Deep analysis and optimization guidance method and system based on dental clinic operation data

Abstract

The application provides a deep analysis and optimization guidance method and system based on dental clinic operation data. The method comprises the following steps: obtaining the operation original data of the target dental clinic, performing data standardization and cleaning processing to obtain a clinic operation standardized data set, extracting quantity dimension feature data, rate dimension feature data and price dimension feature data, respectively processing to obtain quantity dimension score, rate dimension score and price dimension score, processing according to the quantity dimension score, rate dimension score and price dimension score to obtain an operation diagnosis comprehensive score, further processing to obtain an operation problem priority level, and matching a corresponding individualized optimization guidance scheme, collecting tracking data after the execution of the individualized optimization guidance scheme according to a preset monitoring period, extracting optimization effect index data, processing to obtain an optimization execution effect evaluation coefficient, and accordingly triggering a model iteration optimization process, so as to realize the deep analysis and optimization guidance technology based on the dental clinic operation data.

Description

Technical Field

[0001] This application relates to the fields of data analysis and business management technology, and more specifically, to methods and systems for in-depth analysis and optimization guidance based on dental clinic business data. Background Technology

[0002] With the rapid expansion of the oral healthcare market and the continuous growth in the number of dental clinics, industry competition is becoming increasingly fierce. Clinic managers not only face the pressure of acquiring patient traffic, but also need to deal with multi-dimensional operational challenges such as low service conversion efficiency, unbalanced customer spending structure, and insufficient resource utilization. Against this backdrop, how to conduct systematic and in-depth analysis of clinic operation data and form precise optimization guidance has become a key issue in enhancing the core competitiveness of dental clinics.

[0003] Currently, dental clinics' operational data is typically scattered across multiple data sources, including billing systems, appointment systems, HIS systems, and channel promotion platforms. This results in inconsistent data definitions, high missing rates, and low standardization, making it difficult for managers to obtain a true and reliable overall operational view. Existing operational analysis methods largely rely on manual report statistics or single-dimensional indicator evaluations, lacking systematic modeling of the multi-dimensional linkage between "patient traffic (volume) - service conversion (rate) - pricing structure (price)". This makes it impossible to effectively identify the core shortcomings affecting operational performance or to output targeted optimization action plans.

[0004] In addition, existing solutions generally lack a dynamic tracking and feedback mechanism for the implementation effect of optimization solutions, resulting in a disconnect between business diagnosis conclusions and actual improvement results. Optimization suggestions are difficult to form a closed-loop iteration, the system's adaptive capability is insufficient, and outpatient managers often have to rely on experience to make judgments, making it impossible to achieve data-driven continuous improvement.

[0005] Therefore, existing technologies have significant shortcomings in areas such as multi-source integration of dental clinic operational data, multi-dimensional quantitative analysis, personalized optimization guidance, and closed-loop effect verification.

[0006] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for in-depth analysis and optimization guidance based on dental clinic operation data. This method involves acquiring raw operation data from a target dental clinic, performing data standardization and cleaning to obtain a standardized clinic operation dataset, extracting quantitative dimension feature data from this dataset to obtain quantitative dimension scores, extracting rate dimension feature data from the same dataset to obtain rate dimension scores, extracting price dimension feature data from the same dataset to obtain price dimension scores, and processing the quantitative, rate, and price dimension scores to obtain a comprehensive operational diagnosis score. Further processing yields the priority level of operational issues, and corresponding personalized optimization guidance plans are matched. Tracking data after the implementation of the personalized optimization guidance plans is collected according to a preset monitoring cycle, optimization effect index data is extracted, and optimization execution effect evaluation coefficients are obtained. This triggers a corresponding model iteration optimization process, thus realizing a technology for in-depth analysis and optimization guidance based on dental clinic operation data.

[0008] This application also provides a method for in-depth analysis and optimization guidance based on dental clinic operation data, including the following steps: Obtain the raw operational data of the target dental clinic, perform data standardization and cleaning, and obtain a standardized dataset of clinic operations. Based on the standardized outpatient operation dataset, quantitative dimension feature data is extracted and processed to obtain quantitative dimension scores. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. Based on the standardized outpatient operation dataset, price dimension feature data is extracted and processed to obtain price dimension scores. The scores are processed based on the quantity dimension, rate dimension, and price dimension to obtain a comprehensive business diagnosis score. Further processing is then used to determine the priority level of business issues, and corresponding personalized optimization guidance plans are matched accordingly. Based on the preset monitoring cycle, track data after the implementation of the personalized optimization guidance plan is collected, optimization effect index data is extracted, the optimization execution effect evaluation coefficient is obtained, and the model iterative optimization process is triggered accordingly.

[0009] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operating data described in this application, the step of obtaining the original operating data of the target dental clinic, performing data standardization and cleaning processing, and obtaining a standardized dataset of clinic operations includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-charge amounts. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

[0010] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operation data described in this application, the step of extracting quantitative dimension feature data and processing it to obtain quantitative dimension scores based on the standardized dataset of clinic operations includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of channel traffic structure, and the average number of patients seen by doctors per day are extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

[0011] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operation data described in this application, the step of extracting rate dimension feature data and processing the acquisition rate dimension score according to the standardized dataset of clinic operations includes: Based on the standardized outpatient operation dataset, the following data were extracted: initial visit transaction volume, initial visit volume, follow-up visit transaction volume, follow-up visit volume, repeat purchase volume of returning customers, and total number of returning customers. The initial consultation conversion rate is calculated by comparing the initial consultation sales volume with the initial consultation store visit volume. The repeat visit conversion rate is calculated by comparing the repeat visit sales volume with the repeat visit in-store volume. The repeat purchase rate of existing customers is calculated by comparing the ratio of the repeat purchase volume of existing customers to the total number of existing customers. The deviations of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers are calculated by comparing them with the preset industry benchmark conversion rate set to obtain the deviation values ​​of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers. The initial consultation conversion rate deviation, follow-up consultation conversion rate deviation, and repeat customer repurchase rate deviation are weighted and calculated to obtain a rate dimension score.

[0012] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operation data described in this application, the step of extracting price dimension feature data and processing it to obtain a price dimension score based on the standardized dataset of clinic operations includes: Extract total revenue, total number of outpatient visits, revenue from high-value items, and total cost of consumables from the standardized outpatient operation dataset. The overall average order value is calculated by comparing the total revenue with the total number of outpatient visits. The revenue share of high-value projects is calculated by comparing the revenue of high-value projects with the total revenue. The deviation rate between the overall average order value and the median of the preset industry average order value benchmark range is calculated to obtain the average order value deviation rate. The price dimension score is obtained by weighting the deviation rate of the average order value, the revenue ratio of high-value items, and the preset benchmark value of the high-value item ratio.

[0013] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operating data described in this application, the step of processing data according to quantity dimension scoring, rate dimension scoring, and price dimension scoring to obtain a comprehensive operating diagnosis score, further processing to obtain the priority level of operating problems, and matching corresponding personalized optimization guidance plans includes: Based on the quantitative dimension score, the rate dimension score, and the price dimension score, a weighted summation is performed using a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive business diagnosis score. Obtain a preset set of business diagnostic level thresholds, including a first preset business diagnostic level threshold and a second preset business diagnostic level threshold, wherein the first preset business diagnostic level threshold is less than the second preset business diagnostic level threshold; If the overall business diagnosis score is less than or equal to the first preset business diagnosis level threshold, the business problem priority level is first-level priority. If the overall business diagnosis score is greater than the first preset business diagnosis level threshold and less than the second preset business diagnosis level threshold, then the business problem priority level is level two priority. If the overall business diagnosis score is greater than or equal to the second preset business diagnosis level threshold, the priority level of the business problem is level three. Based on the priority level of operational issues, a corresponding personalized optimization guidance plan is matched using a preset optimization plan rule base.

[0014] Optionally, in the method for in-depth analysis and optimization guidance based on dental clinic operating data described in this application, the step of collecting tracking data after the implementation of the personalized optimization guidance plan according to a preset monitoring cycle, extracting optimization effect index data, processing to obtain the optimization implementation effect evaluation coefficient, and triggering the model iterative optimization process accordingly includes: Based on the outpatient operation data collected after the implementation of the preset monitoring cycle, the optimization performance indicators are extracted, including the change rate of store visits, the change rate of overall conversion rate, and the change rate of overall average transaction value. The optimization performance evaluation coefficient is obtained by weighting the changes in store traffic, overall conversion rate, and average order value. The evaluation coefficient for optimized execution effect is compared with the preset threshold for achieving the desired effect; If the evaluation coefficient of the optimized execution effect is less than the preset effect threshold, the execution data of this scheme will be stored in the preset optimization effect sample library, and the parameters of the preset optimization scheme rule library will be updated according to the preset machine learning model to obtain the iterative optimization scheme rule library.

[0015] Secondly, this application provides a deep analysis and optimization guidance system based on dental clinic operation data. The system includes a memory and a processor. The memory includes a program for a deep analysis and optimization guidance method based on dental clinic operation data. When the program for the deep analysis and optimization guidance method based on dental clinic operation data is executed by the processor, it performs the following steps: Obtain the raw operational data of the target dental clinic, perform data standardization and cleaning, and obtain a standardized dataset of clinic operations. Based on the standardized outpatient operation dataset, quantitative dimension feature data is extracted and processed to obtain quantitative dimension scores. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. Based on the standardized outpatient operation dataset, price dimension feature data is extracted and processed to obtain price dimension scores. The scores are processed based on the quantity dimension, rate dimension, and price dimension to obtain a comprehensive business diagnosis score. Further processing is then used to determine the priority level of business issues, and corresponding personalized optimization guidance plans are matched accordingly. Based on the preset monitoring cycle, track data after the implementation of the personalized optimization guidance plan is collected, optimization effect index data is extracted, the optimization execution effect evaluation coefficient is obtained, and the model iterative optimization process is triggered accordingly.

[0016] Optionally, in the deep analysis and optimization guidance system based on dental clinic operation data described in this application, the step of obtaining the original operation data of the target dental clinic, performing data standardization and cleaning processing, and obtaining a standardized dataset of clinic operation data includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-transaction fees. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

[0017] Optionally, in the deep analysis and optimization guidance system based on dental clinic operation data described in this application, the step of extracting quantitative dimension feature data and processing it to obtain quantitative dimension scores based on the standardized dataset of clinic operations includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of traffic structure from different channels, and the average number of patients seen by doctors per day were extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

[0018] As can be seen from the above, the method and system for in-depth analysis and optimization guidance based on dental clinic operation data provided in this application obtains the original operation data of the target dental clinic, performs data standardization and cleaning to obtain a standardized dataset of clinic operation data, extracts quantitative dimension feature data from the standardized dataset of clinic operation data, processes it to obtain quantitative dimension scores, extracts rate dimension feature data from the standardized dataset of clinic operation data, processes it to obtain rate dimension scores, extracts price dimension feature data from the standardized dataset of clinic operation data, processes it to obtain price dimension scores, processes the quantitative dimension scores, rate dimension scores, and price dimension scores to obtain a comprehensive operational diagnosis score, further processes it to obtain the priority level of operational issues, matches corresponding personalized optimization guidance plans, collects tracking data after the implementation of personalized optimization guidance plans according to a preset monitoring cycle, extracts optimization effect index data, processes it to obtain optimization execution effect evaluation coefficients, and triggers the model iterative optimization process accordingly, thereby realizing the technology of in-depth analysis and optimization guidance based on dental clinic operation data.

[0019] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the method for in-depth analysis and optimization guidance based on dental clinic operating data provided in this application embodiment; Figure 2 A flowchart illustrating the method for obtaining the priority level of operational issues in the in-depth analysis and optimization guidance method based on dental clinic operational data provided in this application embodiment; Figure 3 This is a schematic diagram of the architecture of the method for in-depth analysis and optimization guidance based on dental clinic operation data provided in the embodiments of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for in-depth analysis and optimization guidance based on dental clinic operating data, as described in some embodiments of this application. This method is used in terminal devices, such as computers and mobile terminals. The method includes the following steps: S11. Obtain the original operational data of the target dental clinic, perform data standardization and cleaning processing, and obtain a standardized dataset of clinic operations. S12. Based on the standardized outpatient operation dataset, extract the quantitative dimension feature data and process it to obtain the quantitative dimension score. S13. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. S14. Based on the standardized outpatient operation dataset, extract the price dimension feature data and process it to obtain the price dimension score; S15. Based on the quantitative dimension score, the rate dimension score, and the price dimension score, a comprehensive business diagnosis score is obtained. Further processing is performed to obtain the priority level of business issues, and corresponding personalized optimization guidance solutions are matched. S16. Collect tracking data after the implementation of the personalized optimization guidance plan according to the preset monitoring cycle, extract optimization effect index data, process to obtain the optimization execution effect evaluation coefficient, and trigger the model iteration optimization process accordingly.

[0025] It is important to note that the process involves acquiring the target dental clinic's raw operational data, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel acquisition data. This data is then standardized and cleaned using a pre-defined data cleaning rule base to obtain a standardized clinic operational dataset. Based on this dataset, volume-dimensional feature data is extracted, including physician capacity utilization, channel structure balance, and the proportion of initial consultations, and a volume-dimensional score is generated. Similarly, rate-dimensional feature data is extracted from the standardized clinic operational dataset, including deviations in initial consultation conversion rates, follow-up consultation conversion rates, and repeat purchase rates of existing customers, and a rate-dimensional score is generated. Finally, based on the standardized clinic operational dataset, [the following data is extracted / reproduced / extracted / reproduced / recycled]. The system collects price-related feature data, including the deviation rate of average order value, the revenue share of high-value items, and a preset benchmark value for the proportion of high-value items. This data is then processed to obtain a price-related score. Further processing based on the volume-related, rate-related, and price-related scores yields a comprehensive operational diagnostic score. This score is then further processed to determine the priority level of operational issues and to match corresponding personalized optimization guidance plans. Tracking data after the implementation of these personalized optimization guidance plans is collected according to a preset monitoring cycle. Optimization effect indicators, including the rate of change in store visits, the rate of change in overall conversion rate, and the rate of change in overall average order value, are extracted. These are then processed to obtain an evaluation coefficient for the optimization execution effect, triggering a corresponding model iteration optimization process. This enables in-depth analysis and optimization guidance based on dental clinic operational data.

[0026] According to an embodiment of the present invention, the step of obtaining the original operational data of the target dental clinic, performing data standardization and cleaning processing, and obtaining a standardized dataset of clinic operations includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-charge amounts. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

[0027] It is important to note that the raw operational data of the target dental clinic was obtained. This raw operational data comes from multiple business subsystems in the clinic's daily operations, specifically including treatment business data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. Treatment business data includes patient visit records, treatment item categories and quantities, attending physician information, and visit time distribution data. Among these, patient visit records are used to reconstruct the clinic's actual patient volume and frequency; treatment item data reflects the clinic's service structure and item combination; attending physician information is used to assess each physician's capacity utilization and efficiency; and visit time distribution data... This data is used to analyze the rationality of peak-hour scheduling and vacancy rates; consumable cost data includes consumable types, usage, procurement costs, and loss data; by aggregating consumable consumption records for each treatment item and combining the procurement unit price with the loss ratio, the actual marginal cost of a single item can be calculated; appointment traffic data includes channel source tags and appointment attendance rate; channel source tags are used to identify whether patients reach the clinic through different channels such as online platforms, search engines, social media channels, referrals, or offline promotions; the appointment attendance rate reflects the quality of traffic acquisition and appointment conversion efficiency of each channel; revenue data includes project categories and single-session fees, with project categories categorized as orthodontics, implants, and prosthodontics. The system categorizes core services such as teeth cleaning and pediatric dentistry. Combining the single-visit fee with the service category allows for calculation of the average transaction value and the revenue share of high-value services. Customer profile data includes initial and follow-up visit indicators and customer spending history. Initial and follow-up visit indicators differentiate between new and existing customers, supporting separate statistics for initial and follow-up visit conversion rates. Customer spending history records the time intervals between each visit and the amount spent, used to calculate repeat purchase rate and customer lifetime value. Channel acquisition data includes channel advertising costs and conversion feedback data. Channel advertising costs record the actual expenditure on each promotional channel, while conversion feedback data records the final transaction amount after traffic is generated from each channel. The number of paying customers and the cost of acquiring customers per channel can be combined to calculate the cost of acquiring customers through each channel, which is used to evaluate the return on investment of each channel. After completing the above multi-source data collection, the original operational data is standardized and cleaned using a preset data cleaning rule library. The data cleaning rule library performs three types of operations: caliber alignment, missing value imputation, and outlier removal. Calibration alignment eliminates numerical deviations caused by differences in settlement methods by weighted mapping of revenue statistics for each business subsystem. Missing value imputation uses a weighted combination of historical averages and estimated values ​​from related fields to fill in missing values. Outlier removal marks and removes values ​​that significantly deviate from the normal range based on preset thresholds. After the above cleaning process, a standardized outpatient operation dataset with unified caliber and complete specifications is output.

[0028] According to an embodiment of the present invention, the step of extracting quantitative dimension feature data and processing it to obtain quantitative dimension scores based on the standardized outpatient operation dataset includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of traffic structure from different channels, and the average number of patients seen by doctors per day were extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

[0029] It is important to note that, based on the standardized dataset for outpatient operations, volume-dimensional feature data was extracted, specifically including total visits, initial visits, follow-up visits, channel traffic structure ratio, and average daily patient visits per doctor. Total visits represent the cumulative number of visits by all patients within the statistical period; initial visits represent the number of new customers visiting for the first time within the statistical period; and follow-up visits represent the number of existing customers visiting for the first time within the statistical period. Follow-up visits equal total visits. Initial consultation visit volume; Channel traffic structure ratio records the proportion of visits from each customer acquisition channel (such as online platforms, search engines, social media, referrals, offline promotions, etc.) to the total number of visits; Average daily number of consultations per doctor is the average number of consultations completed by each doctor per day within the statistical period, used to measure the efficiency of doctor resource utilization; The average daily number of consultations per doctor is compared with the preset baseline value for doctor consultations to obtain the doctor's capacity utilization rate; The preset baseline value for doctor consultations is set comprehensively based on the doctor's professional qualifications, departmental scheduling rules, and historical capacity data; The formula for calculating the doctor's capacity utilization rate is: Doctor's capacity utilization rate = Average daily number of consultations per doctor / Preset baseline value for doctor consultations; The higher the capacity utilization rate, the more fully the doctor's resources are utilized; If the utilization rate is consistently low, it indicates that there is scheduling redundancy or doctor idleness; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing through the preset channel structure entropy calculation model; The channel structure entropy calculation model draws on the principle of information entropy to quantify the dispersion of traffic ratios of each channel, and the calculation formula is: Channel structure entropy H = Σ(p i × ln p i ), where p iLet represent the traffic share of the i-th channel. A lower entropy value indicates that traffic is concentrated in a few channels, leading to higher customer acquisition risk; a higher entropy value indicates a more balanced channel distribution and stronger risk resistance. For easier quantitative comparison, channel structure entropy is mapped to a channel structure balance score. The balance score is 1 when the entropy value is maximum (uniform distribution across channels) and 0 when the entropy value is 0 (all traffic is concentrated in a single channel). The ratio of initial consultation visits to total visits is calculated to obtain the initial consultation traffic share, calculated as: Initial consultation traffic share = Initial consultation visits / Total visits. The initial consultation traffic share reflects the clinic's ability to acquire new customers; a low share indicates insufficient growth potential. Finally, based on doctor productivity utilization, channel structure balance, and initial consultation traffic share, a weighted calculation is performed using a preset list of volume dimension weight parameters to obtain the volume dimension score S_volume, calculated as: S_volume = w1 × Doctor's capacity utilization rate + w2 × Channel structure balance + w3 × Initial visit traffic ratio; where w1, w2, and w3 are preset weighting coefficients, and w1 + w2 + w3 = 1; For example, the monthly data of a dental clinic is as follows: total visits 600, initial visits 180, follow-up visits 420; the average number of visits per doctor per day is 8, with a preset baseline of 10; the traffic ratios of the five major channels are 40%, 25%, 15%, 12%, and 8% respectively; doctor's capacity utilization rate = 8 / 10 = 0.80, channel structure entropy H = (0.4×ln0.4+0.25×ln0.25+0.15×ln0.15+0.12×ln0.12+0.08×ln0.08)≈1.52, and the normalized equilibrium is approximately 0.85; the proportion of initial diagnosis traffic is 180 / 600=0.30, and the normalized value is approximately 0.75 (with the industry benchmark of 0.4 as the full score reference); taking w1=0.3, w2=0.3, w3=0.4, then S_quantity=0.3×0.80+0.3×0.85+0.4×0.75=0.795, and the quantity dimension score is at a medium-to-high level.

[0030] According to an embodiment of the present invention, the step of extracting rate dimension feature data and processing the acquisition rate dimension score based on the standardized outpatient operation dataset includes: Based on the standardized outpatient operation dataset, the following data were extracted: initial visit transaction volume, initial visit volume, follow-up visit transaction volume, follow-up visit volume, repeat purchase volume of returning customers, and total number of returning customers. The initial consultation conversion rate is calculated by comparing the initial consultation sales volume with the initial consultation store visit volume. The repeat visit conversion rate is calculated by comparing the repeat visit sales volume with the repeat visit in-store volume. The repeat purchase rate of existing customers is calculated by comparing the ratio of the repeat purchase volume of existing customers to the total number of existing customers. The deviations of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers are calculated by comparing them with the preset industry benchmark conversion rate set to obtain the deviation values ​​of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers. The initial consultation conversion rate deviation, follow-up consultation conversion rate deviation, and repeat customer repurchase rate deviation are weighted and calculated to obtain a rate dimension score.

[0031] It is important to note that, based on the standardized dataset for outpatient operations, the following feature data were extracted: initial consultation transaction volume, initial consultation visit volume, follow-up consultation transaction volume, follow-up visit volume, repeat purchases by existing customers, and total number of existing customers. Specifically, initial consultation transaction volume refers to the number of patients who completed a paid consultation upon their first visit within the statistical period; initial consultation visit volume refers to the total number of patients who visited for the first time within the statistical period; follow-up consultation transaction volume refers to the number of patients who completed a paid consultation upon their second visit within the statistical period; repeat visit volume refers to the total number of patients who visited for the second visit within the statistical period; repeat purchases by existing customers refers to the number of existing customers who had previously visited and made a new purchase record within the statistical period; and total number of existing customers refers to the total number of unique visits by all existing customers within the statistical period. The initial consultation conversion rate is calculated by comparing the initial consultation transaction volume with the initial consultation visit volume. The formula is: Initial Consultation Conversion Rate = Initial Consultation Transaction Volume / Initial Consultation Visit Volume. The initial consultation conversion rate reflects the outpatient... The ability to convert new customer traffic into actual paying patients; the return visit conversion rate is calculated by comparing the return visit sales volume with the return visit visits, using the formula: Return Visit Conversion Rate = Return Visit Sales Volume / Return Visit Visits; the return visit conversion rate reflects the clinic's ability to retain and re-convert existing patients, and a higher return visit conversion rate indicates higher patient satisfaction and trust; the repeat purchase rate is calculated by comparing the repeat purchase volume of existing customers with the total number of existing customers, using the formula: Repeat Purchase Rate = Repeat Purchase Volume of Existing Customers / Total Number of Existing Customers; the repeat purchase rate reflects the continued consumption willingness and brand loyalty of existing customers and is an important indicator for assessing the long-term operational stability of the clinic; the deviation values ​​of the initial visit conversion rate, return visit conversion rate, and repeat purchase rate are calculated by comparing them with a preset industry benchmark conversion rate set, using the formula: Deviation Value = (Actual Conversion Rate / Repeat Purchase Rate) / Repeat Purchase Rate. The deviation is calculated as follows: (Industry benchmark conversion rate) / (Industry benchmark conversion rate). A positive deviation indicates that the conversion rate is better than the industry average, while a negative deviation indicates that it is lower than the industry average. The larger the absolute value of the deviation, the more significant the problem or advantage. The deviation values ​​for initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate are weighted and calculated using a pre-defined list of rate dimension weight parameters to obtain the rate dimension score S_rate. The calculation formula is: S_rate = w4 × Normalized value of initial consultation conversion rate deviation + w5 × Normalized value of follow-up consultation conversion rate deviation + w6 × Normalized value of repeat purchase rate deviation; where w4, w5, and w6 are pre-defined weight coefficients, and w4 + w5 + w6 = 1. Typically, w4 is the highest value because initial consultation conversion has the greatest marginal contribution to revenue. For example, a dental clinic's monthly data is as follows: 200 initial consultation visits, 80 initial consultation sales; 400 follow-up consultation visits, 320 follow-up consultation sales; 600 returning customers, 180 returning customers making repeat purchases; the industry benchmark conversion rate set is: initial consultation sales conversion rate benchmark 0.45, follow-up consultation sales conversion rate benchmark 0.80, returning customer repeat purchase rate benchmark 0.35; initial consultation sales conversion rate = 80 / 200 = 0.40, deviation value = (0.40) / 200 = 0.40. 0.45) / 0.45= 0.111; Follow-up consultation conversion rate = 320 / 400 = 0.80, deviation value = (0.80) 0.80) / 0.80=0; Repeat purchase rate of existing customers = 180 / 600 = 0.30, Deviation value = (0.30) / 600 = 0.80; 0.35) / 0.35= 0.143; taking w4=0.4, w5=0.3, w6=0.3, after normalization the deviation value is mapped to the [0,1] interval, the calculated S_rate≈0.819, the rate dimension score is at a medium level, and the low initial diagnosis conversion is the main shortcoming.

[0032] According to an embodiment of the present invention, the step of extracting price dimension feature data and processing it to obtain a price dimension score based on the standardized outpatient operation dataset includes: Extract total revenue, total number of outpatient visits, revenue from high-value items, and total cost of consumables from the standardized outpatient operation dataset. The overall average order value is calculated by comparing the total revenue with the total number of outpatient visits. The revenue share of high-value projects is calculated by comparing the revenue of high-value projects with the total revenue. The deviation rate between the overall average order value and the median of the preset industry average order value benchmark range is calculated to obtain the average order value deviation rate. The price dimension score is obtained by weighting the deviation rate of the average order value, the revenue ratio of high-value items, and the preset benchmark value of the high-value item ratio.

[0033] It is important to note that, based on the standardized dataset for outpatient operations, price-related feature data was extracted, specifically including total revenue, total number of visits, revenue from high-value procedures, and total cost of consumables. Total revenue is the total charge for all outpatient services within the statistical period; total number of visits is the sum of all visits by patients within the statistical period; revenue from high-value procedures refers to the total revenue of service categories (such as implants, orthodontics, and aesthetic restorations) whose unit price exceeds a preset high-value threshold within the statistical period; total cost of consumables is the cumulative procurement cost of consumables consumed for each service within the statistical period. The high-value threshold is determined based on industry standards and the outpatient clinic's own operational positioning, typically taking more than twice the median charge for all services as the criterion. The total revenue and the total number of visits... The overall average revenue per visit (ARW) is calculated by comparing the number of visits to the total revenue. The formula is: Overall ARR = Total Revenue / Total Visits. ARR reflects the average spending per visit. The revenue share of high-value services is calculated by comparing the revenue from high-value services to the total revenue. The formula is: High-value service revenue share = High-value service revenue / Total Revenue. This share reflects the quality of the outpatient revenue structure; a higher share indicates a greater contribution from high-value services and a more substantial profit margin. A consistently low share suggests an inefficient revenue structure and requires attention to the excessive concentration of low-priced customer acquisition services. The deviation rate between the overall ARW and the median of the preset industry ARW benchmark range is calculated to obtain the ARW deviation rate. The formula is: ARW Deviation Rate = (Overall ARW) / (RAR ... The deviation rate is calculated as follows: (Industry benchmark median) / (Industry benchmark median); a positive deviation rate indicates that the average transaction value is above the industry average, while a negative deviation rate indicates that the average transaction value is below the industry average. The preset industry average transaction value benchmark median is determined by collecting historical average transaction value data from dental clinics of the same size in the same region, and after statistical processing, taking the median of the upper and lower quartiles as the benchmark reference point. The price dimension score S_price is obtained by weighting the average transaction value deviation rate, the revenue ratio of high-value items, and the preset high-value item ratio benchmark value. The calculation formula is: S_price = w7 × average transaction value. The formula is: Unit Price Deviation Normalized Value + w8 × High-Value Item Revenue Ratio Normalized Value + w9 × Cost Control Score; where w7, w8, and w9 are preset weighting coefficients, and w7 + w8 + w9 = 1; the cost control score is obtained by the inverse mapping of consumable cost to revenue ratio, i.e., the lower the cost, the higher the score, used to constrain the risk of high price and low profit; for example, the monthly data of a dental clinic is as follows: total revenue of 1.2 million yuan, total number of visits of 600; revenue from high-value items (implants / orthodontics) of 360,000 yuan; total cost of consumables of 180,000 yuan. The industry benchmark parameters are: median of the benchmark range for average order value of 2,200 yuan, benchmark for high-value item ratio of 0.30; overall average order value = 1,200,000 / 600 = 2,000 yuan; average order value deviation rate = (2000 2200) / 2200≈ 0.091; High-value project revenue ratio = 360,000 / 1,200,000 = 0.30 (exactly equal to the benchmark value); Consumable cost rate = 180,000 / 1,200,000 = 0.15, cost control score is 0.85; Taking w7 = 0.4, w8 = 0.3, w9 = 0.3, after normalization, S_price = 0.4 × 0.75 + 0.3 × 1.00 + 0.3 × 0.85 ≈ 0.855, the price dimension score is at a good level, the main shortcoming is that the overall average order value is slightly lower than the industry median, it is recommended to optimize the project portfolio to improve the average order value.

[0034] Please refer to Figure 2 , Figure 2 This document presents a flowchart illustrating the process of obtaining the priority level of operational issues using a method for in-depth analysis and optimization guidance based on dental clinic operational data, as provided in this application embodiment. According to this embodiment, the process involves processing data based on quantity dimension scoring, rate dimension scoring, and price dimension scoring to obtain a comprehensive operational diagnosis score; further processing to obtain the priority level of operational issues; and matching corresponding personalized optimization guidance schemes. Based on the quantitative dimension score, the rate dimension score, and the price dimension score, a weighted summation is performed using a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive business diagnosis score. Obtain a preset set of business diagnostic level thresholds, including a first preset business diagnostic level threshold and a second preset business diagnostic level threshold, wherein the first preset business diagnostic level threshold is less than the second preset business diagnostic level threshold; If the overall business diagnosis score is less than or equal to the first preset business diagnosis level threshold, the business problem priority level is first-level priority. If the overall business diagnosis score is greater than the first preset business diagnosis level threshold and less than the second preset business diagnosis level threshold, then the business problem priority level is level two priority. If the overall business diagnosis score is greater than or equal to the second preset business diagnosis level threshold, the priority level of the business problem is level three. Based on the priority level of operational issues, a corresponding personalized optimization guidance plan is matched using a preset optimization plan rule base.

[0035] It is emphasized that, according to the volume dimension score S_volume, the rate dimension score S_rate and the price dimension score S_price, weighted summation processing is performed through a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive business diagnosis score S, and the calculation formula is: S =α×S_volume+β×S_rate+γ×S_price; wherein α, β and γ are preset three-dimensional comprehensive weight coefficients, and α+β+γ=1; the weight distribution follows the principle of "conversion first, traffic second, price auxiliary", β (the weight of the rate dimension) is set to be the highest, because the marginal contribution of conversion efficiency to revenue is the most significant; α (the weight of the volume dimension) takes the second place; γ (the weight of the price dimension) is relatively the lowest; a preset business diagnosis grade threshold set is acquired, which includes a first preset business diagnosis grade threshold T1 and a second preset business diagnosis grade threshold T2, and T1<T2; T1 is the trigger line for comprehensive in-depth diagnosis, and T2 is the judgment line for good operation. The two thresholds divide the comprehensive score interval into three grade intervals: if the comprehensive business diagnosis score S≤ T1, the priority level of the business problem is determined as first-level priority, indicating that the outpatient clinic has significant shortcomings in multiple dimensions of volume, rate and price at the same time, the operation status is in an early warning state, it is necessary to immediately start the comprehensive in-depth diagnosis process, and output a comprehensive optimization guidance plan covering the three dimensions of volume, rate and price; if the comprehensive business diagnosis score T1<S<T2, the priority level of the business problem is determined as second-level priority, indicating that the overall operation of the outpatient clinic has local weak links but does not fall into an overall dilemma. The system automatically identifies the dimension with the lowest score among S_volume, S_rate and S_price as the weakest problem dimension, triggers the key diagnosis process for this dimension and outputs a single-dimension focused optimization guidance plan; if the comprehensive business diagnosis score S≥T2, the priority level of the business problem is determined as third-level priority, indicating that the overall operation status of the outpatient clinic is good, and the operation of each dimension is basically healthy. Only the operation status maintenance prompt and fine-tuning suggestions are output, and the current operation strategy remains unchanged; according to the priority level of the business problem, the corresponding personalized optimization guidance plan is matched through a preset optimization plan rule base; wherein, the preset optimization plan rule base stores a set of plan entries graded by problem dimension and priority, and each plan contains structured information such as applicable conditions, execution steps, expected improvement indicators and monitoring cycles; during matching, the optimal adaptive plan is located according to the combination index of the current priority level and the weakest problem dimension, and the plan is pushed to the outpatient management end for reference and execution by management personnel; for example, following the calculation result of the foregoing example: S_volume=0.795, S_rate=0.819, S_price=0.855; taking α=0.25, β=0.45, γ=0.30 (the weight of the rate dimension is the highest), then: the comprehensive business diagnosis score S=0.25×0.795+0.45×0.819+0.30×0.855≈0.824; preset T1=0.60, T2=0.85; since 0.60<0.824<0.85, it is determined as second-level priority; among the three-dimensional scores, S_volume=0.The lowest and weakest dimension is 795, which is the quantity dimension. The system automatically matches optimization solutions based on the quantity dimension from the rule base, and outputs personalized guidance for improving doctors' productivity utilization and optimizing channel structure.

[0036] According to an embodiment of the present invention, the step of collecting tracking data after the execution of a personalized optimization guidance plan according to a preset monitoring period, extracting optimization effect index data, processing to obtain optimization execution effect evaluation coefficients, and triggering a model iterative optimization process accordingly includes: Based on the outpatient operation data collected after the implementation of the preset monitoring cycle, the optimization performance indicators are extracted, including the change rate of store visits, the change rate of overall conversion rate, and the change rate of overall average transaction value. The optimization performance evaluation coefficient is obtained by weighting the changes in store traffic, overall conversion rate, and average order value. The evaluation coefficient for optimized execution effect is compared with the preset threshold for achieving the desired effect; If the evaluation coefficient of the optimized execution effect is less than the preset effect threshold, the execution data of this scheme will be stored in the preset optimization effect sample library, and the parameters of the preset optimization scheme rule library will be updated according to the preset machine learning model to obtain the iterative optimization scheme rule library.

[0037] It is important to note that after the personalized optimization guidance plan is pushed to the outpatient management terminal, outpatient operation tracking data is collected according to a preset monitoring period (usually set weekly or monthly) after the plan is implemented. Optimization performance indicators are extracted, including the rate of change in customer traffic, the rate of change in overall conversion rate, and the rate of change in average transaction value. The rate of change in customer traffic reflects the magnitude of change in total outpatient traffic before and after the plan's implementation, and is calculated using the formula: Rate of change in customer traffic = (Total customer traffic during the implementation period) / (Total customer traffic during the implementation period) (Total store visits during the baseline period) / Total store visits during the baseline period; The overall conversion rate change rate reflects the change in the overall transaction efficiency of initial consultations, follow-up consultations, and repeat purchases before and after the implementation of the plan. The calculation formula is: Overall conversion rate change rate = (Overall conversion rate during the implementation period) / Total conversion rate during the implementation period (Base period overall conversion rate) / Base period overall conversion rate; The overall average order value change rate reflects the change in the average single transaction amount before and after the implementation of the plan. The calculation formula is: Overall average order value change rate = (Average order value during implementation period) / Base period overall conversion rate; (average customer transaction value in the baseline period) / average customer transaction value in the baseline period; according to the change rate of store entry volume, the change rate of comprehensive conversion rate and the change rate of overall average customer transaction value, weighted calculation is performed through a preset list of effect index weight parameters to obtain an optimization execution effect evaluation coefficient E. The calculation formula is: E =λ1×normalized value of store entry volume change rate +λ2×normalized value of comprehensive conversion rate change rate +λ3×normalized value of average customer transaction value change rate; wherein λ1, λ2 and λ3 are preset weight coefficients, and λ1+λ2+λ3=1; after normalization processing, each change rate is mapped to the [0,1] interval: positive growth is mapped to a positive score, and negative growth is mapped to a low score or zero score; the optimization execution effect evaluation coefficient E is compared with a preset effect compliance threshold E_th: if E≥E_th, it is determined that the optimization execution effect meets the standard, the current scheme is maintained for continuous operation and enters the next monitoring cycle for continuous observation; if E<E_th, it is determined that the optimization execution effect does not meet the standard, the input data (business problem priority level, weakest dimension, pushed scheme type), output data (original values of three change rates) and evaluation result of the current scheme are stored in a preset optimization effect sample library as negative feedback samples, and then a preset machine learning model is called to update and adjust parameters in a preset optimization scheme rule base; the machine learning model takes feature-effect pairs in the historical sample library as a training set, and adopts algorithms such as gradient descent or Bayesian optimization to iteratively solve better scheme matching parameters, so as to obtain an optimized scheme rule base after iteration; after the update is completed, the system automatically re-matches based on the latest rule base and pushes the corrected personalized optimization guidance scheme, forming a closed-loop continuous optimization mechanism of "diagnosis → push → execution → evaluation → iteration"; for example, the data of a secondary priority outpatient department after implementing the volume dimension focused optimization scheme for one month is as follows: total store entry volume in the baseline period is 600 person-times → 660 person-times in the execution period, comprehensive conversion rate in the baseline period is 0.72 → 0.76 in the execution period, average customer transaction value in the baseline period is 2,000 yuan → 2,100 yuan in the execution period; preset E_th=0.70, take λ1=0.3, λ2=0.4, λ3=0.3; change rate of store entry volume=(660 600) / 600=+10%; change rate of comprehensive conversion rate=(0.76 0.72) / 0.72≈+5.6%; change rate of average customer transaction value=(2100 2000) / 2000=+5%; after normalized weighting, E ≈0.82>0.70, it is determined that the effect meets the standard, and the current scheme is maintained to enter the next monitoring cycle; if E=0.55<0.70 obtained from a certain calculation, the sample storage and model iteration process will be triggered.

[0038] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of the in-depth analysis and optimization guidance method based on dental clinic operation data provided by the embodiment of the present application.

[0039] Secondly, the present invention also discloses a deep analysis and optimization guidance system based on dental clinic operation data, including a memory and a processor. The memory includes a method program for deep analysis and optimization guidance based on dental clinic operation data. When the processor executes the method program for deep analysis and optimization guidance based on dental clinic operation data, it performs the following steps: Obtain the raw operational data of the target dental clinic, perform data standardization and cleaning, and obtain a standardized dataset of clinic operations. Based on the standardized outpatient operation dataset, quantitative dimension feature data is extracted and processed to obtain quantitative dimension scores. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. Based on the standardized outpatient operation dataset, price dimension feature data is extracted and processed to obtain price dimension scores. The scores are processed based on the quantity dimension, rate dimension, and price dimension to obtain a comprehensive business diagnosis score. Further processing is then used to determine the priority level of business issues, and corresponding personalized optimization guidance plans are matched accordingly. Based on the preset monitoring cycle, track data after the implementation of the personalized optimization guidance plan is collected, optimization effect index data is extracted, the optimization execution effect evaluation coefficient is obtained, and the model iterative optimization process is triggered accordingly.

[0040] It is important to note that the process involves acquiring the target dental clinic's raw operational data, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel acquisition data. This data is then standardized and cleaned using a pre-defined data cleaning rule base to obtain a standardized clinic operational dataset. Based on this dataset, volume-dimensional feature data is extracted, including physician capacity utilization, channel structure balance, and the proportion of initial consultations, and a volume-dimensional score is generated. Similarly, rate-dimensional feature data is extracted from the standardized clinic operational dataset, including deviations in initial consultation conversion rates, follow-up consultation conversion rates, and repeat purchase rates of existing customers, and a rate-dimensional score is generated. Finally, based on the standardized clinic operational dataset, [the following data is extracted / reproduced / extracted / reproduced / recycled]. The system collects price-related feature data, including the deviation rate of average order value, the revenue share of high-value items, and a preset benchmark value for the proportion of high-value items. This data is then processed to obtain a price-related score. Further processing based on the volume-related, rate-related, and price-related scores yields a comprehensive operational diagnostic score. This score is then further processed to determine the priority level of operational issues and to match corresponding personalized optimization guidance plans. Tracking data after the implementation of these personalized optimization guidance plans is collected according to a preset monitoring cycle. Optimization effect indicators, including the rate of change in store visits, the rate of change in overall conversion rate, and the rate of change in overall average order value, are extracted. These are then processed to obtain an evaluation coefficient for the optimization execution effect, triggering a corresponding model iteration optimization process. This enables in-depth analysis and optimization guidance based on dental clinic operational data.

[0041] According to an embodiment of the present invention, the step of obtaining the original operational data of the target dental clinic, performing data standardization and cleaning processing, and obtaining a standardized dataset of clinic operations includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-charge amounts. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

[0042] It is important to note that the raw operational data of the target dental clinic was obtained. This raw operational data comes from multiple business subsystems in the clinic's daily operations, specifically including treatment business data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. Treatment business data includes patient visit records, treatment item categories and quantities, attending physician information, and visit time distribution data. Among these, patient visit records are used to reconstruct the clinic's actual patient volume and frequency; treatment item data reflects the clinic's service structure and item combination; attending physician information is used to assess each physician's capacity utilization and efficiency; and visit time distribution data... This data is used to analyze the rationality of peak-hour scheduling and vacancy rates; consumable cost data includes consumable types, usage, procurement costs, and loss data; by aggregating consumable consumption records for each treatment item and combining the procurement unit price with the loss ratio, the actual marginal cost of a single item can be calculated; appointment traffic data includes channel source tags and appointment attendance rate; channel source tags are used to identify whether patients reach the clinic through different channels such as online platforms, search engines, social media channels, referrals, or offline promotions; the appointment attendance rate reflects the quality of traffic acquisition and appointment conversion efficiency of each channel; revenue data includes project categories and single-session fees, with project categories categorized as orthodontics, implants, and prosthodontics. The system categorizes core services such as teeth cleaning and pediatric dentistry. Combining the single-visit fee with the service category allows for calculation of the average transaction value and the revenue share of high-value services. Customer profile data includes initial and follow-up visit indicators and customer spending history. Initial and follow-up visit indicators differentiate between new and existing customers, supporting separate statistics for initial and follow-up visit conversion rates. Customer spending history records the time intervals between each visit and the amount spent, used to calculate repeat purchase rate and customer lifetime value. Channel acquisition data includes channel advertising costs and conversion feedback data. Channel advertising costs record the actual expenditure on each promotional channel, while conversion feedback data records the final transaction amount after traffic is generated from each channel. The number of paying customers and the cost of acquiring customers per channel can be combined to calculate the cost of acquiring customers through each channel, which is used to evaluate the return on investment of each channel. After completing the above multi-source data collection, the original operational data is standardized and cleaned using a preset data cleaning rule library. The data cleaning rule library performs three types of operations: caliber alignment, missing value imputation, and outlier removal. Calibration alignment eliminates numerical deviations caused by differences in settlement methods by weighted mapping of revenue statistics for each business subsystem. Missing value imputation uses a weighted combination of historical averages and estimated values ​​from related fields to fill in missing values. Outlier removal marks and removes values ​​that significantly deviate from the normal range based on preset thresholds. After the above cleaning process, a standardized outpatient operation dataset with unified caliber and complete specifications is output.

[0043] According to an embodiment of the present invention, the step of extracting quantitative dimension feature data and processing it to obtain quantitative dimension scores based on the standardized outpatient operation dataset includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of channel traffic structure, and the average number of patients seen by doctors per day are extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

[0044] It is important to note that, based on the standardized dataset for outpatient operations, volume-dimensional feature data was extracted, specifically including total visits, initial visits, follow-up visits, channel traffic structure ratio, and average daily patient visits per doctor. Total visits represent the cumulative number of visits by all patients within the statistical period; initial visits represent the number of new customers visiting for the first time within the statistical period; and follow-up visits represent the number of existing customers visiting for the first time within the statistical period. Follow-up visits equal total visits. Initial consultation visit volume; Channel traffic structure ratio records the proportion of visits from each customer acquisition channel (such as online platforms, search engines, social media, referrals, offline promotions, etc.) to the total number of visits; Average daily number of consultations per doctor is the average number of consultations completed by each doctor per day within the statistical period, used to measure the efficiency of doctor resource utilization; The average daily number of consultations per doctor is compared with the preset baseline value for doctor consultations to obtain the doctor's capacity utilization rate; The preset baseline value for doctor consultations is set comprehensively based on the doctor's professional qualifications, departmental scheduling rules, and historical capacity data; The formula for calculating the doctor's capacity utilization rate is: Doctor's capacity utilization rate = Average daily number of consultations per doctor / Preset baseline value for doctor consultations; The higher the capacity utilization rate, the more fully the doctor's resources are utilized; If the utilization rate is consistently low, it indicates that there is scheduling redundancy or doctor idleness; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing through the preset channel structure entropy calculation model; The channel structure entropy calculation model draws on the principle of information entropy to quantify the dispersion of traffic ratios of each channel, and the calculation formula is: Channel structure entropy H = Σ(p i × ln p i ), where p iLet represent the traffic share of the i-th channel. A lower entropy value indicates that traffic is concentrated in a few channels, leading to higher customer acquisition risk; a higher entropy value indicates a more balanced channel distribution and stronger risk resistance. For easier quantitative comparison, channel structure entropy is mapped to a channel structure balance score. The balance score is 1 when the entropy value is maximum (uniform distribution across channels) and 0 when the entropy value is 0 (all traffic is concentrated in a single channel). The ratio of initial consultation visits to total visits is calculated to obtain the initial consultation traffic share, calculated as: Initial consultation traffic share = Initial consultation visits / Total visits. The initial consultation traffic share reflects the clinic's ability to acquire new customers; a low share indicates insufficient growth potential. Finally, based on doctor productivity utilization, channel structure balance, and initial consultation traffic share, a weighted calculation is performed using a preset list of volume dimension weight parameters to obtain the volume dimension score S_volume, calculated as: S_volume = w1 × Doctor's capacity utilization rate + w2 × Channel structure balance + w3 × Initial visit traffic ratio; where w1, w2, and w3 are preset weighting coefficients, and w1 + w2 + w3 = 1; For example, the monthly data of a dental clinic is as follows: total visits 600, initial visits 180, follow-up visits 420; the average number of visits per doctor per day is 8, with a preset baseline of 10; the traffic ratios of the five major channels are 40%, 25%, 15%, 12%, and 8% respectively; doctor's capacity utilization rate = 8 / 10 = 0.80, channel structure entropy H = (0.4×ln0.4+0.25×ln0.25+0.15×ln0.15+0.12×ln0.12+0.08×ln0.08)≈1.52, after normalization the balance is ≈0.85; the proportion of initial diagnosis traffic = 180 / 600=0.30, after normalization ≈0.75 (with the industry benchmark of 0.4 as the full score reference); taking w1=0.3, w2=0.3, w3=0.4, then S_volume = 0.3×0.80+0.3×0.85+0.4×0.75=0.795, the volume dimension score is at a medium-high level.

[0045] According to an embodiment of the present invention, the step of extracting rate dimension feature data and processing the acquisition rate dimension score based on the standardized outpatient operation dataset includes: Based on the standardized outpatient operation dataset, the following data were extracted: initial visit transaction volume, initial visit volume, follow-up visit transaction volume, follow-up visit volume, repeat purchase volume of returning customers, and total number of returning customers. The initial consultation conversion rate is calculated by comparing the initial consultation sales volume with the initial consultation store visit volume. The repeat visit conversion rate is calculated by comparing the repeat visit sales volume with the repeat visit in-store volume. The repeat purchase rate of existing customers is calculated by comparing the ratio of the repeat purchase volume of existing customers to the total number of existing customers. The deviations of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers are calculated by comparing them with the preset industry benchmark conversion rate set to obtain the deviation values ​​of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers. The initial consultation conversion rate deviation, follow-up consultation conversion rate deviation, and repeat customer repurchase rate deviation are weighted and calculated to obtain a rate dimension score.

[0046] It is important to note that, based on the standardized dataset for outpatient operations, the following feature data were extracted: initial consultation transaction volume, initial consultation visit volume, follow-up consultation transaction volume, follow-up visit volume, repeat purchases by existing customers, and total number of existing customers. Specifically, initial consultation transaction volume refers to the number of patients who completed a paid consultation upon their first visit within the statistical period; initial consultation visit volume refers to the total number of patients who visited for the first time within the statistical period; follow-up consultation transaction volume refers to the number of patients who completed a paid consultation upon their second visit within the statistical period; repeat visit volume refers to the total number of patients who visited for the second visit within the statistical period; repeat purchases by existing customers refers to the number of existing customers who had previously visited and made a new purchase record within the statistical period; and total number of existing customers refers to the total number of unique visits by all existing customers within the statistical period. The initial consultation conversion rate is calculated by comparing the initial consultation transaction volume with the initial consultation visit volume. The formula is: Initial Consultation Conversion Rate = Initial Consultation Transaction Volume / Initial Consultation Visit Volume. The initial consultation conversion rate reflects the outpatient... The ability to convert new customer traffic into actual paying patients; the return visit conversion rate is calculated by comparing the return visit sales volume with the return visit visits, using the formula: Return Visit Conversion Rate = Return Visit Sales Volume / Return Visit Visits; the return visit conversion rate reflects the clinic's ability to retain and re-convert existing patients, and a higher return visit conversion rate indicates higher patient satisfaction and trust; the repeat purchase rate is calculated by comparing the repeat purchase volume of existing customers with the total number of existing customers, using the formula: Repeat Purchase Rate = Repeat Purchase Volume of Existing Customers / Total Number of Existing Customers; the repeat purchase rate reflects the continued consumption willingness and brand loyalty of existing customers and is an important indicator for assessing the long-term operational stability of the clinic; the deviation values ​​of the initial visit conversion rate, return visit conversion rate, and repeat purchase rate are calculated by comparing them with a preset industry benchmark conversion rate set, using the formula: Deviation Value = (Actual Conversion Rate / Repeat Purchase Rate) / Repeat Purchase Rate. The deviation is calculated as follows: (Industry benchmark conversion rate) / (Industry benchmark conversion rate). A positive deviation indicates that the conversion rate is better than the industry average, while a negative deviation indicates that it is lower than the industry average. The larger the absolute value of the deviation, the more significant the problem or advantage. The deviation values ​​for initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate are weighted and calculated using a pre-defined list of rate dimension weight parameters to obtain the rate dimension score S_rate. The calculation formula is: S_rate = w4 × Normalized value of initial consultation conversion rate deviation + w5 × Normalized value of follow-up consultation conversion rate deviation + w6 × Normalized value of repeat purchase rate deviation; where w4, w5, and w6 are pre-defined weight coefficients, and w4 + w5 + w6 = 1. Typically, w4 is the highest value because initial consultation conversion has the greatest marginal contribution to revenue. For example, a dental clinic's monthly data is as follows: 200 initial consultation visits, 80 initial consultation sales; 400 follow-up consultation visits, 320 follow-up consultation sales; 600 returning customers, 180 returning customers making repeat purchases; the industry benchmark conversion rate set is: initial consultation sales conversion rate benchmark 0.45, follow-up consultation sales conversion rate benchmark 0.80, returning customer repeat purchase rate benchmark 0.35; initial consultation sales conversion rate = 80 / 200 = 0.40, deviation value = (0.40) / 200 = 0.40. 0.45) / 0.45= 0.111; Follow-up consultation conversion rate = 320 / 400 = 0.80, deviation value = (0.80) 0.80) / 0.80=0; Repeat purchase rate of existing customers = 180 / 600 = 0.30, Deviation value = (0.30) / 600 = 0.80; 0.35) / 0.35= 0.143; taking w4=0.4, w5=0.3, w6=0.3, after normalization the deviation value is mapped to the [0,1] interval, the calculated S_rate≈0.819, the rate dimension score is at a medium level, and the low initial diagnosis conversion is the main shortcoming.

[0047] According to an embodiment of the present invention, the step of extracting price dimension feature data and processing it to obtain a price dimension score based on the standardized outpatient operation dataset includes: Extract total revenue, total number of outpatient visits, revenue from high-value items, and total cost of consumables from the standardized outpatient operation dataset. The overall average order value is calculated by comparing the total revenue with the total number of outpatient visits. The revenue share of high-value projects is calculated by comparing the revenue of high-value projects with the total revenue. The deviation rate between the overall average order value and the median of the preset industry average order value benchmark range is calculated to obtain the average order value deviation rate. The price dimension score is obtained by weighting the deviation rate of the average order value, the revenue ratio of high-value items, and the preset benchmark value of the high-value item ratio.

[0048] It is important to note that, based on the standardized dataset for outpatient operations, price-related feature data was extracted, specifically including total revenue, total number of visits, revenue from high-value procedures, and total cost of consumables. Total revenue is the total charge for all outpatient services within the statistical period; total number of visits is the sum of all visits by patients within the statistical period; revenue from high-value procedures refers to the total revenue of service categories (such as implants, orthodontics, and aesthetic restorations) whose unit price exceeds a preset high-value threshold within the statistical period; total cost of consumables is the cumulative procurement cost of consumables consumed for each service within the statistical period. The high-value threshold is determined based on industry standards and the outpatient clinic's own operational positioning, typically taking more than twice the median charge for all services as the criterion. The total revenue and the total number of visits... The overall average revenue per visit (ARW) is calculated by comparing the number of visits to the total revenue. The formula is: Overall ARR = Total Revenue / Total Visits. ARR reflects the average spending per visit. The revenue share of high-value services is calculated by comparing the revenue from high-value services to the total revenue. The formula is: High-value service revenue share = High-value service revenue / Total Revenue. This share reflects the quality of the outpatient revenue structure; a higher share indicates a greater contribution from high-value services and a more substantial profit margin. A consistently low share suggests an inefficient revenue structure and requires attention to the excessive concentration of low-priced customer acquisition services. The deviation rate between the overall ARW and the median of the preset industry ARW benchmark range is calculated to obtain the ARW deviation rate. The formula is: ARW Deviation Rate = (Overall ARW) / (RAR ... The deviation rate is calculated as follows: (Industry benchmark median) / (Industry benchmark median); a positive deviation rate indicates that the average transaction value is above the industry average, while a negative deviation rate indicates that the average transaction value is below the industry average. The preset industry average transaction value benchmark median is determined by collecting historical average transaction value data from dental clinics of the same size in the same region, and after statistical processing, taking the median of the upper and lower quartiles as the benchmark reference point. The price dimension score S_price is obtained by weighting the average transaction value deviation rate, the revenue ratio of high-value items, and the preset high-value item ratio benchmark value. The calculation formula is: S_price = w7 × average transaction value. The formula is: Unit Price Deviation Normalized Value + w8 × High-Value Item Revenue Ratio Normalized Value + w9 × Cost Control Score; where w7, w8, and w9 are preset weighting coefficients, and w7 + w8 + w9 = 1; the cost control score is obtained by the inverse mapping of consumable cost to revenue ratio, i.e., the lower the cost, the higher the score, used to constrain the risk of high price and low profit; for example, the monthly data of a dental clinic is as follows: total revenue of 1.2 million yuan, total number of visits of 600; revenue from high-value items (implants / orthodontics) of 360,000 yuan; total cost of consumables of 180,000 yuan. The industry benchmark parameters are: median of the benchmark range for average order value of 2,200 yuan, benchmark for high-value item ratio of 0.30; overall average order value = 1,200,000 / 600 = 2,000 yuan; average order value deviation rate = (2000 2200) / 2200≈ 0.091; High-value project revenue ratio = 360,000 / 1,200,000 = 0.30 (exactly equal to the benchmark value); Consumable cost rate = 180,000 / 1,200,000 = 0.15, cost control score is 0.85; Taking w7 = 0.4, w8 = 0.3, w9 = 0.3, after normalization, S_price = 0.4 × 0.75 + 0.3 × 1.00 + 0.3 × 0.85 ≈ 0.855, the price dimension score is at a good level, the main shortcoming is that the overall average order value is slightly lower than the industry median, it is recommended to optimize the project portfolio to improve the average order value.

[0049] According to an embodiment of the present invention, the process of obtaining a comprehensive business diagnosis score based on quantitative, rate, and price dimension scores, further processing to obtain the priority level of business issues, and matching corresponding personalized optimization guidance schemes includes: Based on the quantitative dimension score, the rate dimension score, and the price dimension score, a weighted summation is performed using a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive business diagnosis score. Obtain a preset set of business diagnostic level thresholds, including a first preset business diagnostic level threshold and a second preset business diagnostic level threshold, wherein the first preset business diagnostic level threshold is less than the second preset business diagnostic level threshold; If the overall business diagnosis score is less than or equal to the first preset business diagnosis level threshold, the business problem priority level is first-level priority. If the overall business diagnosis score is greater than the first preset business diagnosis level threshold and less than the second preset business diagnosis level threshold, then the business problem priority level is level two priority. If the overall business diagnosis score is greater than or equal to the second preset business diagnosis level threshold, the priority level of the business problem is level three. Based on the priority level of operational issues, a corresponding personalized optimization guidance plan is matched using a preset optimization plan rule base.

[0050] It is emphasized that, according to the quantity dimension score S_quantity, the rate dimension score S_rate and the price dimension score S_price, weighted summation processing is performed through a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive operation diagnosis score S, and the calculation formula is: S =α×S_quantity+β×S_rate+γ×S_price; wherein α, β and γ are preset three-dimensional comprehensive weight coefficients, and α+β+γ=1; the weight allocation follows the principle of "conversion first, traffic second, price auxiliary", and β (the weight of the rate dimension) is set to be the highest, because conversion efficiency has the most significant marginal contribution to revenue; α (the weight of the quantity dimension) is the second; γ (the weight of the price dimension) is relatively the lowest; a preset operation diagnosis grade threshold set is obtained, which includes a first preset operation diagnosis grade threshold T1 and a second preset operation diagnosis grade threshold T2, with T1<T2; T1 is the trigger line for comprehensive in-depth diagnosis, and T2 is the judgment line for good operation, and the two thresholds divide the comprehensive score interval into three grade intervals: if the comprehensive operation diagnosis score S≤ T1, the priority level of the operation problem is determined as first-level priority, indicating that the clinic has significant shortcomings in multiple dimensions of quantity, rate and price at the same time, the operation status is in an early warning state, it is necessary to immediately start the comprehensive in-depth diagnosis process, and output a comprehensive optimization guidance scheme covering three dimensions of quantity, rate and price; if the comprehensive operation diagnosis score T1<S<T2, the priority level of the operation problem is determined as second-level priority, indicating that the overall operation of the clinic has local weak links but is not caught in an overall dilemma, the system automatically identifies the dimension with the lowest score among S_quantity, S_rate and S_price as the weakest problem dimension, triggers a key diagnosis process for this dimension and outputs a single-dimension focused optimization guidance scheme; if the comprehensive operation diagnosis score S≥T2, the priority level of the operation problem is determined as third-level priority, indicating that the overall operation status of the clinic is good, the operation of each dimension is basically healthy, and only an operation status maintenance prompt and fine-tuning suggestions are output, keeping the current operation strategy unchanged; according to the priority level of the operation problem, the corresponding personalized optimization guidance scheme is matched through a preset optimization scheme rule base; wherein, the preset optimization scheme rule base stores a set of scheme entries classified by problem dimension and priority, and each scheme contains structured information such as applicable conditions, execution steps, expected improvement indicators and monitoring cycles; during matching, the optimally adapted scheme is located according to the combination index of the current priority level and the weakest problem dimension, and the scheme is pushed to the clinic management end for reference and execution by management personnel; for example, following the calculation result of the foregoing example: S_quantity=0.795, S_rate=0.819, S_price=0.855; taking α=0.25, β=0.45, γ=0.30 (the rate dimension has the highest weight), then: the comprehensive operation diagnosis score S=0.25×0.795+0.45×0.819+0.30×0.855≈0.824; preset T1=0.60, T2=0.85; since 0.60<0.824<0.85, it is determined as second-level priority; among the three-dimensional scores, S_quantity=0.The lowest and weakest dimension is 795, which is the quantity dimension. The system automatically matches optimization solutions based on the quantity dimension from the rule base, and outputs personalized guidance for improving doctors' productivity utilization and optimizing channel structure.

[0051] According to an embodiment of the present invention, the step of collecting tracking data after the execution of a personalized optimization guidance plan according to a preset monitoring period, extracting optimization effect index data, processing to obtain optimization execution effect evaluation coefficients, and triggering a model iterative optimization process accordingly includes: Based on the outpatient operation data collected after the implementation of the preset monitoring cycle, the optimization performance indicators are extracted, including the change rate of store visits, the change rate of overall conversion rate, and the change rate of overall average transaction value. The optimization performance evaluation coefficient is obtained by weighting the changes in store traffic, overall conversion rate, and average order value. The evaluation coefficient for optimized execution effect is compared with the preset threshold for achieving the desired effect; If the evaluation coefficient of the optimized execution effect is less than the preset effect threshold, the execution data of this scheme will be stored in the preset optimization effect sample library, and the parameters of the preset optimization scheme rule library will be updated according to the preset machine learning model to obtain the iterative optimization scheme rule library.

[0052] It is important to note that after the personalized optimization guidance plan is pushed to the outpatient management terminal, outpatient operation tracking data is collected according to a preset monitoring period (usually set weekly or monthly) after the plan is implemented. Optimization performance indicators are extracted, including the rate of change in customer traffic, the rate of change in overall conversion rate, and the rate of change in average transaction value. The rate of change in customer traffic reflects the magnitude of change in total outpatient traffic before and after the plan's implementation, and is calculated using the formula: Rate of change in customer traffic = (Total customer traffic during the implementation period) / (Total customer traffic during the implementation period) (Total store visits during the baseline period) / Total store visits during the baseline period; The overall conversion rate change rate reflects the change in the overall transaction efficiency of initial consultations, follow-up consultations, and repeat purchases before and after the implementation of the plan. The calculation formula is: Overall conversion rate change rate = (Overall conversion rate during the implementation period) / Total conversion rate during the implementation period (Base period overall conversion rate) / Base period overall conversion rate; The overall average order value change rate reflects the change in the average single transaction amount before and after the implementation of the plan. The calculation formula is: Overall average order value change rate = (Average order value during implementation period) / Base period overall conversion rate; (average customer price in the base period) / average customer price in the base period; according to the change rate of store entry volume, the change rate of comprehensive conversion rate and the change rate of overall average customer price, weighting calculation is performed through a preset weight parameter list of effect indicators to obtain an optimization execution effect evaluation coefficient E, and the calculation formula is: E =λ1×normalized change rate of store entry volume +λ2×normalized change rate of comprehensive conversion rate +λ3×normalized change rate of average customer price; where λ1, λ2 and λ3 are preset weight coefficients, and λ1+λ2+λ3=1; each change rate is normalized and mapped to the interval [0,1]: positive growth is mapped to a positive score, and negative growth is mapped to a low score or zero score; the optimization execution effect evaluation coefficient E is compared with a preset effect reaching threshold E_th: if E≥E_th, it is determined that the optimization execution effect reaches the standard, the current scheme is maintained to continue operating and enters the next monitoring cycle for continuous observation; if E<E_th, it is determined that the optimization execution effect does not reach the standard, the input data (operation problem priority level, weakest dimension, pushed scheme type), output data (original values of three change rates) and evaluation result of this scheme are stored in a preset optimization effect sample library as a negative feedback sample, and then a preset machine learning model is called to update and adjust parameters in a preset optimization scheme rule base; the machine learning model takes feature-effect pairs in the historical sample library as a training set, and adopts algorithms such as gradient descent or Bayesian optimization to iteratively solve for better scheme matching parameters, so as to obtain an optimized scheme rule base after iteration; after the update is completed, the system automatically re-matches based on the latest rule base and pushes the revised personalized optimization guidance scheme, forming a closed-loop continuous optimization mechanism of "diagnosis→push→execution→evaluation→iteration"; for example, the data one month after a secondary priority clinic implements the focused optimization scheme for the execution volume dimension is as follows: total store entry volume in the base period is 600 person-times → 660 person-times in the execution period, comprehensive conversion rate in the base period is 0.72 → 0.76 in the execution period, average customer price in the base period is 2000 yuan → 2100 yuan in the execution period; preset E_th=0.70, take λ1=0.3, λ2=0.4, λ3=0.3; change rate of store entry volume=(660 600) / 600=+10%; change rate of comprehensive conversion rate=(0.76 0.72) / 0.72≈+5.6%; change rate of average customer price=(2100 2000) / 2000=+5%; after normalized weighting, E ≈0.82>0.70, it is determined that the effect reaches the standard, and the current scheme is maintained to enter the next monitoring cycle; if E=0.55<0.70 is obtained from a certain calculation, the sample storage and model iteration process will be triggered.

[0053] This invention discloses a method and system for in-depth analysis and optimization guidance based on dental clinic operational data. The method involves acquiring raw operational data from a target dental clinic, performing data standardization and cleaning to obtain a standardized operational dataset, extracting quantitative dimension features from this dataset, processing these features to obtain a quantitative dimension score, extracting rate dimension features from the same dataset, processing these features to obtain a rate dimension score, and extracting price dimension features from the same dataset, processing these features to obtain a price dimension score. The method further processes these quantitative, rate, and price dimension scores to obtain a comprehensive operational diagnosis score, further processing to determine the priority level of operational issues, and matching corresponding personalized optimization guidance plans. The method collects tracking data after the implementation of the personalized optimization guidance plans according to a preset monitoring cycle, extracts optimization effect index data, processes this data to obtain an optimization execution effect evaluation coefficient, and triggers a corresponding model iteration optimization process. This enables in-depth analysis and optimization guidance based on dental clinic operational data.

[0054] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0055] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0056] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0057] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for in-depth analysis and optimization guidance based on dental clinic operation data, characterized in that: Includes the following steps: Obtain the raw operational data of the target dental clinic, perform data standardization and cleaning, and obtain a standardized dataset of clinic operations. Based on the standardized outpatient operation dataset, quantitative dimension feature data is extracted and processed to obtain quantitative dimension scores. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. Based on the standardized outpatient operation dataset, price dimension feature data is extracted and processed to obtain price dimension scores. The scores are processed based on the quantity dimension, rate dimension, and price dimension to obtain a comprehensive business diagnosis score. Further processing is then used to determine the priority level of business issues, and corresponding personalized optimization guidance plans are matched accordingly. Based on the preset monitoring cycle, track data after the implementation of the personalized optimization guidance plan is collected, optimization effect index data is extracted, the optimization execution effect evaluation coefficient is obtained, and the model iterative optimization process is triggered accordingly.

2. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 1, characterized in that, The process of acquiring the original operational data of the target dental clinic, performing data standardization and cleaning, and obtaining a standardized dataset of clinic operations includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-charge amounts. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

3. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 2, characterized in that, The step of extracting quantitative dimension feature data from the standardized outpatient operation dataset and processing it to obtain quantitative dimension scores includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of traffic structure from different channels, and the average number of patients seen by doctors per day were extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

4. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 2, characterized in that, The step of extracting rate dimension feature data and processing the acquisition rate dimension score based on the standardized outpatient operation dataset includes: Based on the standardized outpatient operation dataset, the following data were extracted: initial visit transaction volume, initial visit volume, follow-up visit transaction volume, follow-up visit volume, repeat purchase volume of returning customers, and total number of returning customers. The initial consultation conversion rate is calculated by comparing the initial consultation sales volume with the initial consultation store visit volume. The repeat visit conversion rate is calculated by comparing the repeat visit sales volume with the repeat visit in-store volume. The repeat purchase rate of existing customers is calculated by comparing the ratio of the repeat purchase volume of existing customers to the total number of existing customers. The deviations of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers are calculated by comparing them with the preset industry benchmark conversion rate set to obtain the deviation values ​​of the initial consultation conversion rate, follow-up consultation conversion rate, and repeat purchase rate of existing customers. The initial consultation conversion rate deviation, follow-up consultation conversion rate deviation, and repeat customer repurchase rate deviation are weighted and calculated to obtain a rate dimension score.

5. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 2, characterized in that, The step of extracting price dimension feature data from the standardized outpatient operation dataset and processing it to obtain a price dimension score includes: Extract total revenue, total number of outpatient visits, revenue from high-value items, and total cost of consumables from the standardized outpatient operation dataset. The overall average order value is calculated by comparing the total revenue with the total number of outpatient visits. The revenue share of high-value projects is calculated by comparing the revenue of high-value projects with the total revenue. The deviation rate between the overall average order value and the median of the preset industry average order value benchmark range is calculated to obtain the average order value deviation rate. The price dimension score is obtained by weighting the deviation rate of the average order value, the revenue ratio of high-value items, and the preset benchmark value of the high-value item ratio.

6. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 1, characterized in that, The process involves scoring based on quantity, rate, and price dimensions to obtain a comprehensive business diagnosis score. Further processing yields the priority level of business issues, and corresponding personalized optimization guidance plans are then matched, including: Based on the quantitative dimension score, the rate dimension score, and the price dimension score, a weighted summation is performed using a preset three-dimensional comprehensive weight parameter list to obtain the comprehensive business diagnosis score. Obtain a preset set of business diagnostic level thresholds, including a first preset business diagnostic level threshold and a second preset business diagnostic level threshold, wherein the first preset business diagnostic level threshold is less than the second preset business diagnostic level threshold; If the overall business diagnosis score is less than or equal to the first preset business diagnosis level threshold, the business problem priority level is first-level priority. If the overall business diagnosis score is greater than the first preset business diagnosis level threshold and less than the second preset business diagnosis level threshold, then the business problem priority level is level two priority. If the overall business diagnosis score is greater than or equal to the second preset business diagnosis level threshold, the priority level of the business problem is level three. Based on the priority level of operational issues, a corresponding personalized optimization guidance plan is matched using a preset optimization plan rule base.

7. The method for in-depth analysis and optimization guidance based on dental clinic operation data according to claim 1, characterized in that, The process of collecting tracking data after the implementation of the personalized optimization guidance plan according to a preset monitoring cycle, extracting optimization effect index data, processing it to obtain the optimization execution effect evaluation coefficient, and triggering the model iterative optimization process accordingly includes: Based on the outpatient operation data collected after the implementation of the preset monitoring cycle, the optimization performance indicators are extracted, including the change rate of store visits, the change rate of overall conversion rate, and the change rate of overall average transaction value. The optimization performance evaluation coefficient is obtained by weighting the changes in store traffic, overall conversion rate, and average order value. The evaluation coefficient for optimized execution effect is compared with the preset threshold for achieving the desired effect; If the evaluation coefficient of the optimized execution effect is less than the preset effect threshold, the execution data of this scheme will be stored in the preset optimization effect sample library, and the parameters of the preset optimization scheme rule library will be updated according to the preset machine learning model to obtain the iterative optimization scheme rule library.

8. A system for in-depth analysis and optimization guidance based on dental clinic operational data, characterized in that: The system includes a memory and a processor. The memory contains a program for a method of in-depth analysis and optimization guidance based on dental clinic operating data. When the processor executes the program for the in-depth analysis and optimization guidance method based on dental clinic operating data, it performs the following steps: Obtain the raw operational data of the target dental clinic, perform data standardization and cleaning, and obtain a standardized dataset of clinic operations. Based on the standardized outpatient operation dataset, quantitative dimension feature data is extracted and processed to obtain quantitative dimension scores. Based on the standardized outpatient operation dataset, extract the rate dimension feature data and process the acquisition rate dimension score. Based on the standardized outpatient operation dataset, price dimension feature data is extracted and processed to obtain price dimension scores. The scores are processed based on the quantity dimension, rate dimension, and price dimension to obtain a comprehensive business diagnosis score. Further processing is then used to determine the priority level of business issues, and corresponding personalized optimization guidance plans are matched accordingly. Based on the preset monitoring cycle, track data after the implementation of the personalized optimization guidance plan is collected, optimization effect index data is extracted, the optimization execution effect evaluation coefficient is obtained, and the model iterative optimization process is triggered accordingly.

9. The in-depth analysis and optimization guidance system based on dental clinic operation data according to claim 8, characterized in that, The process of acquiring the original operational data of the target dental clinic, performing data standardization and cleaning, and obtaining a standardized dataset of clinic operations includes: Obtain raw operational data from the target dental clinic, including treatment data, consumable cost data, appointment traffic data, revenue data, customer profile data, and channel customer acquisition data. The medical service data includes patient medical records, medical procedures, attending physician information, and medical time distribution data. The consumable cost data includes consumable types, consumable usage, consumable procurement costs, and loss data; The appointment traffic data includes channel source tags and appointment attendance rate; The revenue data includes project categories and single-charge amounts. The customer profile data includes customer initial and follow-up visit identifiers and customer consumption history data; The customer acquisition data from these channels includes channel advertising costs and conversion feedback data. Based on the original operational data, the data is standardized and cleaned using a pre-set data cleaning rule base to obtain a standardized dataset of outpatient operations.

10. The in-depth analysis and optimization guidance system based on dental clinic operation data according to claim 9, characterized in that, The step of extracting quantitative dimension feature data from the standardized outpatient operation dataset and processing it to obtain quantitative dimension scores includes: Based on the standardized outpatient operation dataset, the total number of visits, the number of initial visits, the number of follow-up visits, the proportion of traffic structure from different channels, and the average number of patients seen by doctors per day were extracted. The average number of patients seen by the doctor per day is compared with the preset benchmark value of the number of patients seen by the doctor to obtain the doctor's productivity utilization rate; Based on the channel traffic structure ratio, the channel structure balance is obtained by processing it through a preset channel structure entropy calculation model. The ratio of the number of initial visits to the total number of visits is calculated to obtain the percentage of initial visits. The quantitative dimension score is obtained by weighting the doctor's capacity utilization rate, channel structure balance, and the proportion of initial consultation traffic.

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