Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

Budget impact of resmetirom for the treatment of adults with Noncirrhotic Nonalcoholic Steatohepatitis (NASH) with moderate to advanced liver fibrosis (consistent with stages F2 to F3 fibrosis)

AIMS

This study assessed the budget impact of resmetirom as a treatment for adults with non-cirrhotic non-alcoholic steatohepatitis (NASH) with moderate-to-advanced liver fibrosis and estimated total costs for a hypothetical private payer in the United States.

MATERIALS AND METHODS

A three-year budget impact analysis based on an open cohort state transition model was developed for a hypothetical one-million-member private health plan. The comparator was Standard of Care (SOC), defined as routine care for non-cirrhotic NASH patients with moderate-to-advanced liver fibrosis. Each year, the number of resmetirom treatment-eligible patients was estimated through prevalent, incident, and diagnostic rate estimates. Costs included resources incurred by the medical and pharmacy benefits of private payers, including resmetirom drug acquisition costs, diagnosis and monitoring, other medical and other prescription costs stratified by disease progression status (i.e., non-cirrhotic vs. cirrhotic/advanced liver diseases). Resmetirom adverse event management costs were included in sensitivity analysis. Drug costs were estimated based on the average wholesale acquisition cost as of March 2024. Other costs were based on published sources and inflated to 2023 US dollars. Budget impact outcomes were presented in aggregate, net, and on a per-member per-month (PMPM) basis.

RESULTS

Compared with a scenario without resmetirom, the introduction of resmetirom yielded results ranging from 50 to 238 treated patients, net budget impact of $2.2 to $9.5 million, and PMPM from $0.19 to $0.80 over years one and three. Net costs excluding resmetirom declined over time. In sensitivity analyses, results were most sensitive to diagnostic and epidemiologic inputs.

LIMITATIONS

Market shares are based on internal forecasts, a short time horizon, average treatment effects, and other limitations common to BIMs.

CONCLUSION

The adoption of resmetirom on the formulary for the treatment of non-cirrhotic NASH with moderate-to-advanced liver fibrosis resulted in a moderate increase in budget impact with declining costs related to NASH progression.

Targeting Community-Based Palliative and Serious Illness Care Resources: Challenges of More Stringent Diagnostic Criteria for Prospective Enrollment

Background: For patients with serious illnesses, one aim of palliative care services is to reduce the frequency and severity of hospital-based episodes of care. Since hospital-alternative palliative care may consume costly resources, providers need to efficiently target high-intensity services toward those most at risk for such adverse episodes of care. Objective: Our objective was to investigate progressively more restrictive diagnosis-based indications of serious illness as used to prospectively identify patients with higher average rates of hospitalization. Design/Setting: We designed an observational cohort quality improvement study using historical Medicare claims records to evaluate diagnostic inclusion criteria for targeting palliative and serious illness care resources. We first isolated a Seriously Ill Population (SIP) and then defined More SIP and Most SIP subgroups. Measurements: Our primary outcome measure was the 2019 acute-care count of hospitalizations for patients in the SIP, More SIP, and Most SIP subgroups, respectively. Results: The More SIP and Most SIP subgroups exhibited higher hospitalization rates. However, they also excluded progressively more seriously ill patients who did experience hospitalizations. In addition, almost half of the Most SIP subgroup were not hospitalized at all, despite having an average hospitalization rate greater than one. Conclusion:Allocating resources (personnel and services) toward reducing hospitalizations when almost half of the targeted population never goes to the hospital could result in unnecessary expenditures and exclude patients that could potentially benefit. Engaging community-based services to detect changes in status could provide supplemental indications of when and for whom to target palliative care resources.

Development and validation of a tool to predict high-need, high-cost patients hospitalised with ischaemic heart disease

OBJECTIVE

To develop and validate a tool to predict patients with ischaemic heart disease (IHD) at risk of excessive healthcare resource utilisation.

DESIGN

A retrospective cohort study.

SETTING

We identified patients through the State of Florida Agency for Health Care Administration (N=586 518) inpatient dataset.

PARTICIPANTS

Adult patients (at least 40 years of age) admitted to the hospital with a diagnosis of IHD between 1 January 2007 and 31 December 2016.

PRIMARY OUTCOME MEASURES

We identified patients whose healthcare utilisation is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individual as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalisation), cost and hospital length of stay. Discrimination power, prediction accuracy and model improvement for the binary logistic model were assessed using receiver operating characteristic statistic, the Brier score and the log-likelihood (LL)-based pseudo-R2, respectively. LL-based pseudo-R2 and Brier score were used for multinomial logistic models.

RESULTS

The binary logistic model had good discrimination power (c-statistic=0.6496), an accuracy of probabilistic predictions (Brier score) of 0.0621 and an LL-based pseudo-R2 of 0.0338 in the development cohort. The model performed similarly in the validation cohort (c-statistic=0.6480), an accuracy of probabilistic predictions (Brier score) of 0.0620 and an LL-based pseudo-R2 of 0.0380. A user-friendly Excel-based HNHC risk predictive tool was developed and readily available for clinicians and policy decision-makers.

CONCLUSIONS

The Excel-based HNHC risk predictive tool can accurately identify at-risk patients for HNHC based on three measures of healthcare expenditures.

Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study

BACKGROUND

Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers.

OBJECTIVE

This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes.

METHODS

This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques: decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public.

RESULTS

Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group.

CONCLUSIONS

We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group.

Characteristics of High-Need, High-Cost Patients : A "Best-Fit" Framework Synthesis

BACKGROUND

Accurately identifying high-need, high-cost (HNHC) patients to reduce their preventable or modifiable health care use for their chronic conditions is a priority and a challenge for U.S. policymakers, health care delivery systems, and payers.

PURPOSE

To identify characteristics and criteria to distinguish HNHC patients.

DATA SOURCES

Searches of multiple databases and gray literature from 1 January 2000 to 22 January 2022.

STUDY SELECTION

English-language studies of characteristics and criteria to identify HNHC adult patients, defined as those with high use (emergency department, inpatient, or total services) or high cost.

DATA EXTRACTION

Independent, dual-review extraction and quality assessment.

DATA SYNTHESIS

The review included 64 studies comprising multivariate exposure studies (n = 47), cluster analyses (n = 11), and qualitative studies (n = 6). A National Academy of Medicine (NAM) taxonomy was an initial "best-fit" framework for organizing the synthesis of the findings. Patient characteristics associated with being HNHC included number and severity of comorbid conditions and having chronic clinical conditions, particularly heart disease, chronic kidney disease, chronic lung disease, diabetes, cancer, and hypertension. Patients' risk for being HNHC was often amplified by behavioral health conditions and social risk factors. The reviewers revised the NAM taxonomy to create a final framework, adding chronic pain and prior patterns of high health care use as characteristics associated with an increased risk for being HNHC.

LIMITATION

Little evidence distinguished potentially preventable or modifiable health care use from overall use.

CONCLUSION

A combination of characteristics can be useful for identifying HNHC patients. Because of the complexity of their conditions and circumstances, improving their quality of care will likely also require an individualized assessment of care needs and availability of support services.

PRIMARY FUNDING SOURCE

Agency for Healthcare Research and Quality. (PROSPERO: CRD42020161179).