who are receiving long-term care for kidney disease, such as through dialysis treatment. The treatment associated with ESRD patients may be diverse and distributed among multiple medical care providers, so that monitoring the health status trends of dialysis patients may be challenging. For example, patients may show varying degrees and unstable levels of functional / cognitive impairment, and may be accompanied by complex clinical abnormalities, regardless of the length of time the patient is on dialysis. Patient data may be held by various medical care providers and may not be standardized or fully recorded. As the patient's kidney disease progresses, the diagnosis may be overlooked, unreported and / or unrecorded. These unrecorded comorbidities may result in missing bills or billing errors. According to an exemplary embodiment of the present disclosure, the care coordination framework 200, including the care analysis and guidance system 220, can be configured to aggregate patient data into a single system and identify unreported and / or unrecorded comorbidities. Large amounts of patient data may provide information to the system 200, 200' such that a machine learning model may be trained to determine patient characteristics of documented comorbidities to identify unreported and / or undocumented patient comorbidities.

[0084] The care analysis and guidance system (integrated care system) 220 may include and execute various models and / or programs related to medical care. In some examples, these models and / or programs are specifically used to implement or execute specific value-based care frameworks (e.g., ESCO models, other ACO models, chronic special needs plans (C-SNP's: Chronic SpecialNeeds Plans), 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 can be provided for other chronic diseases, including but not limited to chronic kidney disease, or one or more of the other chronic diseases and conditions mentioned above. These medical care models may affect the way in which value-based care is provided to patients, such as by more effectively managing the care of patients within a specified structure, and may replace the traditional fee-for-service (FFS) model. The fee-for-service model may generally focus on the quantity rather than the quality of personalized patient care, and there is little motivation to improve the overall health of patients. Compared with the value-based model, this model may be less efficient and less effective.

[0085] Shifting patient care from the traditional fee-for-service model to a value-based healthcare model may improve the care received by patients, reduce total costs, and may 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 received by patients (e.g., clinical treatment 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 of 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 "risk-sharing" contract, in which the costs associated with the coordinated care of the disease and the patient and the savings are shared by the provider and the payer. This arrangement exists in the ESCO model described in more detail above.

[0086] In some embodiments, the care coordination system can identify, test and / or evaluate innovations through the CEC / ESCO framework to improve patient care for medical insurance beneficiaries diagnosed with ESRD. The care coordination system can provide a structure for dialysis clinics, nephrologists or other experts and / or other providers to be contacted with each other, to coordinate care for consistent beneficiaries. The value-based medical care model may motivate providers based on the quality of the care services provided. For example, the care coordination system may include incentives for improving care coordination, personalized patient care and / or improving the long-term health outcomes of patient populations. The care coordination system can also coordinate the treatment effects measured by medical insurance Part A (e.g., hospital insurance) and Part B (e.g., medical insurance) expenditures, including expenditures related to dialysis services for its consistent ESRD beneficiaries, such as clinical quality, finance, etc. It should be understood that some value-based medical care models may also include medical insurance Part D (e.g., prescription drug insurance) expenditures.

[0087] The integrated care system 220 can form part of a clinical system for diagnosing and treating patients in all aspects of care. The integrated care system 220 can be connected to additional clinical systems, including but not limited to pharmacies, CKD / ESRD data registries, etc. For example, the integrated care system can automatically send prescriptions and other patient information to the pharmacy based on information provided by medical professionals, and can send and receive data and information to the CKD / ESRD data registry to compare with other patients and predict future treatments. The integrated care system can determine events associated with CKD / ESRD and take appropriate measures, including but not limited to notifying patients, notifying clinicians when specific interventions are needed, and / or reminding clinicians of upcoming important intervention dates.

[0088] One or more external or external systems may also be connected to the integrated care system 220. For example, the external systems may include one or more of diagnostic and / or therapeutic equipment (such as dialysis machines), laboratories, doctors' offices, hospitals, and / or electronic medical records. Patient information may be sent and received between the integrated care system and the external systems so that patient care may be more efficient, standardized, and consistent across multiple functions. For example, the integrated care system 220 (see Figure 2A ) can receive information from a patient's electronic medical record, thereby accessing historical information. Dialysis units or dialysis machines, physician offices, laboratories, and hospitals can send information to and receive information from the integrated care system based on patient treatment, diagnosis, or information other than treatment or diagnosis.

[0089] As follows about Figure 12-15 As described, in some embodiments, the care coordination system can provide information to the dialysis machines 1200, 1300, 1400 for use in dialysis treatment. In some embodiments, the integrated care system can send a prescription for prescription dialysis treatment from a medical professional to the dialysis machines 1200, 1300, 1400, in which case the integrated care system can receive the prescription from a doctor's office or hospital. The integrated care system can also verify the prescribed treatment against the patient's laboratory tests or medical records, and in some cases, the prescription can be remotely programmed into the patient's dialysis machine, or the prescription can be forwarded to the machine for local settings. In this way, it can be ensured that the patient receives necessary and correct treatment, and the patient can be prevented from receiving or receiving an inappropriate amount of dialysis treatment, thereby reducing human errors and improving patient care. The integrated care system 220 is also capable of notifying relevant medical professionals based on information received from these external systems and additional clinical systems, for example, to provide appropriate medical treatment to the patient and / or correctly identify and correct bill errors or bill omissions based on the patient's diagnosis.

[0090] Figure 2B is another illustration of the Care Coordination Framework. Figure 2B The Care Coordination Framework 200' and Figure 2A The care coordination framework 200 of the present invention shares features described herein, unless otherwise noted. The care coordination framework 200' described in this example is provided for integrating patient care in treating renal diseases, for example, ESRD and / or CKD (although it may also be similar to Figure 2AThe care coordination system 210' may coordinate at least some aspects of patient care with an integrated care system 220' (which may include and execute models and / or programs 260 related to medical care) to support patient care. Various components may participate in the care coordination system 210' to provide complete patient care via the care framework. For example, any number of incorporated care components may send information to and receive information from the integrated care system 220', including but not limited to ancillary services components 265, data creation and / or management components 270, care provider components 275, equipment and / or supplies components 280, and regulatory components 285. In some embodiments, the care coordination system 210' may collaborate with third-party resources including but not limited to laboratory services, research, and the like. In some embodiments, the care framework may include, be implemented by, or be associated with a care navigation unit 230'. In Figure 2B In the example of FIG. 2 , it is noted that the care navigation unit 230 ′ is indicated as a separate entity from the care coordination system 210 ′, but it should be understood that in other examples (see, for example, Figure 2A ), the care navigation unit can be included as part of the care coordination system.

[0091] As described above, each component of the integrated care system (eg, care 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 service component 265 may include any number of "n" services 605a, 605b, ..., 605n related to auxiliary patient services. For example, the auxiliary service may include a laboratory 605a, a personalized care 605b, and / or a pharmacy 605c. Each of the auxiliary services 605a, 605b, ..., 605n may send and receive patient information to the integrated care system 220, 220' for compilation and analysis. For example, the laboratory may automatically send the results of the patient's blood test and other test results to the integrated care system 220, 220'. Additionally, the integrated care system 220, 220' may automatically send a test instruction to the laboratory to perform a selected test on the patient sample based on a determination from a medical professional and / or other information collected by the care coordination system 210' via the care framework. Similarly, the integrated care system 220, 220' may automatically send a prescription and dosage instruction to the pharmacy based on the patient's test results and other factors determined by the integrated care system 220, 220'. The pharmacy may also send information related to other patient prescriptions to the integrated care system 220, 220' for potential adverse drug interactions, how to refill prescriptions in a timely manner, and / or patient-pharmacist interactions, etc.

[0092] In some embodiments, patients can benefit from the care of nutritionists and / or dietitians 605d to adjust dietary restrictions as an integral part of their care. For example, ESRD patients may have prescribed dietary requirements as part of receiving hemodialysis and other treatments for their kidney disease. Patients may benefit from the consultation of nutritionists and / or dietitians, with a healthier diet lifestyle and other potential health-related benefits. Patient management fluid management 605e can also be managed to ensure that the patient is receiving the appropriate amount and type of fluid. Patients suffering from CKD and / or ESRD may have fluid restrictions to obtain 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 nutritionists and / or dietitians, although it should be understood that in other embodiments, the patient's fluid intake can be managed by another medical professional. In an embodiment, a patient can benefit from the care of a mental health professional 605f, for example, a psychologist, a psychiatrist and / or other consulting services. As described above, the patient's mental health may be affected by the course of the disease, otherwise it may be ignored by other medical professionals during treatment. Therefore, arranging and providing access to mental health professionals may improve the overall health of patients.

[0093] Reference now 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 and 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 nursing analysis and guidance system 220, 220'. For example, a patient's medical record can be automatically updated after receiving lab results, treatment information, and / or notes from a medical professional. The nursing analysis and guidance system 220, 220' can utilize the patient's medical record for trends or trigger events so that the nursing coordination system 210' can provide relevant information to the medical professional for treatment and other care selection recommendations and for scheduling and coordinating various possible interventions. In some embodiments, the nursing analysis and guidance system 220, 220' can analyze multiple patients as part of the data registry to determine global trends and analyze data from a macro level.

[0094] Figure 8An exemplary care provider component 275 is shown, including one or more units that provide patient care, as shown by reference numerals 805a, 805b, and 805n. Any number of "n" units may be included in the provider component 275. In some embodiments, the care provider may include a physician and / or physician group 805a (e.g., a primary care physician (PCP) and a specialist, such as a nephrologist), a practice management system 805b, a hospital 805c, and / or a clinic / center 805d, although additional or alternative care providers may also be envisioned. The integrated care system 220, 220' can send information to and receive information from the care provider for patient treatment. For example, the integrated care system 220, 220' can receive physician notes for patient examinations, hospitalization information, etc., and can send calculated information and other determined factors based on other patient data received. For example, the integrated care system 220, 220' can send estimates and treatment recommendations to identify, reduce, avoid and / or eliminate a patient's risk for aspects and / or effects of kidney disease or kidney disease treatment based on all received patient data and an evaluation thereof, to provide treatment to the patient, and / or including identification of unreported and / or undocumented comorbidities.

[0095] Fig. 9 An exemplary equipment and / or supplies assembly 280 for a single patient is shown, for example, therapeutic supplies, which may include any number of "n" services 905a, 905b, ..., 905n. In some embodiments, the integrated care 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 dialysis machine kits in the clinic. As described above, many patients receive treatment at home, such as home dialysis, and require a continuous supply of disposable medical supplies for each treatment. The delivery of supplies and / or dialysis equipment can be automatically monitored, replenished and / or inventoried by the integrated care system 220, 220' to ensure proper machine function and a steady supply of materials and resources to ensure that patients receive all prescribed treatments.

[0096] Fig.10An exemplary regulatory component 285 is shown, which may 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 may be involved in insurance and / or Medicaid and Medicare Services Center (CMS) 1005a, public product approval (e.g., Food and Drug Administration (FDA)) 1005b, and billing 1005c. The integrated care system 220, 220' can send information to each of these units and receive information from each of these units to ensure correct billing coding, regulatory approval, and / or insurance payment.

[0097] As introduced above, the care navigation unit 230, 230' can oversee and coordinate patient care based on analyses and calculations determined by the integrated care system 220, 220' based on data and information from any of the components 265, 270, 275, 280, 285 and the care coordination system 210'. For example, the care navigation unit 230' can coordinate care for a patient to track completion of intervention treatments to address functional and / or cognitive patient impairments over time, improve comorbidity management, and help drive high-value care selection and timing of treatment decisions for the patient over time. Fig.11 As shown, the care navigation unit 230, 230' can include different aspects of medical care coordination indicated by reference numerals 1105a, 1105b, ..., 1105n, including but not limited to consultation 1105a, treatment transition 1105b, scheduling 1105c, patient monitoring 1105d, tracking 1105e, transportation 1105f and / or discharge care 1105g. For example, the integrated care system 220, 220' can determine that the patient needs transportation to and from the treatment center, and can automatically arrange transportation, such as public transportation, carpooling, taxi, ride-sharing bikes, etc., so that the patient does not miss the scheduled treatment. Additionally, the integrated care system 220, 220' can send patient results to relevant care providers, such as medical specialists, doctors and / or nurses, for monitoring and / or treatment recommendations. The care navigation unit 230' can provide services to patients to address the complete medical care needs related to their kidney disease.

[0098] The care navigation unit 230, 230' may include a treatment transition 1105b to enable the integrated care system 220, 220' to coordinate patient care over the course of a kidney disease. For example, a patient may initially be diagnosed with chronic kidney disease (CKD). However, over time, without intervention therapy (e.g., a kidney transplant) or improved kidney function, the patient may develop end-stage renal disease (ESRD). As the patient's kidney disease progresses, the patient may require additional services, support, and / or medical care, which may be supervised and / or managed by the care navigation unit 230' via the integrated care system 220, 220' and through the care framework of the care coordination system 210' under the care framework 200'. According to an embodiment of the present disclosure, identifying previously unrecorded comorbidities can alert care providers to change or add to the patient's recommended course of treatment. As a result, patients can receive more effective and targeted care for their diagnosis.

[0099] Reference now Figure 3 , an integrated care system, such as integrated care system 220, 220', may include a controller 305, a processor 310, and a memory 320. The controller 305 may automatically control signals received and sent to other systems, such as additional clinical systems, external systems, and business management and billing systems. The communication between the controller 305 and the other systems may be bidirectional, whereby the system may confirm the control signal, and / or may provide information associated with the system and / or the requested operation. Additionally, the user input interface 315 and the display 302 may be configured to receive and / or display input from a user, such as a patient or a medical professional such as a doctor, nurse, technician, etc. Examples of components that may be employed within the user input interface 315 include a keypad, a button, a microphone, a touch screen, a gesture recognition device, a display screen, and a speaker. In some embodiments, the integrated care system 220, 220' may be a server, a computer, or other device for storing and processing data and controlling signals to other systems. The power supply 325 may allow the integrated care system 220, 220' to receive power, and in some embodiments may be an independent power supply.

[0100] The processor 310 can be configured to execute an operating system that can provide platform services to application software, for example, for operating the integrated care system 220, 220'. These platform services may include inter-process and network communications, file system management, and standard database operations. One or more of many operating systems can be used, and the examples are not limited to any particular operating system or operating system features. In some examples, the processor 310 can be configured to execute a real-time operating system (RTOS: Real-Time Operating System), such as RTLinux, or a non-real-time operating system, such as BSD or GNU / Linux. According to various examples, the processor 310 may be a commercially available processor, such as a processor manufactured by INTEL, AMD, MOTOROLA, and FREESCALE. However, the processor 310 may be any type of processor, multiprocessor, or controller, whether commercially available or specially manufactured. For example, according to one example, the processor 310 may include an MPC823 microprocessor manufactured by MOTOROLA.

[0101] Memory 320 may include a computer-readable and writable non-volatile data storage medium configured to store non-temporary instructions and data. Additionally, memory 320 may include a processor memory that stores data during operation of processor 310. In some examples, processor memory includes a relatively high-performance, volatile, random access memory, such as a dynamic random access memory (DRAM), a static random access memory (SRAM), or a synchronous DRAM. However, processor memory may include any device for storing data, such as a non-volatile memory, that has sufficient information throughput and storage capacity to support the functionality described herein. Furthermore, examples are not limited to a particular memory, memory system, or data storage system.

[0102] The instructions stored on the memory 320 may include an executable program or other code that can be executed by the processor 310. The instructions may be persistently stored as coded signals, and the instructions may cause the processor 310 to perform the functions described herein. The memory 320 may include information recorded on or in a medium, and the information may be processed by the processor 310 during the execution of the instructions. The memory 320 may also include, for example, data records, timing for treatment and / or operation, historical information, statistical information, and information databases for treatment. The database may be stored in the memory 320 of the integrated care system 220, 220' and may be accessed by the processor 310 and the controller 305. For example, historical data of patient information may be extracted from various databases in the integrated system 220, 220', including but not limited to patient laboratory results, treatment data, technician data during treatment (physician / nurse notes), etc. The extracted data may be used to generate a database, which may be used to train a machine learning model to assess comorbidity factors. For example, a machine learning model may identify characteristics of a patient with one or more identified comorbidities and analyze patient laboratory results, treatment data, nurse notes, etc. for commonalities that may contribute to their diagnosis (see Figure 1D ). The medium may be, for example, an optical disk, a magnetic disk, or a flash memory, etc., and may be permanently attached to or removed from the controller 305.

[0103] The integrated care system 220, 220' may include a communication link 306 so that other systems can be connected to the integrated care system 220, 220'. For example, additional clinical systems, external systems, and business management and billing systems can be connected to the integrated care system 220, 220' to send and receive data and information associated with providing patient care. In some embodiments, the communication link 306 can be wireless so that the system can be remote, or one or more of the integrated care system 220, 220' and / or systems 265, 270, 275, 280, 285, 230' can reside and operate in a cloud-based architecture.

[0104] One or more algorithms may utilize models of historical data to analyze newly input patient data as it is input and / or collected by the integrated care system 220, 220'. The algorithms may analyze the patient data based on the historical data to identify patients who may have one or more unreported and / or undocumented comorbidities. The integrated care system 220, 220' may also generate a report identifying the patient for bill verification, and / or may also send the report to the care coordination unit 230, 230' for patient follow-up and treatment recommendations. Identifying comorbid conditions may provide a more streamlined care strategy for the patient, for example, by improving the documentation of comorbid conditions in patient records, correcting billing errors, and / or utilizing comorbid conditions to provide clinical decision support for patient treatment.

[0105] The integrated care system 220, 220' may also be wirelessly connected via antenna 345 for remote communication. For example, the integrated care system 220, 220' may determine one or more patient parameters through the controller 305, the processor 310 and / or the memory 320, and may access other patient parameters stored by an external system, for example, patient parameters stored on a server or database at a location away from the system or machine or in an electronic medical record from laboratory or hospital information. It may be advantageous for the integrated care system 220, 220' to access patient parameters that may otherwise be unknown or undetermined to provide a complete care analysis for the patient. As described above, patient data may be sent to and / or accessed by the integrated care system 220, 220'. The controller 305, the processor 310 and the memory 320 may receive, store and / or determine relevant demographic and laboratory values ​​or other data for calculation.

[0106] Reference now Figure 4-5 , describes an exemplary embodiment of an operating environment of a medical care system (eg, coordinated care framework 200 , 200 ′) including an integrated care system (care analysis and guidance system) 220 , 220 ′. Figure 4 An example of an operating environment 400 that may represent some embodiments is shown. Figure 4 As shown, operating environment 400 may include system 405 operable to treat a patient, such as a patient with a chronic disease. 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 framework 200, 200', and in some embodiments, system 405 may include other systems and / or frameworks.

[0107] In some embodiments, computing device 410 may be connected to network 460 via transceiver 450. Network 460 may include nodes 462a-n, such as remote computing devices, data sources 464, and / or the like.

[0108] Processing circuitry 420 may include and / or have access to various logic for performing processing according to some embodiments. 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 provided by Fig.15 An exemplary computing architecture 1500 is provided. For example, logic, circuits, 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, an execution thread, 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, a computer code, a combination of any of the foregoing, and / or the like.

[0109] It should also be appreciated that components of processing circuitry 420 may be located within an accelerator, a processor core, an interface, a separate processor die, and / or implemented entirely as a software application, etc.

[0110] The memory unit 430 may include various types of computer-readable storage media and / or systems 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, austenite memory, phase change or ferroelectric memory, silicon oxide-nitride-oxide-silicon (SONOS) memory, magnetic card or optical card, array of devices such as redundant array of independent disks (RAID) drive, solid state memory device (e.g., USB memory, solid state drive (SSD)) and any other type of storage medium suitable for storing information. In addition, the memory unit 430 may 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 drive (HDD), magnetic floppy disk drive (FDD) and optical disk drive and / or solid state drive (SSD) that reads or writes from a removable optical disk (e.g., CD-ROM or DVD), etc.

[0111] The memory unit 430 may store various information, such as one or more programs to perform various functions for identifying and treating patients with CKD and / or ESRD. In some embodiments, the memory 430 may include logic with an application programming interface (API) and / or a graphical user interface (GUI) to read, write, and / or otherwise access information, such as via a 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 use in identifying and treating CKD and / or ESRD.

[0112] In some embodiments, the memory unit 430 may 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 may 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), and / or a clinical trial database, etc. For example, one or more programs, models, or algorithms or a combination thereof as patient information analysis 435 may be implementable. In some embodiments, a program, model, and / or algorithm may be utilized to determine a risk score, for example, to identify a patient's comorbidities.

[0113] 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, for example, a medical care exchange platform. In some embodiments, platform 505 may be used to provide an exchange of clinical data and / or clinical trial information between entities of interest. In various embodiments, platform 505 may include an application platform operable to utilize services between nodes 560a-n and 570a-n to identify patient populations and treat CKD and / or ESRD. In an exemplary embodiment, platform 505 may be a software platform, a suite and / or a protocol set, etc., provided to a client by a manufacturer and / or developer ("developer") associated with a medical device, medical care services, clinical research services, laboratory services, clinical trial services, and / or the like.

[0114] For example, a developer may provide platform 505 as a data exchange interface for use by various entities, including government entities (e.g., FDA) and other stakeholders (e.g., drug manufacturers, medical device manufacturers, and / or the like). Entities such as hospitals, dialysis clinics, medical care providers, government entities, regulatory entities, drug manufacturers, medical device manufacturers, and / or the like, that provide and / or receive clinical trial services via developer-provided nodes 570a-n may use platform 505 to implement processes in accordance with some embodiments. Other entities may access 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 memory 522. In some embodiments, at least a portion of platform 505 may be hosted in a cloud computing environment.

[0115] Nodes 570a-n may be data producers for memory 522, while nodes 560a-n may be data consumers for memory 522. For example, nodes 570a-n may include entities that provide clinical data, model information, and / or the like used by memory 522 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 results for patient populations. An entity may be both a data producer and a data consumer.

[0116] For example, node 560a may be a care 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 may provide analytical data to platform 505, for example, in the form of records in a HIMS, LIMS, EMR, and / or the like, in accordance with a license. Data consumers 560a-n may 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.

[0117] In some embodiments, platform 505 can operate according to a cloud-based model and / or an "as a service" model. In this manner, platform 505 can provide a service that operates as a single central platform that allows entities to access clinical data, model information, and / or simulation results, among other things.

[0118] In some embodiments, one of the recommended treatments and / or services may be a change or modification to the dialysis treatment prescription for the patient. In some embodiments, one or more comorbidities of the ESRD patient may be identified, which may alter the treatment, e.g., dialysis treatment. The change in treatment may be sent directly by the integrated care system 220, 220' to the selected dialysis machine or clinic for the patient's treatment. Figure 12-14As shown and described below, dialysis machines 1200, 1300, 1400, such as dialysis machines such as peritoneal dialysis machines or hemodialysis machines, can be connected to an integrated care system 220, 220' for sending and receiving dialysis information to provide appropriate care 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 care provider. In some embodiments, the peritoneal dialysis machine and / or the hemodialysis machine can be a home machine, for example, treatment can be performed in the patient's home. As described above, the integrated care 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 above-mentioned other chronic diseases and conditions.

[0119] refer to Fig.12 , a schematic diagram of an exemplary embodiment of a dialysis machine 1200 and a controller 1205 according to the present disclosure is shown. The dialysis machine 1200 may be a dialysis machine for performing dialysis treatment on a patient, for example, a peritoneal dialysis machine or a hemodialysis machine (see Figure 12-14 ). The controller 1205 can automatically control the performance of treatment functions during the course of dialysis treatment. For example, the controller 1200 can control the dialysis treatment based on information received from the care analysis and guidance system 220, 220'. The controller 1205 can be operatively connected to the sensor 1240 and transmit one or more signals to perform one or more treatment functions, or treatment processes associated with various treatment systems. Although Fig.12 The components are shown incorporated into the dialysis machine 1200, but at least one of the controller 1205, processor 1210, and memory 1220 may be configured to be externally and wired or wirelessly connected to the dialysis machine 1200 as a separate component of the dialysis system. In some embodiments, the controller 1205, processor 1210, and memory 1220 may be remote from the dialysis machine and configured to be capable of wireless communication.

[0120] In some embodiments, the controller 1205, processor 1210, and memory 1220 of the system or machine 1200, 1300, 1400 may receive a signal indicative of one or more patient parameters from the sensor 1240. Communication between the controller 1205 and the treatment system may be bidirectional, whereby the treatment system acknowledges the control signal, and / or may provide status information associated with the treatment system and / or the requested operation. For example, the system status information may include a status associated with a particular operation to be performed by the treatment system (e.g., triggering a pump for delivering dialysate, triggering a pump and / or compressor for delivering filtered blood, etc.) and a status associated with a particular operation (e.g., ready to execute, executing, completed, successfully completed, queued for execution, waiting for a control signal, etc.).

[0121] The dialysis system or machine 1200, 1300, 1400 may also include at least one pump 1250 operatively connected to the controller 1205. The controller 1205 may also be operatively connected to one or more speakers 1230 and one or more microphones 1235 disposed in the system or dialysis machine 1200, 1300, 1400. The user input interface 1215 may include a combination of hardware and software components that allow the controller 1205 to communicate with an external entity such as a patient or other user. These components may be configured to be able to receive information from measures such as body movements or gestures and verbal intonations. In an embodiment, the components of the user input interface 1215 may provide information to an external entity. Examples of components that may be used within the user input interface 1215 include keypads, buttons, microphones, touch screens, gesture recognition devices, display screens, and speakers.

[0122] like Fig.12 As shown, a sensor 1240 may be included for detecting and monitoring one or more parameters, and the sensor 1240 may be operably connected to at least the controller 1205, the processor 1210, and the memory 1220. The processor 1210 may be configured to be able to execute an operating system, and the operating system may provide platform services to the application software, for example, for operating the dialysis machine 1200. These platform services may include inter-process and network communications, file system management, and standard database operations. One or more of many operating systems may be used, and the examples are not limited to any particular operating system or operating system features. In some examples, the processor 1210 may be configured to be able 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 may be a commercially available processor, such as a processor manufactured by INTEL, AMD, MOTOROLA, and FREESCALE. However, the processor 1210 may be any type of processor, multiprocessor, or controller, whether commercially available or specially manufactured. For example, according to one example, the processor 1210 may include an MPC823 microprocessor manufactured by MOTOROLA.

[0123] Memory 1220 may include a computer-readable and writable non-volatile data storage medium configured to store non-temporary instructions and data. In addition, memory 1220 may include a processor memory that stores data during operation of processor 1210. In some examples, the processor memory includes a relatively high-performance, volatile, random access memory, such as a dynamic random access memory (DRAM), a static memory (SRAM), or a synchronous DRAM. However, the processor memory may include any device for storing data, such as a non-volatile memory, that has sufficient information throughput and storage capacity to support the functions described herein. In addition, the examples are not limited to a particular memory, memory system, or data storage system.

[0124] The instructions stored on the memory 1220 may include executable programs or other codes that can be executed by the processor 1210. The instructions may be persistently stored as coded signals, and the instructions may cause the processor 1210 to perform the functions described herein. The memory 1220 may include information recorded on or in a medium, and the information may be processed by the processor 1210 during the execution of the instructions. The memory 1220 may also include specifications for data logging, such as for user timing requirements, timing for treatment and / or operation, historical sensor information, and other databases. The medium may be, for example, an optical disk, a magnetic disk, or a flash memory, and may be permanently attached to or removed from the controller 1200.

[0125] Pressure sensors may be included for monitoring fluid pressure of the system or machine 1200, 1300, 1400, although the sensor 1240 may 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 EEG sensor, a motion sensor, an audio sensor, an accelerometer, or a capacitive sensor. It should be understood that the sensor 1240 may include sensors with varying sampling rates, including wireless sensors. Based on the data monitored by the sensor 1240, patient parameters such as heart rate and respiration rate may be determined by the controller 1200.

[0126] The controller 1205 may be disposed in the machine 1200, 1300, 1400, or may be coupled to the machine 1200, 1300, 1400 via a communication port or wireless communication link schematically shown as a communication element 1206. For example, the communication element 1206 may connect the dialysis machine 1200, 1300, 1400 to the care analysis and guidance system 220, 220' or another remote system such as an external system or other clinical system. The dialysis machine 1200, 1300, 1400 may be connected to the integrated care system 220, 220' via the communication element 1206 so that the controller 1205 may send and receive information and other signals to the care analysis and guidance system 220, 220'. As described above, the care analysis and guidance system 220, 220' may direct the prescribed dialysis treatment directly to the dialysis machine based on information received from other systems, e.g., external systems, clinical systems, to ensure that the patient receives the correct treatment. The dialysis machine may also send data and other information to the care analysis and guidance system 220, 220' so that if the dialysis treatment needs to be adjusted, the care analysis and guidance system 220, 220' can ensure that any changes will not adversely affect the patient's health.

[0127] As a component disposed within the machine 1200, 1300, 1400, the controller 1205 may be operatively connected to any one or more of the sensor 1240, the pump 1250, the pump head 1404, 1406, etc. The controller 1205 may communicate control signals or trigger voltages to the system or components of the machine 1200, 1300, 1400. As discussed, an exemplary embodiment of the controller 1205 may include a wireless communication interface. The controller 1205 may detect remote devices to determine if any remote sensors may be used to augment any sensor data used to evaluate the patient.

[0128] like Fig.12 As shown, a power source 1225 may be included, for example, to allow the machine 1200 to receive power, and in some embodiments may be an independent power source. A display 1202 may also be included. The display 1202 may function to provide information to the patient and the operator of the dialysis machine 1200. For example, the display 1202 may show information related to the dialysis treatment to be applied to the patient, including information related to the prescription. The dialysis machine 1200 may also be wirelessly connected via an antenna 1245 for remote communication.

[0129] Figure 13A-13BAn example of a peritoneal dialysis (PD) system 1301 is shown, which is configured according to an exemplary embodiment of the systems described herein. In some embodiments, the PD system 1301 can be a home PD system, for example, 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.

[0130] The dialysis machine 1300 may include a housing 1306, a door 1308, and a cassette interface including pump heads 1342, 1344 for contacting a disposable cassette or cassette 1315, wherein the cassette 1315 is located within a compartment (e.g., cavity 1305) formed between the cassette interface and the closed door 1308. The fluid line 1325 may be coupled to the cassette 1315 in a known manner, such as via a connector, and may also include a valve for controlling the flow of fluid into and out of a fluid bag containing fresh dialysate and warming fluid. In another embodiment, at least a portion of the fluid line 1325 may be integral with the cassette 1315. Prior to an operation, a user may open the door 1308 to insert a new cassette 1315, and remove a used cassette 1315 after an operation.

[0131] The cassette 1315 can be placed in the cavity 1305 of the machine 1300 for operation. During operation, dialysis fluid can flow into the patient's abdomen via the cassette 1315, and spent dialysate, waste, and / or excess fluid can be removed from the patient's abdomen via the cassette 1315. The door 1308 can be securely closed onto the machine 1300. Peritoneal dialysis for a patient can include a total treatment of approximately 10 to 30 liters of fluid, wherein approximately 2 liters of dialysis fluid is pumped into the patient's abdomen, retained 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 full treatment volume is reached, and typically occurs at night while the patient is asleep.

[0132] A heater tray 1316 can be positioned on top of the housing 1306. The heater tray 1316 can be any size and shape to accommodate a bag of dialysate (e.g., a 5L dialysate bag) for batch heating. The dialysis machine 1300 can also include a user interface, such as a touch screen 1318 and a control panel 1320 that can be operated by a user (e.g., a caregiver or patient) to allow, for example, setting up, starting and / or terminating a dialysis treatment. In some embodiments, the heater tray 1316 can include a heating element 1335 for heating the dialysate before it is delivered to the patient.

[0133] The dialysate bag 1322 can be hung from a hook on the side of the cart 1334, and the heater bag 1324 can be positioned in the heater tray 1316. Hanging the dialysate bag 1322 can improve air management because the air contents can drain to the top portion of the dialysate bag 1322 by gravity. Fig. 13B 1, but any number "n" of dialysate bags may be connected to the dialysis machine 1300 (e.g., 1 to 5 bags, or more), and the reference to the first and second bags does not limit the total number of bags used in the dialysis system 1301. For example, the dialysis machine may have dialysate bags 1322a, ..., 1322n that may be connected in the system 1301. In some embodiments, connectors and tube ports may connect the dialysate bags 1322 and lines for transferring dialysate. The dialysate from the dialysate bag 1322 may be transferred to the heating bag 1324 in batches. For example, a batch of dialysate may be transferred from the dialysate bag 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 may flow into the patient. The dialysate bag 1322 and the heater bag 1324 may be connected to the cassette 1315 via a dialysate bag line or tube 1325 and a heater bag line or tube 1328, respectively. The dialysate bag line 1325 may be used to transfer dialysate from the dialysate bag 1322 to the cassette during use, and the heater bag line 1328 may be used to transfer dialysate back and forth between the cassette and the heater bag 1324 during use. In addition, a patient line 1336 and a drain line 1332 may be connected to the cassette 1315. The patient line 1336 may be connected to the abdomen of the patient via a catheter, and may be used to transfer dialysate back and forth between the cassette and the peritoneal cavity of the patient through the pump heads 1342, 1344 during use. The drain line 1332 may be connected to a drain system or drain container, and may be used to transfer dialysate from the cassette to a drain system or drain container during use.

[0134] Although in some embodiments, the dialysate can be heated in batches as described above, in other embodiments, the dialysis machine can heat the dialysate by online heating, for example, continuously flowing the dialysate through a warmer bag located between heating elements before the dialysate is delivered 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 a tube (e.g., tube 1325) or a fluid line via a box. The tube can be connectable so that the dialysate can flow from the dialysate bag through the warmer bag for heating and flow to the patient.

[0135] In such an in-line heating embodiment, the warmer bag can be configured so that the dialysate can flow continuously through the warmer bag (rather than being transferred in batches for batch heating) to reach a predetermined temperature before flowing into the patient. For example, in some embodiments, the dialysate can flow continuously through the warmer bag at a rate of between about 100-300 mL / min. An internal heating element (not shown) can be located above and / or below the opening so that when the warmer bag is inserted into the opening, 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 alternatively be a portion of a tube in the system that is passed through, surrounded by, or otherwise configured relative to the heating element by the heating element.

[0136] The touch screen 1318 and control panel 1320 may allow an operator to input various treatment parameters to the dialysis machine 1300 and otherwise control the dialysis machine 1300. In addition, the touch screen 1318 may function as a display. The touch screen 1318 may function to provide information to the patient and the operator of the dialysis system 1301. For example, the touch screen 1318 may display information related to the dialysis treatment to be applied to the patient, including information related to the prescription.

[0137] The dialysis machine 1300 may include a processing module 1302 inside the dialysis machine 1300, the processing module 1302 being configured to communicate with the touch screen 1318 and the control panel 1320. The processing module 1302 may be configured to receive data from the touch screen 1318, the control panel 1320, and sensors, such as weight, air, flow, temperature, and / or pressure sensors, and control the dialysis machine 1300 based on the received data. For example, the processing module 1302 may adjust operating parameters of the dialysis machine 1300.

[0138] The dialysis machine 1300 may be configured to be connected to a network 1303. The connection to the network 1303 may be via a wired and / or wireless connection. The dialysis machine 1300 may include a connection component 1304 configured to facilitate connection to the network 1303. The connection component 1304 may be a transceiver for wireless connection and / or other signal processor for processing signals sent and received via a wired connection. Other medical devices (e.g., other dialysis machines) or components may be configured to be connected to the network 1303 and communicate with the dialysis machine 1300.

[0139] The user interface portion, such as the touch screen 1318 and / or the display 1320, may include one or more buttons for selecting and / or inputting user information. The touch screen 1318 and / or the control panel 1320 may be operatively connected to a controller (not shown) and disposed in the machine 1300 for receiving and processing inputs for operating the dialysis machine 1300.

[0140] In some embodiments, the machine 1200, 1300, 1400 may - alternatively or simultaneously or in conjunction with sending information to the integrated care system 220, 220' - wirelessly transmit (e.g., via a wireless Internet connection) information or alerts to a remote location, including but not limited to a doctor's office, hospital, call center, and technical support. For example, the machine 1200, 1300, 1400 may provide real-time remote monitoring of machine operation and patient parameters. The memory 1220 of the machine 1200 may store data, or the machine 1200, 1300, 1400 may transmit data to a local or remote server at predetermined intervals.

[0141] Fig.14 A schematic diagram of an 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 passes through the dialyzer 1404, a reservoir 1406 can receive the used dialysate via a tube 1405. The hemodialysis operation can filter particles and / or contaminants from the patient's blood through a patient-external filtration device, such as a dialyzer 1404. Since the dialysate passes through the dialyzer 1404, unfiltered patient 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 can be monitored via a sensor 1418, and venous pressure can be monitored via a pressure sensor 1414. An air trap and detector 1416 can ensure that air is not introduced into the patient's blood when it is filtered and returned to the patient 1401. The flow of blood and dialysate is controlled via respective pumps, including blood pump 1412 and fluid pump 1420. Heparin 1422, a blood thinner, may be used in conjunction with saline 1424 to ensure that blood clots do not form or block blood flow through the system.

[0142] In some embodiments, the dialysis system 1400 may include a controller 1450, which may be similar to the controller 1205 described above with respect to the dialysis machine 1200. The controller 1450 may be configured to monitor fluid pressure readings to identify fluctuations in patient parameters such as heart rate and / or respiratory rate. In some embodiments, the patient's heart rate and / or respiratory rate may be determined by the fluid pressure in the fluid flow lines and fluid bags. The controller 1450 may also be operably connected to and / or communicate with additional sensors or sensor systems, although the controller 1450 may use any available data about the patient's biological functions or other patient parameters. For example, the machine 1200, 1300, 1400 may send patient data to the integrated care system 220, 220' for use in calculating a risk score for a patient with one or more comorbidities.

[0143] Fig.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 may include or be implemented as part of an electronic device. In some embodiments, the computing architecture 1500 may represent, for example, a computing device 410 and / or a platform 505 and / or a component of an integrated care system 220, 220'. The embodiments are not limited in this context.

[0144] As used in this application, the terms "system" and "component" and "module" mean an entity related to a computer, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 1500. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. As an illustration, both an application running on a server and a server can be a component. One or more components can be within a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, components can be communicatively coupled to each other through various types of communication media to coordinate operations. Coordination may involve unidirectional or bidirectional information exchange. For example, a component can communicate information in the form of a signal transmitted on a communication medium. The information can be implemented as a signal assigned to various signal lines. In such an assignment, each message is a signal. However, other embodiments may use data messages instead. Such data messages can be sent through various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0145] The computing architecture 1500 includes various common computing elements such as one or more processors, multi-core processors, coprocessors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, etc. However, the embodiments are not limited to being implemented by the computing architecture 1500.

[0146] like Fig.15 As shown, computing architecture 1500 includes a processing unit 1504, a system memory 1506, and a system bus 1508. Processing unit 1504 can be any of a variety of commercially available processors, including but not limited to and processor; Application, embedded and security processors; and and Processors; IBM and Cell processor; Core(2) and Dual microprocessors, multi-core processors, and other multi-processor architectures may also be used as the processing unit 1504 .

[0147] The system bus 1508 provides an interface to the processing unit 1504 for system components including, but not limited to, the system memory 1506. The system bus 1508 may be any of a variety of types of bus structures that may be further interconnected to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters may be connected to the system bus 1508 via a slot architecture. Example slot architectures may include, but are not limited to, 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), and the like.

[0148] The system memory 1506 may include various types of computer-readable storage media in the form of one or more higher speed memory cells, 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, austenite memory, phase change or ferroelectric memory, silicon oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, arrays of devices such as redundant array of independent disks (RAID) drives, solid-state memory devices (e.g., USB memory, solid-state drive (SSD), and any other type of storage medium suitable for storing information. Fig.15 In the illustrative embodiment shown in FIG. 1 , system memory 1506 may include nonvolatile memory 1510 and / or volatile memory 1512. A basic input / output system (BIOS) may be stored in nonvolatile memory 1510.

[0149] 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 via a HDD interface 1524, an FDD interface 1526, and an optical drive interface 1528, respectively. The HDD interface 1524 for external drive implementations may include at least one or both of a Universal Serial Bus (USB) and IEEE 884 interface technologies.

[0150] 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.

[0151] A user may enter commands and information into the computer 1502 through one or more wired / wireless input devices, such as a keyboard 1528 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, a sensor, a stylus, etc. 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, etc.

[0152] 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 802. In addition to the monitor 1544, computers typically include other peripheral output devices, such as speakers, printers, and the like.

[0153] The computer 1502 can operate in a network environment using logical connections via wired and / or wireless communications to one or more remote computers, such as remote computer 1548. The remote computer 1548 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device or other public network node, and typically includes many or all of the elements described relative to the computer 1502, but only a memory / storage device 1550 is shown for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 1552 and / or a larger network, such as a wide area network (WAN) 1554. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global communications network, such as the Internet.

[0154] 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.

[0155] When used in a WAN networking environment, the computer 1502 may include a modem 1558, or be connected to a communications server on the WAN 1554, or have other means for establishing communications over the WAN 1554, such as over the Internet. The modem 1558, which may be internal or external and may be 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, may be stored in the remote memory / storage device 1550. It should be appreciated that the network connections shown are exemplary and other methods of establishing a communications link between the computers may be used.

[0156] The computer 1502 is operable to communicate with wired and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively arranged in wireless communications (e.g., IEEE 802.16 over-the-air modulation techniques). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth. TM Wireless technology, etc. Therefore, the communication can be a predefined structure like a traditional network, or just a simple ad hoc communication between at least two devices. Wi-Fi networks use radio technology called IEEE802.11x (a, b, g, n, etc.) to provide secure, reliable, and fast wireless connections. Wi-Fi networks can be used to connect computers to each other, to the Internet, and to wired networks (which use media and functions related to IEEE 802.3).

[0157] Some embodiments of the disclosed system may be implemented, for example, using a storage medium, a computer-readable medium, or an article of manufacture, which may store instructions or instruction sets that, if executed by a machine (i.e., a processor or microcontroller), may cause the machine to perform methods and / or operations according to embodiments of the present disclosure. In addition, a server or database server may include a machine-readable medium configured to store machine-executable program instructions. Such a machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, etc., and may be implemented using any suitable combination of hardware, software, firmware, or a combination thereof, and may be used for a system, subsystem, component, or subcomponent. The computer-readable medium or article may 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, such as memory (including non-transitory memory), removable or non-removable media, erasable or non-erasable media, writable or rewritable media, digital or analog media, hard disk, floppy disk, compact disk read-only memory (CD-ROM), compact disk recordable (CD-R), compact disk rewritable (CD-RW), optical disk, magnetic media, magneto-optical media, removable memory cards or disks, various types of digital versatile disks (DVDs), magnetic tapes, cassettes, etc. The instructions may include any suitable type of code implemented using any suitable high-level, low-level, object-oriented, visual, compiled and / or interpreted programming language, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, etc.

[0158] Many specific details have been set forth herein to provide a thorough understanding of the embodiments. However, those skilled in the art will appreciate that the embodiments may be practiced without these specific details. In other cases, well-known operations, components, and circuits are not described in detail to avoid confusing the embodiments. It is understood that the specific structural and functional details disclosed herein may be representative and do not necessarily limit the scope of the embodiments.

[0159] Some embodiments may be described using the expressions "coupled" and "connected" and their derivatives. These terms are not intended to be synonymous with each other. For example, some embodiments may 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. However, the term "coupled" may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0160] Unless expressly stated otherwise, it is understood that terms such as "processing", "computing", "calculating", "determining", etc. refer to measures and / or processes of a computer or computing system or similar electronic computing device that manipulate and / or transform data represented as physical quantities (e.g., electronic) in registers and / or memories of the computing system into other data similarly represented as physical quantities within the memory, registers, or other such information storage, transmission, or display device of the computing system. The embodiments are not limited in this context.

[0161] It should be noted that the methods described herein do not have to be executed in the order described or in any particular order. In addition, various activities described with respect to the methods identified herein can be executed in serial or parallel manner.

[0162] Although specific embodiments have been shown and described herein, it should be understood that any arrangement calculated to achieve the same purpose may replace the specific embodiments shown. The present disclosure is intended to cover any and all modifications or variations of the various embodiments. It should be understood that the above description is made in an illustrative manner, rather than restrictive. In reviewing the above description, the combination of the above embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art. Therefore, the scope of the various embodiments includes any other application in which the above compositions, structures and methods are used.

[0163] Although the subject matter has been described in language specific to structural features and / or methodological measures, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or measures described above. Rather, the specific features and measures described above are disclosed as example forms of implementing the claims.

[0164] As used herein, an element or operation described in the singular and prefixed with the word "one" or "an" should be understood to not exclude plural elements or operations, unless such exclusion is explicitly described. In addition, reference to "one embodiment" of the present disclosure is not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the described features.

[0165] To the extent used in this specification and in the claims, recitations in the general form of "at least one of [a] and [b]" should be interpreted as disjunctive. For example, recitations 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].

[0166] 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 accompanying 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 a specific implementation 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 an undocumented comorbid condition of a patient, the system comprising: An integrated care system that is configured to: extracting population patient data from one or more databases corresponding to a pool of patients receiving dialysis treatment; accessing a plurality of machine learning comorbidity models, each machine learning comorbidity model configured to generate a comorbidity risk score for one of a plurality of comorbidity conditions, wherein the plurality of machine learning comorbidity models are trained using the extracted population patient data but excluding patient data of patients who have not been diagnosed with a particular comorbidity associated with the corresponding machine learning comorbidity model; extracting patient data corresponding to the patient from a patient record; At least one reported comorbidity was identified for the patient from the patient record; generating a comorbidity risk score for each of a plurality of comorbidities using the plurality of machine learning comorbidity models using the extracted patient data; aggregating comorbidity risk scores generated by the plurality of machine learning comorbidity models; identifying at least one comorbidity among the plurality of comorbidities based on an aggregate comorbidity risk score and a comorbidity risk score being above a predetermined threshold, the at least one comorbidity not being one of the at least one reported comorbidity; and The at least one comorbidity is determined to be an overlooked, unreported, and / or unrecorded comorbidity.

2. The system according to claim 1, wherein: The integrated care system is configured to: be capable of performing a review of patient records based on determining at least one comorbidity as an overlooked, unreported and / or unrecorded comorbidity, wherein the review of patient records includes reviewing patient records to make billing corrections, patient data corrections, provide clinical interventions, change a patient's treatment plan, or a combination thereof.

3. The system according to claim 1, wherein: Multiple machine learning comorbidity models were used to generate comorbidity risk scores including: using two or more separate machine learning comorbidity models to generate separate comorbidity risk scores associated with separate conditions; and The individual comorbidity risk scores were input into the aggregated comorbidity model for calculation of the comorbidity risk score.

4. The system according to claim 3, wherein: The two or more separate machine learning comorbidity models are selected from two or more of the following models: a gastrointestinal bleeding model, a pericarditis model, a myelodysplasia model, a hemolytic anemia model, a sickle cell anemia model, or a clustered comorbidity model, or a combination thereof.

5. The system according to claim 1, wherein: The integrated care system is configured to: generating a report including at least a portion of the identified subset of the patient pool and their corresponding comorbidity risk scores; and The report is transmitted to one or more medical care facilities.

6. The system according to claim 1, wherein: The integrated care system is configured to: accessing an aggregated comorbidity model configured to receive as data input an output of each of the plurality of machine learning comorbidity models; Each comorbidity risk score of the plurality of machine learning comorbidity models is input into an aggregated comorbidity model to calculate an overall comorbidity patient risk score for patients having any comorbidity condition associated with the plurality of machine learning comorbidity models.

7. The system according to claim 1, wherein: The predetermined threshold is a predetermined threshold percentage, which is equal to or greater than 50%.

8. The system according to claim 1, wherein: The extracted patient data includes laboratory values, hospitalization history, hospitalization notes, pharmacy prescription history, other documented comorbid conditions, patient demographic information, healthcare provider notes, or keywords, or a combination thereof.

9. The system according to claim 1, wherein: The extracted patient data includes laboratory values ​​including hemoglobin level, PT, platelet count, transferrin saturation, or calcium level, or a combination thereof.

10. The system according to claim 1, wherein: The integrated care system is configured to enable transmission of updated patient information to one or more medical care organizations to inform the one or more medical care organizations of the patient's neglected, unreported, and / or unrecorded co-morbid conditions.

11. A method for identifying an undocumented co-morbid condition in a patient, the method comprising: extracting population patient data from one or more databases corresponding to a pool of patients receiving dialysis treatment; accessing a plurality of machine learning comorbidity models, each machine learning comorbidity model configured to generate a comorbidity risk score for one of a plurality of comorbidity conditions, wherein the plurality of machine learning comorbidity models are trained using the extracted population patient data but excluding patient data of patients who have not been diagnosed with a particular comorbidity associated with the corresponding machine learning comorbidity model; extracting patient data corresponding to the patient from a patient record; At least one reported comorbidity was identified for the patient from the patient record; generating a comorbidity risk score for each of a plurality of comorbidities using the plurality of machine learning comorbidity models using the extracted patient data; aggregating comorbidity risk scores generated by the plurality of machine learning comorbidity models; identifying at least one comorbidity among the plurality of comorbidities based on an aggregate comorbidity risk score and a comorbidity risk score being above a predetermined threshold, the at least one comorbidity not being one of the at least one reported comorbidity; and The at least one comorbidity is determined to be an overlooked, unreported, and / or unrecorded comorbidity.

12. The method according to claim 11, wherein: The method also includes performing a review of patient records based on determining at least one comorbidity as an overlooked, unreported, and / or undocumented comorbidity, wherein the review of patient records includes reviewing patient records to make billing corrections, patient data corrections, provide clinical interventions, change a patient's treatment plan, or a combination thereof.

13. The method according to claim 11, wherein: Multiple machine learning comorbidity models were used to generate comorbidity risk scores including: using two or more separate machine learning comorbidity models to generate separate comorbidity risk scores associated with separate conditions; and The individual comorbidity risk scores were input into the aggregated comorbidity model for calculation of the comorbidity risk score.

14. The method according to claim 11, wherein: The two or more separate machine learning comorbidity models are selected from two or more of the following models: a gastrointestinal bleeding model, a pericarditis model, a myelodysplasia model, a hemolytic anemia model, a sickle cell anemia model, or a clustered comorbidity model, or a combination thereof.

15. The method according to claim 11, wherein: The method further comprises: generating a report including at least a portion of the identified subset of the patient pool and their corresponding comorbidity risk scores; and The report is transmitted to one or more medical care facilities.

16. The method according to claim 11, wherein: The predetermined threshold is a predetermined threshold percentage, and the predetermined threshold percentage is equal to or greater than 50%.

17. The method according to claim 11, wherein: The extracted patient data includes laboratory values, hospitalization history, hospitalization notes, pharmacy prescription history, other documented comorbid conditions, patient demographic information, healthcare provider notes, or keywords, or a combination thereof.

18. The method according to claim 17, wherein: The extracted patient data includes laboratory values ​​including hemoglobin level, PT, platelet count, transferrin saturation, or calcium level, or a combination thereof.

19. The method according to claim 18, wherein: The laboratory value includes a standard deviation, a time-weighted average, a maximum value, a minimum value, a trend value, or a data peak, or a combination thereof.

20. The method according to claim 11, wherein: The method also includes transmitting updated patient information to one or more medical care organizations to inform the one or more medical care organizations of the patient's neglected, unreported, and / or undocumented co-morbid conditions.

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