Digital management method for improving family resilience of oral cancer patient

Abstract

The invention discloses a digital management method for improving family resilience of an oral cancer patient, and relates to the technical field of medical health management and digital intervention. According to the method, a four-order mathematical formula model of a single-dimensional score, a dynamic weight, a comprehensive index and an intervention gain is constructed, and influence factors and an intervention effect of the family resilience are quantified; according to the method, family shortages in different treatment stages are identified through a dynamic weight adjustment formula, a targeted scheme is generated, homogenization intervention is avoided, and the R value is averagely increased by 18.6 points after medium and low restoring force family intervention; according to the invention, an APP, intelligent hardware and a hospital system are integrated, automatic data acquisition and real-time formula calculation are realized, the management efficiency is improved by 60%, and manual operation errors are reduced; according to the method, formula parameters can be finely adjusted through localized data (for example, alpha E of the E dimension is adjusted according to economic level differences of different regions), the requirements of different medical institutions are met, and the platform is easy to operate and can be quickly mastered by families and doctors.

Description

Technical Field

[0001] This invention relates to the field of medical and health management and digital intervention technology, specifically a digital management method for improving the resilience of families of oral cancer patients. Background Technology

[0002] Oral cancer is a common malignant tumor of the head and neck, with an annual global incidence of over 300,000 cases. Treatment processes (surgery, radiotherapy, and chemotherapy) can easily lead to swallowing difficulties, facial deformities, and psychological trauma, severely impacting not only the patient's physical and mental well-being but also placing multiple challenges on their families, including heavy care burdens, significant financial pressure, and intense psychological stress. Family resilience, as a core ability of families to cope with crisis events, directly determines patient treatment adherence and prognosis—studies show that patients from highly resilient families have a 23% higher 5-year survival rate than those from low-resilient families. However, current family management of oral cancer patients suffers from significant technical deficiencies: 1. Subjectivity in assessment methods: Existing family resilience assessments rely on questionnaire scales (such as the Family Resilience Assessment Scale-C), which are scored manually (e.g., subjective scoring of the family communication dimension from 1 to 4 points). This fails to quantify the differences in the contributions of each influencing factor, resulting in an error rate of over 30% in the assessment results. 2. Homogeneous intervention programs: Clinical interventions often employ standardized health education and psychological counseling, failing to develop personalized programs based on the family's resilience (such as insufficient financial support for some families or inadequate caregiving capabilities for others), resulting in an intervention effectiveness rate of less than 40%. 3. Lack of quantifiable effect tracking: The lack of scientific mathematical models to quantify the intervention effect, relying solely on subjective indicators such as patient satisfaction, makes it impossible to dynamically adjust the intervention strategy, leading to a decline in the resilience of some families due to inappropriate intervention; 4. Low level of digitalization: It does not integrate digital tools (such as APP, wearable devices) to achieve real-time data collection and analysis, relies on manual recording, and the data is lagging and incomplete, making it difficult to support long-term dynamic management.

[0003] More importantly, existing technologies have not built scientific mathematical formula models—they cannot quantify the relationship between dimensions such as family care capacity and economic support level and resilience, nor can they calculate the extent to which intervention measures improve resilience, resulting in a lack of quantifiable and replicable scientific basis for the management process.

[0004] Therefore, constructing a mathematical formula model that integrates multi-dimensional quantitative assessment, dynamic weight adjustment, and intervention effect calculation, combined with digital management processes, has become the core key to breaking through existing technological bottlenecks. Summary of the Invention

[0005] The purpose of this invention is to provide a digital management method for improving the resilience of families of oral cancer patients, in order to solve the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a digital management method for improving the resilience of families of oral cancer patients, comprising at least the following steps: S1: Multi-dimensional basic data collection, collecting family structure and care data, economic support data, psychological and social data and health management data of oral cancer patients' families through a digital platform, once a week, continuing for 3 months after the end of treatment; S2: Conduct a multi-dimensional quantitative assessment of recovery capabilities; S3: Generation of personalized intervention plans; S4: Tracking the effects of intervention; S5: Digital closed-loop management, the platform automatically collects data, runs formulas and generates reports, realizing full-process automation.

[0007] Furthermore, the family structure and care data include the number of family caregivers F1, the kinship between the primary caregiver and the patient F2, the daily care duration of the caregiver F3, and whether the caregiver has received professional care training F4; The economic support data includes household monthly disposable income E1, medical expense reimbursement rate E2, whether social assistance is received E3, and household debt situation E4. The psychological and social data include the weekly psychological counseling time of family members (P1), the number of family members providing social support (P2), the frequency of family communication (P3), and the patient's psychological status score (P4). The health management data includes patient medication adherence rate (H1), family health knowledge level (H2), patient nutritional target achievement rate (H3), and family emergency response capability (H4).

[0008] Furthermore, S2 includes at least the following steps: S2.1: First, calculate the score for each dimension: in: Do not correspond to linear normalization under the dimensions of family structure and care data; Do not correspond to linear normalization under the economic support data dimension; Do not correspond to linear normalization under the dimensions of psychological and social data; Do not use linear normalization under the corresponding health management data dimension; in: , For the minimum and maximum values ​​of clinical statistical indicators (e.g., F3: min = 1 hour (max=12 hours). S2.2: Dynamic weight adjustment: in, Based on weights, ={0.30,0.25,0.25,0.20}; To adjust the coefficient, ={0.6,0.4,0.5,0.3} The coefficients for treatment stages are: diagnosis period = 1.2, treatment period = 1.0, and recovery period = 0.8. The score for the corresponding dimension; After normalization, we get ; S2.3: Calculation of Comprehensive Resilience Index: in, .

[0009] Furthermore, the personalized intervention plan is generated based on the R value and Promoting intervention measures (psychological counseling, care training, etc.); The R value is set as follows: R<60 for low resilience, 60≤R<100 for medium resilience, and R≥100 for high resilience.

[0010] Furthermore, S4 includes at least the following steps: pass: Where ΔSd is the single-dimensional gain; θdk is the weight coefficient for each dimension; Idk is the change in this indicator; ηd is the adjustment factor; and Ttotal is the total time. Intervention effect coefficient Psychological counseling = 0.8, care training = 0.7, economic assistance = 0.5, health education = 0.6; Response coefficient ={0.7,0.5,0.9,0.8}; Calculate the one-dimensional gain: Where ΔR is the overall gain; γ is the balance coefficient, used to adjust the influence of the weighted score and the minimum score; The normalized dynamic weight represents the weight value at time t+1; This represents the change in the minimum score of a single dimension between time period t and t+1, measuring the increase or decrease in the minimum dimension score of resilience during that period. Calculate the overall gain and dynamically adjust the scheme.

[0011] Furthermore, the digital platform includes an APP, a doctor's interface, and smart hardware interfaces, automatically synchronizing questionnaire data, dietary data, and hospital system data.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a fourth-order mathematical formula model consisting of single-dimensional scores, dynamic weights, a comprehensive index, and intervention gains to quantify the influencing factors and intervention effects of family resilience; 2. This invention identifies family weaknesses at different treatment stages through a dynamic weight adjustment formula, generates targeted solutions, avoids homogenized interventions, and improves the average R score of families with low to moderate resilience by 18.6 points after intervention. 3. This invention integrates an app, smart hardware, and a hospital system to achieve automatic data collection and real-time formula calculation, improving management efficiency by 60% and reducing human error. 4. The formula parameters of this invention can be fine-tuned through localized data (such as adjusting αE in dimension E according to differences in economic levels in different regions) to adapt to the needs of different medical institutions. Moreover, the platform is easy to operate, and both families and doctors can quickly get started. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a flowchart illustrating the frequency, type, and preprocessing of data acquisition at different stages of this invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0016] This invention aims to overcome the shortcomings of existing oral cancer patient family resilience management assessments, such as subjectivity, homogeneity of interventions, lack of quantifiable effects, and lack of digitalization. It provides a digital management method based on multi-dimensional quantitative assessment formulas for family resilience, dynamic weight adjustment formulas, and intervention effect gain formulas. Through mathematical models, it achieves accurate resilience assessment, personalized intervention plan generation, and dynamic tracking of intervention effects. Combined with a digital platform, it realizes fully automated management, significantly improving the level of patient family resilience and clinical management efficiency.

[0017] This invention can be applied to family support management throughout the treatment process of oral cancer patients. Through quantitative assessment, precise intervention and effect tracking, it can significantly improve the recovery ability of patients' families to cope with the disease and improve the prognosis of patients and the quality of family life.

[0018] Example 1: See figure and Figure 2 : Step 1: Collection of basic data on family resilience across multiple dimensions Four types of core data from oral cancer patients' families are collected through a digital management platform (including an app, a doctor's app, and hardware device interfaces). The collection cycle is once a week after the patient's diagnosis until three months after the end of treatment: Family structure and care data (F): Number of family caregivers (F1, unit: person), kinship between the primary caregiver and the patient (F2, quantitative value: spouse=4, parents / children=3, siblings=2, others=1), daily care time of caregivers (F3, unit: hour), whether caregivers have received professional care training (F4, Boolean value: yes=1, no=0). Economic support data (E): monthly disposable income of the household (E1, unit: 10,000 yuan), medical expense reimbursement ratio (E2, unit: %), whether social assistance is received (E3, Boolean value: yes=1, no=0), household debt situation (E4, quantitative value: no debt=4, light debt <50,000 yuan=3, moderate debt 50,000-100,000 yuan=2, heavy debt >100,000 yuan=1). Psychological and social data (P): Weekly psychological counseling time for family members (P1, in hours), number of family members providing social support (P2, in people), frequency of family communication (P3, quantitative values: daily communication = 4, 3-5 times per week = 3, 1-2 times per week = 2, <1 time per month = 1), patient's psychological status score (P4, using the Self-Rating Anxiety Scale (SAS), quantitative values: <50 points = 4, 50-59 points = 3, 60-69 points = 2, ≥70 points = 1); Health management data (H): Patient medication adherence rate (H1, unit: %), family health knowledge mastery (H2, score through platform quizzes, unit: points, full score 100), patient nutritional target achievement rate (H3, unit: %, calculated through dietary data collected by smart meal trays), family emergency response capability (H4, quantitative value: mastering 3 or more emergency skills = 4, mastering 1-2 skills = 3, mastering only basic nursing care = 2, no emergency response capability = 1).

[0019] Step 2: Construction of a Multidimensional Family Resilience Assessment Formula This invention is the first to construct a three-order quantitative assessment formula based on dimensional scores, dynamic weights, and a comprehensive resilience index, enabling accurate calculation of family resilience. The specific formula is as follows: 2.1 Formula for Calculating Single-Dimensional Resilience Score For the four types of basic data collected, a standardized processing method and a weighted summation of indicators were used to calculate the resilience score for each dimension (value range [0,100]). The formula is taken as an example of the family structure and care dimension (S_F): in: The basic weights of the k-th indicator under the family structure and care dimension (determined through training with clinical data): =0.25, =0.20, =0.30, =0.25, total weight = 1); The standardized value of the k-th indicator (using Min-Max standardization to eliminate dimensions, formula: For example, if F3 (care duration) has X_F3,min = 1 hour and X_F3,max = 12 hours, and a family has F3 = 6 hours, then... ); Family structure and care dimension resilience score (value range [0,100], the higher the score, the stronger the resilience of this dimension).

[0020] Similarly, the economic support dimension ( ), psychological and social dimensions ( ), health management dimension ( The calculation formulas for ) are as follows: This formula overcomes the shortcomings of traditional scales that use equal weighting. It assigns weights to each indicator based on their impact on the resilience of oral cancer families (e.g., care duration F3 has the highest weight because oral cancer patients require long-term intensive care), making the single-dimensional scores more in line with clinical reality and reducing the assessment error by more than 40% compared to traditional methods.

[0021] 2.2 Dynamic Weight Adjustment Formula Considering the differences in the core needs of family resilience at different stages of oral cancer treatment (diagnosis, treatment, and recovery), a dynamic weighting adjustment formula is constructed to adjust the contribution of each dimension to overall resilience in real time. The formula is as follows: in: : The dynamic weight of the d-th dimension (d∈{F,E,P,H}, total weight = 1); : The base weight of the d-th dimension (clinical default value: =0.30, =0.25, 0.25, =0.20); Dimension adjustment factor (set according to the importance of the dimension): =0.6, =0.4, =0.5, =0.3, indicating that adjustments to the care dimension have the highest priority. : The current score of the d-th dimension (e.g., S_F=60 points); Treatment stage coefficient (when the diagnosis period T=1) =1.2, during the treatment period T=2 =1.0, during the recovery period T=3 =0.8, indicating that families in the diagnosis period need more special support. Taking family structure and care dimension (d=F) as an example, if =60 points, T=1 (confirmation period), then: After dynamic weight calculation, normalization is required to ensure that the sum of weights equals 1. This formula achieves dynamic weight adjustment of two factors: treatment stage and dimensional shortcomings—during the diagnosis period ( =1.2) and low score in the care dimension ( For families with a score of 60, the weight of the care dimension is automatically increased (from 0.30 to 0.386), which solves the static defect of using the same weight at different stages in traditional methods and makes the comprehensive assessment more targeted.

[0022] 2.3 Formula for calculating the Comprehensive Family Resilience Index (R) Combining scores from various dimensions ( , , , ) and normalized dynamic weights ( '、 '、 '、 Introducing a nonlinear correction factor (γ), the comprehensive family resilience index is calculated (range [0, 100]), as shown in the following formula: in: Nonlinear correction factor (value 0.7, determined through training with clinical data); The minimum score for each dimension (reflecting the weakest link effect, i.e., the family's resilience is limited by the weakest dimension). : Comprehensive Family Resilience Index (R≥100 indicates high resilience, 60≤R<100 indicates medium resilience, and R<60 indicates low resilience).

[0023] Example: If =0.386, =60; =0.26, =70; =0.22, =50; =0.134, =65, then: This formula addresses the problem of traditional linear formulas neglecting the weakest link effect through nonlinear calculations involving weighted summation and short-board correction—even if a family scores highly in most dimensions (such as...). =60、 =70), but the psychological dimension score was low ( =50), the overall index is still limited by the weakest link (R≈56.71), which is more in line with the actual situation that clinical family resilience depends on the weakest link.

[0024] Step 3: Generation of personalized digital intervention plans (based on formula results) The digital management platform uses the Comprehensive Resilience Index (R) and scores from various dimensions to determine its effectiveness. ), generate personalized intervention plans: Families with low resilience (R<60): Prioritize interventions for the dimension with the lowest score (as shown in the example). =50, with priority given to arrange two psychological counseling sessions per week), while also matching high-priority resources (such as free care training and financial assistance application channels). For families with moderate resilience (60≤R<100): Develop intervention plans for dimensions with scores <70 (e.g., =65, pushing daily health knowledge learning tasks). Highly resilient families (R≥100): Maintain existing management practices, regularly share family mutual support cases, and encourage participation in peer support.

[0025] The intervention plan is pushed out in real time through the platform's app, including the intervention content (such as psychological counseling), frequency (such as twice a week), person in charge (such as psychologist Zhang XX), and expected goals (such as within 4 weeks). (Raised to 60 points).

[0026] Step 4: Constructing the formula for the gain of intervention effect To quantify the intervention effect, an intervention effect gain formula was constructed to calculate the improvement in scores for each dimension after the intervention. The formula for the increase in the overall resilience index (ΔR) is as follows: 4.1 Formula for the gain of single-dimensional intervention effect (ΔS_d) in: , : Scores of dimension d before intervention (week t) and after intervention (week t+1); : The effectiveness coefficient of the k-th intervention under the d-th dimension (such as psychological counseling) =0.8, social support group =0.6 (determined through clinical data). The implementation rate of the k-th intervention measure (values ​​range from 0 to 1, e.g., psychological counseling is conducted twice a week, and if both sessions are actually completed, then...). =1); : Dimensional response coefficients (d∈{F,E,P,H}, =0.7, =0.5, =0.9, =0.8, indicating that the psychological dimension is most sensitive to the intervention (the value is 0.8, reflecting that the psychological dimension is most sensitive to the intervention). Total intervention period (unit: weeks, e.g., 12 weeks); : Improvement in single-dimensional score ( A value greater than 0 indicates that the intervention is effective. ≤0 indicates intervention and adjustment is needed.

[0027] 4.2 Formula for the gain of comprehensive resilience intervention effect (ΔR) This formula, for the first time, quantifies the correlation between intervention measures, implementation rate, and score improvement, through... , Equal coefficients are used to differentiate the effectiveness of different interventions (e.g., psychological counseling is more effective than social support), addressing the limitation of traditional methods in quantifying intervention effectiveness—for example, the implementation rate of interventions in a family's psychological dimension. =1, then ≈0.8×1×0.9×√(1 / 12)≈0.21, 4 weeks later It can improve the score by about 0.84 points, making the intervention effect predictable and traceable.

[0028] Step 5: Closed-loop management of the digital platform The digital management platform enables closed-loop management of data collection, formula calculation, solution generation, and effect tracking. Data collection module: Automatically synchronizes APP questionnaire data (such as family communication frequency P3), smart hardware data (such as the nutritional compliance rate of smart plates H3), and hospital system data (such as medical expense reimbursement ratio E2). Formula calculation module: Runs the mathematical formulas from steps 2 and 4 in real time and generates... R, , The results are quantified and visualized (e.g., a line chart showing the weekly changes in R). Solution adjustment module: If ≤0 (intervention ineffective), automatically adjust intervention measures (e.g., change online psychological counseling to offline one-on-one consultation), and recalculate. ; Report generation module: Generates a monthly family resilience assessment report, including changes in R-value, weakness dimensions, intervention effects, and plans for the following month, and pushes it to doctors and families.

[0029] Example 2: This embodiment is used to verify the performance of the model proposed in Embodiment 1 above. Study subjects: 150 families of patients diagnosed with oral cancer in the Department of Stomatology of a tertiary hospital from January 2024 to December 2024 were selected (52 families with low resilience, 68 families with medium resilience, and 30 families with high resilience). The intervention period was 12 weeks. Parameter settings: Base weights ={0.30,0.25,0.25,0.20}, adjustment coefficient ={0.6,0.4,0.5,0.3}, correction factor γ=0.7, effect coefficient (Psychological counseling = 0.8, care training = 0.7, economic assistance = 0.5, health education = 0.6); Intervention effect: After 12 weeks, the R value of low resilience families increased from 52.3±4.5 to 71.2±3.8 (ΔR=18.9), the R value of medium resilience families increased from 68.5±3.2 to 82.4±2.9 (ΔR=13.9), and the R value of high resilience families remained at 100.6±2.5; the intervention effectiveness rate (ΔR≥10) reached 88%, which was significantly higher than that of the traditional method (40%). Formula accuracy: The comprehensive resilience index (R) was significantly positively correlated with patient treatment compliance (r=0.72) and quality of life score after 5 months (r=0.68) (P<0.001), verifying the clinical effectiveness of the formula.

[0030] Example 3: This embodiment is based on the typical family intervention case presented in Embodiment 1. ( , , , ) and normalized dynamic weights ( '、 '、 '、 ') Patient Family A: Diagnosis period (T=1), number of family caregivers F1=1 (spouse), caregiver untrained (F4=0), medical reimbursement rate E2=60%, patient SAS score P4=65 (anxiety), medication adherence rate H1=80%; Initial assessment: =45, =65, =48, =60; Dynamic weights =0.39, =0.25, =0.23, =0.13; Overall R = 54.2 (low resilience); Intervention plan: Prioritize intervention in the P dimension ( =48): Psychological counseling twice a week ( =1); Supplementary F-dimensional intervention: Online training for caregivers ( =1); Results tracking: After 4 weeks, =58(Δ =10), =55 (Δ =10); Overall R=66.8 (improved to medium resilience); 8 weeks later, =65, =62, R=75.3, the expected goal has been achieved.

[0031] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A digital management method for improving the resilience of families of oral cancer patients, characterized in that: At least the following steps are included: S1: Multi-dimensional basic data collection, collecting family structure and care data, economic support data, psychological and social data and health management data of oral cancer patients' families through a digital platform, once a week, continuing for 3 months after the end of treatment; S2: Conduct a multi-dimensional quantitative assessment of recovery capabilities; S3: Generation of personalized intervention plans; S4: Tracking the effects of intervention; S5: Digital closed-loop management, the platform automatically collects data, runs formulas and generates reports, realizing full-process automation.

2. The digital management method for improving the resilience of families of oral cancer patients according to claim 1, characterized in that: The family structure and care data include the number of family caregivers F1, the kinship between the primary caregiver and the patient F2, the daily care duration of the caregiver F3, and whether the caregiver has received professional care training F4. The economic support data includes household monthly disposable income E1, medical expense reimbursement rate E2, whether social assistance is received E3, and household debt situation E4. The psychological and social data include the weekly psychological counseling time of family members (P1), the number of family members providing social support (P2), the frequency of family communication (P3), and the patient's psychological status score (P4). The health management data includes patient medication adherence rate (H1), family health knowledge level (H2), patient nutritional target achievement rate (H3), and family emergency response capability (H4).

3. The digital management method for improving the resilience of families of oral cancer patients according to claim 1, characterized in that: S2 includes at least the following steps: S2.1: First, calculate the score for each dimension: in: Do not correspond to linear normalization under the dimensions of family structure and care data; Do not correspond to linear normalization under the economic support data dimension; Do not correspond to linear normalization under the dimensions of psychological and social data; Do not correspond to linear normalization under the health management data dimension; S2.2: Dynamic weight adjustment: in, Based on the weights, ={0.30,0.25,0.25,0.20}; To adjust the coefficient, ={0.6,0.4,0.5,0.3} The coefficients for treatment stages are: diagnosis period = 1.2, treatment period = 1.0, and recovery period = 0.

8. The score for the corresponding dimension; After normalization, we get ; S2.3: Calculation of Comprehensive Resilience Index: in, .

4. A digital management method for improving the resilience of families of oral cancer patients according to claim 3, characterized in that: The personalized intervention plan is generated based on the R value and Push intervention measures; The R value is set as follows: R<60 for low resilience, 60≤R<100 for medium resilience, and R≥100 for high resilience.

5. A digital management method for improving the resilience of families of oral cancer patients according to claim 4, characterized in that: The S4 includes at least the following steps: pass: Where ΔSd is the single-dimensional gain; θdk is the weight coefficient for each dimension; Idk is the change in this indicator; ηd is the adjustment factor; and Ttotal is the total time. Calculate the one-dimensional gain: Where ΔR is the overall gain; γ is the balance coefficient, used to adjust the influence of the weighted score and the minimum score; The normalized dynamic weight represents the weight value at time t+1; This represents the change in the minimum score of a single dimension between time period t and t+1, measuring the increase or decrease in the minimum dimension score of resilience during that period. Calculate the overall gain and dynamically adjust the scheme.

6. A digital management method for improving the resilience of families of oral cancer patients according to claim 1, characterized in that: The digital platform includes an APP, a doctor's interface, and smart hardware interfaces, and automatically synchronizes questionnaire data, dietary data, and hospital system data.

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