1.1 Introduction
Healthcare risk professionals play a key strategic role as they hold responsibility for not only preventing situations but also for executing damage control. Risk management is essential in highly regulated industries like healthcare given human lives are at stake. With healthcare in constant flux as new regulations are published, the rise of weather and climate disasters, and the effect of technology, Risk Managers need to focus on these five priorities (PolicyMedical, 2021).
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Research hypothesis and questions, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
The business of risk adjustment has come a long way since the publication of the Academy’s “Monograph Number One” with the title, “Health Risk Assessment and Health Risk Adjustment - Crucial Elements in Effective Health Care Reform” in May 1993. Less than ten years later, we had hospital inpatient diagnosis-based approaches, such as the model used by the Market Stabilization Pool for small group and individual coverage in NYS in conjunction with mandated community rating. The PIP-DCG approach for Medicare + Choice, also inpatient only, soon followed.
Risk adjustment models have included variables such as demographic (i.e. age and gender) and clinical markers based either on ICD-9 diagnosis codes and/or pharmacy codes such as the National Drug Codes (NDCs). Literature points to other variables such as geography, Body Mass Index (BMI), education, and income that also explain the variation in healthcare cost – but have hitherto not been included in risk adjustment programs mainly because such variables are not typically found in claim data. If these nontraditional variables explain meaningful variation in cost beyond traditional risk adjustment models – then this may provide incentives for issuers to select certain members. If such incentives lead to selection that affects the financial performance of issuers – then the policy goals of the risk adjustment program will be undermined. Recognizing the importance of fortifying risk adjustment programs against selection based on nontraditional variables, the Society of Actuaries’ Health Section sponsored an in-depth study into the relationship of nontraditional variables with health costs. This report presents the results of this study. We used the Medical Expenditure Panel Survey (MEPS) data in this research. Specific details concerning the data and preparation can be found in Section 3.2. This data is unique in that it includes a large number of individual characteristics (from BMI to whether a person has difficulty enjoying hobbies) together with healthcare claim data. There are limitations to the use of MEPS data, and these limitations are discussed further in Section 4. The results of this research demonstrate that it is important to adjust the traditional risk adjustment model in order to recognize nontraditional variables. The report develops a new measure (Loss Ratio Advantage or LRA) to help quantify the potential of a nontraditional variable to affect a risk adjustment program. With the help of this measure, the report compares the importance of over thirty variables that were systematically narrowed down from a list of over fifteen hundred variables describing various characteristics of the general population (i.e. The purchasers of healthcare insurance coverage). The nontraditional variables were broadly categorized into demographic, economic, lifestyle, psychological self-assessment (i.e. how a person feels about their mental health), and physical self-assessment.
Therefore, in Uth, Uyo, Akwa Ibom where the research was carried out, the activities that was conducted is to know the Impact of Non-traditional Variables in Health Care Risk Adjustment.
1.3 Statement of the Problem
Risk adjustment of any kind is inherently imperfect, the complexity and sophistication of risk adjustment models has increased significantly in the past couple decades. With the passage of the Affordable Care Act (ACA), risk adjustment will be required for non-grandfathered commercial small group and individual coverage both inside and outside Exchanges. Using a structured and scientific approach, the researcher has examined a long list of non-traditional drivers of health cost, chosen the most relevant ones, and tested their effect on bottom-line medical cost when included in the traditional risk adjustment formula.
1.4 Aim and Objectives of the Study
The aim of the study is to examine the Impact of Non-traditional Variables in Health Care Risk Adjustment using Uth, Uyo, Akwa Ibom as a case study. In achieving this aim, the following specific objectives were laid out as follows:
- To determine the relationship between non-traditional variables and health care risk adjustment in Nigeria.
- To ascertain the impact of non-traditional variables on health care risk adjustment in Nigeria.
1.5 Research Questions
The study came up with research questions so as to be able to ascertain the above stated objectives. The specific research questions for the study are stated below as follows:
- Is there a relationship between non-traditional variables and health care risk adjustment in Nigeria?
- Does non-traditional variables significantly impacts on health care risk adjustment in Nigeria?
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