August 21, 2019
Risk scoring enables healthcare providers to assess the risk of the patient population. This allows them to allocate resources based on the risk score. Someone who has a higher risk score like a 50-year-old male with asthma requires more attention than a healthy 25-year-old athlete. Risk scoring is a type of healthcare predictive analytics that allows predicting the costs associated with future treatment for insured patients.
Historical data plays an important role in risk scoring. It helps determine which patients will need more case. In the case of the above example, the older male is more likely to visit an emergency room. He will require regular care and the costs incurred on his treatment will be higher.
The metric helps healthcare providers in risk stratification of the patient population. Every facility has a limited budget and by categorizing patients using risk score, they can manage it better. It will result in balanced health management for the patient population and mitigate operational costs.
However, risk scoring needs to be made better. The transition from traditional payment methods to value-based reimbursement calls for better health management systems. Risk scoring with predictive analytics will help facilities better understand the risks involved with each patient and optimize their care. Risk scoring is based on several factors that need to be considered while categorizing the patient population.
Determine the appropriate indicators of risk
Apart from basic indicators like age and gender, other indicators are also included in risk assessment. Diagnostic information and disease condition are the two major contributing indicators. These are important indicators to predict the risk of a patient’s health. But not
Medical records, claims, and drug data are good sources to find disease status and diagnostic information. Other indicators include the intensity of the diseases, disabilities, professional status, and medi-claim status. Some diseases-specific indicators can also be utilized for a niche patient population.
Quality of data sources and data ethics
Healthcare analytics is based on historical data. This data defines different indicators for calculating the risk score. More often than not, mortality, morbidity, pharmaceutical, and self-report factors are considered as data sets for risk scoring.
The mortality record of a hospital is reported by the public and their own records. This data, however, isn’t a proper indicator of a healthcare organization’s effectiveness. A high mortality rate could mean that there are more sick patients in the hospital than others. The patient population needs to be aligned with the mortality rate for using it as an indicator.
Morbidity rate provides an overall report of the person’s health condition. The data is available from in-patient/out-patient records. This data is based on the diagnosis of a patient’s condition which helps in predicting their state and further treatment. It has better accuracy as it is based on diagnosis rather than treatments.
Pharmaceutical data is made available by the World Health Organization. It enables to identify chronic healthcare situations. It is standardized data and eliminates any chances of upcoding.
Self-reporting data allows healthcare providers to understand the social factors that impact a patient’s health. It also helps in determining the intensity of the health condition. SF-12 and SF-36 are the standard methods used to collect this data but socioeconomic factors are often neglected in this.
The quality of data is essential in risk scoring. Different data acts as varied indicators for different patients. For healthcare predictive analytics to ensure accuracy of risk scoring, the data needs to be accurate too. Standard coding practices must be used and regular reviews should be conducted to avoid errors.
Create a coherent risk scoring framework
It is important to determine a coherent framework for risk scoring. Generally, two methods are used to measure the risk. The first one is the cell-based method. In this, patients are categorized into risk buckets on the basis of the complexity of their diseases. Patients with severe illnesses are arranged first and then the process is moved to the lower risk patients. This risk stratification allows placing patients in different buckets and determines the costs associated with each of them.
Regression analysis is the second risk scoring methodology. It creates a predictive regression line for the patient population using several different indicators. A patient’s risk is determined by aligning their risk factors with the regression line. Even though the complexity of this method makes it difficult to implement, it provides a more accurate result than the cell-based method.
Modify perspectives for the model
The data gathered and processed can either be analyzed prospectively or retrospectively. Prospective risk is utilized to determine the future risks associated with the patient’s health. It is based upon years of historical data that helps identify the future condition of the patient.
Retrospective risk deals with real-time risk and predicts the risks associated with that year only. Historical data is not utilized; the present condition of the patient is rather used as a factor to identify his health status. This methodology is complex and utilizes statistics in predictive analytics. It offers a clear view of where the patient’s health stands today.
Expect achievable results
Whether an organization is resource-limited or not, there are several costs associated with the methodologies. Finding the workers that are skilled in statistics, training for practices, data quality
Risk scoring offers huge benefits for health management of the patient population. To ensure success with risk scoring, organizations need to define their goals and align them with the models of assessment. TechJini is a leading healthcare solutions provider that enables effective risk scoring with top-notch products. Contact us today and start your risk scoring journey today.