METHODS: The authors obtained data on volumes and reimbursement rates for the most common 25 tests at the five hospitals with which they are affiliated and organized them to be as comparable as possible. Simple descriptive statistics were used to make cross-country comparisons.
RESULTS: There are strong similarities across all five hospitals in the top five tests by both volume and revenue. However, the top five by volume differ from the top five by revenue. Reimbursement rates also follow common patterns, being lowest for the most common biochemical test; intermediate for the most common hematology and microbiology tests, respectively; and highest for the most common pathology test.
CONCLUSIONS: Most of the most common tests also appear in the new Essential Diagnostics List. This may inform plans for universal health coverage.
METHODS: This study employs a quantitative method by way of a cross-sectional approach. The 2018 JKN claims data, drawn from a 1% sample that JKN annually produces, were analyzed. Nine hundred forty-five HIV patients out of 1,971,744 members were identified in the data sample and their claims record data at primary care and hospital levels were analyzed. Using ICD (International Statistical Classification of Diseases and Related Health Problems), 10 codes (i.e., B20, B21, B22, B23, and B24) that fall within the categories of HIV-related disease. For each level, patterns of service utilization by patient-health status, discharge status, severity level, and total cost per claim were analyzed.
RESULTS: Most HIV patients (81%) who first seek care at the primary-care level are referred to hospitals. 72.5% of the HIV patients receive antiretroviral treatment (ART) through JKN; 22% at the primary care level; and 78% at hospitals. The referral rate from public primary-care facilities was almost double (45%) that of private providers (24%). The most common referral destination was higher-level hospitals: Class B 48%, and Class C 25%, followed by the lowest Class A at 3%. Because JKN pays hospitals for each inpatient admission, it was possible to estimate the cost of hospital care. Extrapolating the sample of hospital cases to the national level using the available weight score, it was estimated that JKN paid IDR 444 billion a year for HIV hospital services and a portion of capitation payment.
CONCLUSION: There was an underrepresentation of PLHIV (People Living with HIV) who had been covered by JKN as 25% of the total PLHIV on ART were able to attain access through other schemes. This study finding is principally aligned with other local research findings regarding a portion of PLHIV access and the preferred delivery channel. Moreover, the issue behind the underutilization of National Health Insurance services in Indonesia among PLHIV is similar to what was experienced in Vietnam in 2015. The 2015 Vietnam study showed that negative perception, the experience of using social health insurance as well as inaccurate information, may lead to the underutilization problem (Vietnam-Administration-HIV/AIDSControl, Social health insurance and people living with HIV in Vietnam: an assessment of enrollment in and use of social health insurance for the care and treatment of people living with HIV, 2015). Furthermore, the current research finding shows that 99% of the total estimated HIV expenditure occurred at the hospital. This indicates a potential inefficiency in the service delivery scheme that needs to be decentralized to a primary-care facility.
METHODS: Medical claims records from February 2019 to February 2020 were extracted from a health insurance claims database. Data cleaning and data analysis were performed using Python 3.7 with the Pandas, NumPy and Matplotlib libraries. The top five most common diagnoses were identified, and for each diagnosis, the most common medication classes and medications prescribed were quantified. Potentially inappropriate prescribing practices were identified by comparing the medications prescribed with relevant clinical guidelines.
KEY FINDINGS: The five most common diagnoses were upper respiratory tract infection (41.5%), diarrhoea (7.7%), musculoskeletal pain (7.6%), headache (6.7%) and gastritis (4.0%). Medications prescribed by general practitioners were largely as expected for symptomatic management of the respective conditions. One area of potentially inappropriate prescribing identified was inappropriate antibiotic choice. Same-class polypharmacy that may lead to an increased risk of adverse events were also identified, primarily involving multiple paracetamol-containing products, non-steroidal anti-inflammatory drugs (NSAIDs), and antihistamines. Other areas of non-adherence to guidelines identified included the potential overuse of oral corticosteroids and oral salbutamol, and inappropriate gastroprotection for patients receiving NSAIDs.
CONCLUSIONS: While prescribing practices are generally appropriate within the private primary care sector, there remain several areas where some potentially inappropriate prescribing occurs. The areas identified should be the focus in continuing efforts to improve prescribing practices to obtain the optimal clinical outcomes while reducing unnecessary risks and healthcare costs.
METHODS: Through the Association of Southeast Asian Nations Costs in Oncology study, 1,294 newly diagnosed patients with cancer (Ministry of Health [MOH] hospitals [n = 577], a public university hospital [n = 642], private hospitals [n = 75]) were observed in Malaysia. Cost diaries and questionnaires were used to measure incidence of financial toxicity, encompassing financial catastrophe (FC; out-of-pocket costs ≥ 30% of annual household income), medical impoverishment (decrease in household income from above the national poverty line to below that line after subtraction of cancer-related costs), and economic hardship (inability to make necessary household payments). Predictors of financial toxicity were determined using multivariable analyses.
RESULTS: One fifth of patients had private health insurance. Incidence of FC at 1 year was 51% (MOH hospitals, 33%; public university hospital, 65%; private hospitals, 72%). Thirty-three percent of households were impoverished at 1 year. Economic hardship was reported by 47% of families. Risk of FC attributed to conventional medical care alone was 18% (MOH hospitals, 5%; public university hospital, 24%; private hospitals, 67%). Inclusion of expenditures on nonmedical goods and services inflated the risk of financial toxicity in public hospitals. Low-income status, type of hospital, and lack of health insurance were strong predictors of FC.
CONCLUSION: Patients with cancer may not be fully protected against financial hardships, even in settings with universal health coverage. Nonmedical costs also contribute as important drivers of financial toxicity in these settings.
METHODS: Subsidised and self-paying patients were identified at public and private healthcare institutions in three states of Malaysia. Patients were then purposively selected for semi-structured, face-to-face interviews according to their medication adherence status (including adherent and non-adherent patients), which was measured using the Medication Event Monitoring System (MEMS). Adherence was defined as having 80% or more for the percentage of days in which the dose regimen was executed as prescribed. The interview was conducted from January to August 2016 and during the interviews, patients were asked to provide reasons for their medication adherence or non-adherence. The patient interviews were audio recorded and transcribed verbatim. Data were analysed using thematic analysis with NVivo 11 software.
RESULTS: Thirteen subsidised and 12 self-paying patients were interviewed. The themes found among subsidised and self-paying patients were similar. The factors that influenced adherence to medication include the 'perceived importance of quality of life' and 'perceived benefit or value of the medications'. A unique factor reported by patients in this study included 'perceived value of the money spent on medications'; more specifically, patients adhered to their medications because they valued the money spent to buy/receive the medications.
CONCLUSION: Medication adherence among subsidised and self-paying patients was influenced by many factors, including a unique factor relating to their perceptions of the value of money spent on medications.
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.