ABSTRACT
Introduction: Professional indemnity (PI) or medical malpractice insurance (MMI) has been a hot topic considering the increasing number of medical negligence cases rising worldwide. However, there is a palpable difference in understanding and usage of this tool in developed countries and regions such as India. Aim: This study aimed to analyze the general understanding of resident doctors and consultants about MMI and knowledge about its technical jargon. Materials and Methods: We distributed short Google Form questionnaires about various aspects of MMI. We recorded the data from 141 resident doctors and 42 consultants in the Navi Mumbai area of India. As it was a survey, we required no ethical review. Results: As consultants’ experience grew, so did their understanding of medical indemnity. Approximately 90%, 64%, and 22% of consultants with 10 years, 5–10 years, and 5 years of experience had acquired PI. The AOY:AOT (any one year:anyone time) ratio was known to just 35% of these specialists. About half of the resident doctors were aware of PI and the effects of medical specialization on PI. Around a fifth of the individuals had only acquired the PI. Conclusion: There needs to be more clarity between the need and knowledge of MMI in India. This needs to be addressed by teaching medical postgraduates about it during training. “There should be special emphasis on medical indemnity in terms of its need, clauses, and cost during postgraduate medical training.”
ABSTRACT
Medical claims database is an important source of data for studying the characteristics, and burden of diseases, to provide a basis for the development of policy on management. The database is usually used to identify patients through International Classification of Diseases and free text-building algorithms, thus it is crucial to validate whether the algorithm is correctly identifing the targeted population. This paper introduces both traditional and emerging validation methods including machine learning, natural language processing and database linkage etc.. We also have tried to present a suitable validation method for the current situation in China, so as to promote the application of big data in medical areas and to provide reference for epidemiology studies, based on medical claims database in this country.
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BACKGROUND: This study aims to analyze the behavioral changes of healthcare providers and influencing factors after the reviewer unification of auto insurance medical benefit claims by an independent review agency. METHODS: The comparison data were collected from the second half of 2013 and the same period of 2014. The key indicators are the number of admission days, the number of outpatient visits, inpatient ratio, inpatient medical expenses, and outpatient medical expenses. RESULTS: Four indicators (number of admission days, number of outpatient visits, inpatient ratio, and outpatient medical expenses) showed statistically significant drops, while one indicator (inpatient medical expenses) showed no significant change. CONCLUSION: The reviewer unification of auto insurance medical benefit claims by an independent review agency showed significant reduction in cost and patient days.
Subject(s)
Humans , Health Personnel , Inpatients , Insurance , OutpatientsABSTRACT
<b>Objective</b>: Medical information databases provide useful Real World Evidence (RWE) and a comprehensive view of medical activities. However, since each database has limited coverage and cannot be self-sufficient, combining information from multiple databases is a useful research technique. In this study, we examined methods of estimating patient numbers by combining information from multiple databases in order to assess the respective databases and identify the respective characteristics, biases and idiosyncrasies. This process also allowed us to propose improvements in the grand design of medical information databases in Japan.<br><b>Design</b>: Retrospective observational cohort study<br><b>Methods</b>: We attempted to estimate the numbers of patients treated for certain diseases and the numbers prescribed a drug by three methods: i) We estimated patient numbers for seven diseases using an insurance claims database, adjusting the proportion of elderly patients according to a hospital medical records database; ii) Sales information for drug X was combined with the prescribed volume per person estimated from pharmacy claims databases to estimate the number of patients administered X; this number was divided by the prescription rate obtained from a medical claims database to calculate patient numbers for the associated disease; and iii) We examined two surveys of the National Institute of Infectious Diseases (NIID) for timely estimation of patient numbers for influenza, referring to estimates from an insurance claims database.<br><b>Results</b>: In Method i)-iii), we proved that it is possible to estimate patient numbers for many diseases and administered drugs by effectively combining multiple medical information databases. Validation could be claimed when multiple methods lead to similar results.<br><b>Conclusion</b>: These databases provided by government agencies and private corporations are separately managed, and there is no grand plan to integrate them into one platform. It is crucial that databases, rather than being designed to stand alone, are standardized according to widely used systems under a solid master data management strategy. This will make it easier to combine information from multiple databases and to maximize their values. Mutual use of these databases by academic researchers for epidemiological and clinical studies and by government policy makers and data scientists of pharmaceutical companies may improve the usefulness of these databases and expand their application in research.