Distinguishing moral hazard from access for high-cost healthcare under insurance

Incidence of moral hazards among health care providers in the implementation of social health insurance toward universal health coverage: evidence from rural province hospitals in Indonesia

Objective

To identify the incidence of moral hazards among health care providers and its determinant factors in the implementation of national health insurance in Indonesia.

Methods

Data were derived from 360 inpatient medical records from six types C public and private hospitals in an Indonesian rural province. These data were accumulated from inpatient medical records from four major disciplines: medicine, surgery, obstetrics and gynecology, and pediatrics. The dependent variable was provider moral hazards, which included indicators of up-coding, readmission, and unnecessary admission. The independent variables are Physicians' characteristics (age, gender, and specialization), coders' characteristics (age, gender, education level, number of training, and length of service), and patients' characteristics (age, birth weight, length of stay, the discharge status, and the severity of patient's illness). We use logistic regression to investigate the determinants of moral hazard.

Results

We found that the incidences of possible unnecessary admissions, up-coding, and readmissions were 17.8%, 11.9%, and 2.8%, respectively. Senior physicians, medical specialists, coders with shorter lengths of service, and patients with longer lengths of stay had a significant relationship with the incidence of moral hazard.

Conclusion

Unnecessary admission is the most common form of a provider's moral hazard. The characteristics of physicians and coders significantly contribute to the incidence of moral hazard. Hospitals should implement reward and punishment systems for doctors and coders in order to control moral hazards among the providers.

Does an Overcrowded Emergency Department Reduce Moral Hazard? Lessons from Emergency Department Visits to Three Hospitals in an Israeli Metropolitan Area

Metropolitan Haifa, Israel, has three hospitals: Rambam Health Care Campus, Bnai Zion Medical Center, and Carmel Medical Center. In 2007-2014, the length of stay at RHCC's emergency department increased, while the number of visits decreased. We ask whether the increase in LOS is associated with the falling numbers of visits to other EDs, whether an increase in LOS induces more referrals to competing hospitals in the metropolitan area, and whether it pays to be a crowded ED in mitigating moral hazard. Average LOS at Rambam climbed from 3.5 h in 2000-2007 to 6.4 in 2008-2018. While the number of visits to Rambam decreased significantly, those to Bnai Zion increased significantly and quite linearly. A one-way ANOVA test reveals a statistically significant difference among the three hospitals. In addition, Rambam was significantly different from Carmel but not from Bnai Zion. When LOS stabilized at Rambam from 2016 to 2018 and increased at Bnai Zion, referrals to Rambam went up again. Policymakers should instruct all hospitals to publish LOS data, regulate referrals to EDs, and find an optimal LOS that will reduce competition, non-urgent visits, and moral hazard.

Jump in Elective Total Hip and Knee Arthroplasty Numbers at Age 65 Years: Evidence for Moral Hazard?

Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are outstanding options for many older patients at the discretion of the patient and the surgeon. As patients turn 65 years, greater than 98% of the US population becomes eligible for Medicare, and this represents a time of changing healthcare coverage for many.

Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

BACKGROUND

Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes.

METHODS

This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period.

RESULTS

Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001).

CONCLUSIONS

ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.