Changes to the patient, provider, payer conversation
In the era of big data and its enormous potential, healthcare organizations still largely depend on the analysis of traditional data sources to understand and predict patient and population health risk. Traditional sources typically include billing, electronic medical records, laboratory, pharmacy, medical claims and patient health risk assessments. However, they alone are no longer enough to provide accurate risk and outcome predictions.
Nonclinical life events and socioeconomic information, including street crime, illiteracy, income levels and even lack of access to fresh, healthy food, contribute to health risks and outcomes of individuals across the United States. To start, these factors, also identified by the Institutes of Medicine as “social determinants of health,” exacerbate health conditions ranging from asthma, diabetes and high blood pressure to depression, metabolic syndrome and chronic obstructive pulmonary disease.
Exploring big data for insights from this type of information is a new frontier in predictive modeling and can greatly benefit the healthcare industry. According to McKinsey & Company, big data can save $300 billion in healthcare largely through reductions in expenditures. Let us take a look at how socioeconomic data helps improve the accuracy of risk predictions, reveal hidden or unknown trends and improve health outcomes.
Imagine a single mother who has just experienced a divorce and had to move to a new neighborhood that has high crime rates. Up until then she was healthy with few to no medical complaints, but living in a less safe place than she did before, having to find a job after she has been caring for her child at home and dealing with emotional effects of divorce all make her susceptible to serious stress that can set off a series of adverse health challenges. If this type of change in an individual’s social environment could be captured, it could serve as an early warning system to the possibility of increased clinical risks. Such an understanding would allow either her health plan or provider to be proactive in getting her the care she and her child need.
This type of insight is equally critical for identifying and treating low or nonusers of the healthcare system who have unknown and rising risks. In this case, a plan or provider has scarce clinical information and cannot adequately predict the individual’s health risks. Socioeconomic data can help provide a relative risk picture for an individual.
While a person’s medical history will always provide the greatest insight into their future health risk, socioeconomic data alone can increase the accuracy of traditional age/gender models for identifying and predicting future costs. Many analysts and modelers have recently started to explore the use of social determinants of health and found little to no value in the data. There are many reasons socioeconomic data could not be useful; so before moving forward there are a couple of things you should be sure of first. Most critically, the data must be current and, secondly, linked accurately to an individual. Many data sources do not have enough data points for a single individual resulting in the data being compiled at a broad level, making it less useful. Additionally, not all socioeconomic data is equal. What is used must be vetted and proven to have predictive power to identify future health outcomes.
Today, our health system understands that patients are people, and what happens outside of the medical settings has a major impact on an individual’s current and future health. Applying socioeconomic data can enhance our understanding of a person’s specific challenges and change our approach to managing their health and the care they receive.
