Question

How does a healthcare organization benefit in the planning process when forecasting for the future operation?

Answer 1

INTRODUCTION:-Three related fields–healthcare forecasting, risk adjustment, and actuarial science–use similar statistical techniques to predict the future behavior of patients, but for different reasons. Healthcare forecasting generally implies predicting an individual's costs or healthcare utilization for interventional purposes such as proactive disease management, patient education, or surveillance to promote population health. Risk adjustment refers to determining comparability between patient populations based on current or predicted healthcare utilization or expenditures. It is most often used to compensate health plans or physician groups fairly based on their patients'

morbidity. Actuaries use similar statistical methods to set insurance premiums and to determine coverage eligibility.


Previous literature has explored the uses and pitfalls of actuarial techniques and risk adjustment³; this article instead focuses on the use of healthcare forecasting to intervene (eg, using disease management) with individual patients at risk of high costs or suboptimal outcomes.

A schematic approach to health forecasting

A framework for health forecasting is an essential guide. It is, however, uncommon in the literature and so the following framework, which presents a summary of the key processes involved in developing a general health forecasting ervice, is steps help to identify and broadly define the needs and tools of health forecasting. Further, they state the key processes involved in developing and perfecting a health forecasting scheme over time. Thus, the framework demonstrates a dynamic process in which the forecast models created at any time would be continuously improved to meet the purpose of the forecast or the client’s needs.

Step 1:

Identify the concepts and ideas that address an important health condition of great burden and ones that significantly cost the health service. Provide a precise specification of the health outcome to be forecast and a clear definition of the forecasting horizon;

Step 2:

Use the literature to identify causal or highly correlated variables that are associated with the identified health outcome measures in Step 1 (expert consult may be required in building this domain knowledge);

Step 3:

Identify the data sources for both the health outcome measure (Step 1) and all of the potential predictors, and ascertain the availability and completeness (i.e. checking for gaps in the data series) of the data;

Step 4:

Prepare the data sets for basic statistical analyses, including descriptive patterns and the development of forecast algorithms. Some preliminary activities include data cleaning and management, and the generation of supplementary variables for further analyses;

tep 5:

Generate the predictive models and validate them using different sets of similar historical data;

Step 6:

Evaluate and determine the final lists of indicators needed for good predictive model(s) based on the practical access to their measures (data);

Step 7:

Develop very specific and tailor-made health forecast services for a specific purpose/client, and then periodically update the model(s).

BENEFITS OF HEALTH CARE FORECATING

Health plans and physician groups increasingly use sophisticated tools to predict individual patient outcomes. Such analytics will accelerate as US medicine enters the digital age. Promising applications of forecasting include better targeting of disease management as well as innovative patient care approaches such as personalized health insurance and clinical decision support systems. In addition, stakeholders will use predictions to advance their organizational agendas, and unintended consequences could arise. Forecasting-based interventions might have uncertain effectiveness, focus on cost savings rather than long-term health, or specifically exclude disadvantaged populations. Policy makers, health plans, and method developers should adopt strategies that address these concerns in order to maximize the benefit of healthcare forecasting on the long-term health of p