Health Expenditure Concentration and Characteristics of High-Cost Enrollees in CHIP

Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from comorbidity, the cost of treating such a patient becomes dependent on the nature of the comorbidity. Therefore, lack of recognition of comorbidity on asthmatic patient poses a challenge in predicting the cost of treatment. In this study, we proposed a comorbidity portfolio design that improves the prediction cost of treating asthmatic patients by regrouping frequently occurred comorbidities in different cost groups. In the experiment, predictive models, including logistic regression, random forest, support vector machine, classification regression tree, and backpropagation neural network were trained with real-world data of asthmatic patients from 2012 to 2014 in a large city of China. The 10-fold cross validation and random search algorithm were employed to optimize the hyper-parameters. We recorded significant improvements using our model, which are attributed to comorbidity portfolios in area under curve (AUC) and sensitivity increase of 46.89% (standard deviation: 4.45%) and 101.07% (standard deviation: 44.94%), respectively. In risk analysis of comorbidity on cost, respiratory diseases with a cumulative proportion in the adjusted odds ratio of 36.38% (95%CI: 27.61%, 47.86%) and circulatory diseases with a cumulative proportion in the adjusted odds ratio of 23.83% (95%CI: 15.95%, 35.22%) are the dominant risks of asthmatic patients that affects the treatment cost. It is found that the comorbidity portfolio is robust, and provides a better prediction of the high-cost of treating asthmatic patients. The preliminary characterization of the joint risk of multiple comorbidities posed on cost are also reported. This study will be of great help in improving cost prediction and comorbidity management.

An Asthma Population Health Improvement Initiative for Children With Frequent Hospitalizations

OBJECTIVES

A relatively small proportion of children with asthma account for an outsized proportion of health care use. Our goal was to use quality improvement methodology to reduce repeat emergency department (ED) and inpatient care for patients with frequent asthma-related hospitalization.

METHODS

Children ages 2 to 17 with ≥3 asthma-related hospitalizations in the previous year who received primary care at 3 in-network clinics were eligible to receive a bundle of 4 services including (1) a high-risk asthma screener and tailored education, (2) referral to a clinic-based asthma community health worker program, (3) facilitated discharge medication filling, and (4) expedited follow-up with an allergy or pulmonology specialist. Statistical process control charts were used to estimate the impact of the intervention on monthly 30-day revisits to the ED or hospital. We then conducted a difference-in-differences analysis to compare changes between those receiving the intervention and a contemporaneous comparison group.

RESULTS

From May 1, 2016, to April 30, 2017, we enrolled 79 patients in the intervention, and 128 patients constituted the control group. Among the eligible population, the average monthly proportion of children experiencing a revisit to the ED and hospital within 30 days declined by 38%, from a historical baseline of 24% to 15%. Difference-in-differences analysis demonstrated 11.0 fewer 30-day revisits per 100 patients per month among intervention recipients relative to controls (95% confidence interval: -20.2 to -1.8; P = .02).

CONCLUSIONS

A multidisciplinary quality improvement intervention reduced health care use in a high-risk asthma population, which was confirmed by using quasi-experimental methodology. In this study, we provide a framework to analyze broader interventions targeted to frequently hospitalized populations.

Assessing the Quality Measure for Follow-up Care After Children's Psychiatric Hospitalizations

OBJECTIVES

Medicaid and Children's Health Insurance Program plans publicly report quality measures, including follow-up care after psychiatric hospitalization. We aimed to understand failure to meet this measure, including measurement definitions and enrollee characteristics, while investigating how follow-up affects subsequent psychiatric hospitalizations and emergency department (ED) visits.

METHODS

Administrative data representing Alabama's Children's Health Insurance Program from 2013 to 2016 were used to identify qualifying psychiatric hospitalizations and follow-up care with a mental health provider within 7 to 30 days of discharge. Using relaxed measure definitions, follow-up care was extended to include visits at 45 to 60 days and visits to a primary care provider. Logit regressions estimated enrollee characteristics associated with follow-up care and, separately, the likelihood of subsequent psychiatric hospitalizations and/or ED visits within 30, 60, and 120 days.

RESULTS

We observed 1072 psychiatric hospitalizations during the study period. Of these, 356 (33.2%) received follow-up within 7 days and 566 (52.8%) received it within 30 days. Relaxed measure definitions captured minimal additional follow-up visits. The likelihood of follow-up was lower for both 7 days (-18 percentage points; 95% confidence interval [CI] -26 to -10 percentage points) and 30 days (-26 percentage points; 95% CI -35 to -17 percentage points) regarding hospitalization stays of ≥8 days. Meeting the measure reduced the likelihood of subsequent psychiatric hospitalizations within 60 days by 3 percentage points (95% CI -6 to -1 percentage point).

CONCLUSIONS

Among children, receipt of timely follow-up care after a psychiatric hospitalization is low and not sensitive to measurement definitions. Follow-up care may reduce the need for future psychiatric hospitalizations and/or ED visits.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

Background

High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Methods

The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.

Results

A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.

Conclusion

HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4239-2) contains supplementary material, which is available to authorized users.

Machine learning approaches for predicting high cost high need patient expenditures in health care

BACKGROUND

This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.

RESULTS

We systematically tests temporal correlation of patient-level health care expenditures in both the short and long terms. The results suggest that medical expenditures are significantly correlated over multiple periods. Our work demonstrates a prevalent and strong temporal correlation and shows promise for predicting future health care expenditures using machine learning. Temporal correlation is stronger in HCHN patients and their expenditures can be better predicted. Including more past periods is beneficial for better predictive performance.

CONCLUSIONS

This study shows that there is significant temporal correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently.