Medical Insurance

Objective: A patient medical insurance coverage plays an essential role in determining the post-acute care (PAC) discharge disposition. The prior health insurance authorization process postpones the PAC discharge disposition, increases the inpatient length of stay, and effects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior health insurance authorization, the inpatient length of stay and inpatient stay expenses. Methodology: We conducted a group discussion involving 25 patient care facilitators (PCFs) and two registered nurses (RNs) and retrieved 1600 patient data records from the initial nursing assessment and discharge notes to conduct a retrospective analysis of PAC discharge dispositions using predictive analytics. Results: The chi-squared automatic interaction detector (CHAID) algorithm enabled the early prediction of the PAC discharge disposition, accelerated the prior ...

Objective: A patient’s medical insurance coverage plays an essential role in determining the post-acute care (PAC) discharge disposition. The prior health insurance authorization process postpones the PAC discharge disposition, increases the inpatient length of stay, and effects patient health. Our study implements predictive analytics for the early prediction of the PAC discharge disposition to reduce the deferments caused by prior health insurance authorization, the inpatient length of stay and inpatient stay expenses. Methodology: We conducted a group discussion involving 25 patient care facilitators (PCFs) and two registered nurses (RNs) and retrieved 1600 patient data records from the initial nursing assessment and discharge notes to conduct a retrospective analysis of PAC discharge dispositions using predictive analytics. Results: The chi-squared automatic interaction detector (CHAID) algorithm enabled the early prediction of the PAC discharge disposition, accelerated the prio...

The last two decades have witnessed an increasing commercialization and weakening of public health provisioning. As a result, the cost of medical care has risen and lack of financial resources has become an important reason for majority of Indians to not seek health care. Clearly, it is those who are socially and economically marginalized that are the most affected. In the industrial sector, it is the informal sector employees who are excluded the most, due to the insecure nature of their work, the lack of social security and health benefits. One of the ways in which to address the problem of financial barriers for accessing health care has been through the evolution of the Community Based Medical Insurance Scheme. There has been a plurality of these schemes, each shaped by different socio-political context.
The present paper is based on my M.Phil dissertation study that looked at the role of the workers’ union in promoting a medical insurance scheme among informal waste pickers. It explores the experiences of waste pickers who have been beneficiaries of the medical insurance scheme of the Pune Municipality of Maharashtra. In-depth interviews with the key informants and waste pickers were undertaken with the objective of analyzing the experiences of beneficiaries with hospitalization, access to health care, cost of treatment, insurance claim procedures and reimbursement of money. The findings revealed that a weak and inefficient public health system compelled poor waste pickers to avail private health care services that are expensive resulting in an increasing indebtedness. The medical insurance is a reimbursement scheme and it was found that the workers are getting very minimal benefits that are not enough to fully cover medical costs. Therefore, even though workers have a medical insurance scheme they are still in debt due to medical expenses. Workers experiences revealed that some financial burden would be reduced if the scheme was based on a cashless model and if the benefit amount was increased.
This study showed that the reimbursement model of an insurance scheme does not reduce financial burden. A medical insurance scheme may increase access to health care or reduce financial burden to some extent if it is a cashless model of insurance. However, in the context of public health, medical insurance schemes do not fulfill the health needs of the poor and raise the serious question: are insurance schemes actually a process by which the private sector is being strengthened?

Objective: Hospital deferments for patients squared to post-acute care (PAC) are lengthier and expensive than routine discharges. Patient’s medical insurance coverage plays a desperate role in determining their PAC discharge disposition. At Unity Point Health, retrieving a patient’s insurance coverage information accedes the PAC discharge disposition process by four days. In this study, we implement predictive analytics for early prediction of PAC discharge disposition to foster the needed care, in the suitable location, just in time. Methodology: To study the existing PAC discharge disposition method at Unity Point Health we conducted a group discussion involving twenty-five patient care facilitators (PCF) and two registered nurses (RN). We manually retrieved sixteen hundred patient’s data (July 2018 through August 2018) from discharge notes and initial nursing assessment to conduct a retrospective analysis of PAC discharge disposition. The analysis is limited to the patients discharged to AR, or SNF. We employed predictive analytics to develop a clinical decision support system (CDSS) that can efficiently identify patients eligible for AR and SNF by the first day of their inpatient stay. All evaluations were conducted using the SPSS Modeler, RStudio, Microsoft Visio and Excel. Results: Chi-Squared Automatic Interaction Detector (CHAID) algorithm was selected to be the best fit model with an (a) overall accuracy of 84.16%, and (b) the area under the receiver operating characteristic (ROC) curve of 0.81. Conclusion: CHAID algorithm is recommended to develop CDSS that can steer early prediction of PAC discharge disposition and thus minimize inpatient length of stay. Early prediction of PAC discharge disposition enabled UnityPoint-Health in reducing inpatient length of stay by forty-four percent and recuperated patient flow.

Health is a very important part of a life. In India, government assisted healthcare services and private health care services are providing health care services. Medical health insurance policies in an important part of Indian health care system. In this paper, we discuss Indian medical health care system in detail. We also suggest the steps to improve the accessibility and quality of these medical insurance policies.