Predictive modeling of health care costs: do cardiovascular risk markers improve prediction?

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

Background

The increasing rate of health care expenditures in the United States has placed a significant burden on the nation’s economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation.

Objective

This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups.

Methods

In the HealthInfoNet, Maine’s health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient’s next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree–based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013.

Results

Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine.

Conclusions

The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

Direct medical costs of hospitalizations for cardiovascular diseases in Shanghai, China: trends and projections

Few studies in China have focused on direct expenditures for cardiovascular diseases (CVDs), making cost trends for CVDs uncertain. Epidemic modeling and forecasting may be essential for health workers and policy makers to reduce the cost burden of CVDs.To develop a time series model using Box-Jenkins methodology for a 15-year forecasting of CVD hospitalization costs in Shanghai.Daily visits and medical expenditures for CVD hospitalizations between January 1, 2008 and December 31, 2012 were analyzed. Data from 2012 were used for further analyses, including yearly total health expenditures and expenditures per visit for each disease, as well as per-visit-per-year medical costs of each service for CVD hospitalizations. Time series analyses were performed to determine the long-time trend of total direct medical expenditures for CVDs and specific expenditures for each disease, which were used to forecast expenditures until December 31, 2030.From 2008 to 2012, there were increased yearly trends for both hospitalizations (from 250,354 to 322,676) and total costs (from US $ 388.52 to 721.58 million per year in 2014 currency) in Shanghai. Cost per CVD hospitalization in 2012 averaged US $ 2236.29, with the highest being for chronic rheumatic heart diseases (US $ 4710.78). Most direct medical costs were spent on medication. By the end of 2030, the average cost per visit per month for all CVDs was estimated to be US $ 4042.68 (95% CI: US $ 3795.04-4290.31) for all CVDs, and the total health expenditure for CVDs would reach over US $1.12 billion (95% CI: US $ 1.05-1.19 billion) without additional government interventions.Total health expenditures for CVDs in Shanghai are estimated to be higher in the future. These results should be a valuable future resource for both researchers on the economic effects of CVDs and for policy makers.

Identifying Patients at Risk for High Medical Costs and Good Candidates for Obesity Intervention

Purpose.

To develop a risk-scoring tool to identify in a base year patients likely to have high medical spending in the subsequent year and to understand the role obesity and obesity reduction may play in mitigating this risk.

Design.

Cross-sectional analysis, using commercial claims and health risk assessment data.

Setting.

United States, 2004–2009.

Subjects.

Panel of 192,750 person-year observations from 116,868 unique working-age employees of large companies.

Measures.

Probability of high medical expenses (80th percentile or above) in the following year; adjusted body mass index (BMI).

Analysis.

Generate risk scores by modeling the likelihood of high next-year expenses as a function of base-year age, sex, medical utilization, comorbidities, and BMI. Estimate the effect of simulated bariatric intervention on patient risk scores.

Results.

Individuals with higher BMI were more likely to be categorized in the very high risk group, in which the average annual medical expense was $8621. A weight-loss intervention transitioning a patient to the next lower obesity class was predicted to reduce this risk by 1.5% to 27.4%—comparable to hypothetically curing a patient of depression or type 2 diabetes.

Conclusion.

A logistic model was used to capture the effect of BMI on the risk of high future medical spending. Weight-loss interventions for obese patients may generate significant savings by reducing this risk.

Impact of comorbidity on medication use in elderly patients with cardiovascular diseases: the OCTOCARDIO study

BACKGROUND

Recommended medications are under-prescribed in elderly patients with atrial fibrillation (AF), coronary artery disease (CAD), and congestive heart failure (CHF). The relationship between under-prescribing and comorbidity is unclear.

DESIGN

Single-day observational study.

METHODS

Analysis of medications taken by patients aged 80 years or over at the time of their admission to cardiology units of 32 French hospitals. Comorbidity was measured using the Charlson comorbidity index (CCI).

RESULTS

The study included 510 patients (57% men, mean age 85 years). History of AF, CHF, and CAD was present in 213 (42%), 199 (39%), and 187 (37%) patients, respectively. CCI was 0 in 110 (22%), 1-2 in 215 (42%), and ≥3 in 185 (36%) patients. Vitamin K antagonists (VKA) were prescribed to 105 (49%) and aspirin to 86 (40%) patients with AF. CCI did not influence VKA prescription but influenced aspirin use, with lower prescription rates in patients with CCI 1-2 than CCI 0 or CCI ≥3 (p = 0.02). In CHF, angiotensin-converting enzyme inhibitors (ACEI) and β-blockers were prescribed to 80 (40%) and 96 (48%) patients, respectively. Rates of prescription of ACEI, β-blockers, statins, and aspirin in patients with CAD were 43%, 56%, 56%, and 66%, respectively. CCI level did not influence any medication use in CHF and CAD.

CONCLUSION

Even in the absence of comorbidity, elderly patients with major cardiovascular diseases are denied from indicated medical treatments probably because of their age alone. Implementing measures to enhance awareness of treatment benefits and promote appropriate prescribing is necessary.