Categories: Big Data, Leveraging Healthcare Analytics

Leveraging Healthcare Analytics for the Changing Landscape

Healthcare analytics is not new to the medical field, however healthcare organizations now have to focus on Leveraging Healthcare Analytics in order to navigate the changing landscape of the healthcare industry. In recent times there has been a huge demand for healthcare data analytics and a huge demand for tools to help process and interpret big data.

According to a recent Research and Markets report, health data analytics is poised to grow into a $34.27 billion industry by the end of 2022. Leveraging healthcare analytics means able to garner actionable insights from various sets of data (patient data) that is typically gotten from electronic health records (EHR).

Healthcare analytics covers a huge area of the medical field and has some sort of relationship with almost all aspects of the industry. Like population health management. Healthcare analytics is being used to fix some challenges in population health management. Population health management is an effective approach to improving healthcare delivery across the population and across a diverse group of people at all levels.

With healthcare analytics, cost of care can be lowered and then healthcare can be accessed by more people. This is of course, a lot more simplified than it really is. There are difficulties to PHM and the data mining involved is an ever-expanding patient base, compounded by the addition of new patients under the Affordable Care Act and the shift toward new payment models that it requires.

In fact, the issues are so much and ever-present that many people in the industry believe that healthcare analytics alone cannot address or solve the problems of the industry. Issues like too few resources and too many patients, inaccessibility and high cost etc plague the industry and it is understandable why many see a bleak future for the healthcare industry.

Healthcare Analytics Adoption Model
“Clinical data analytics follows a framework called the Healthcare Analytics Adoption Model, which was developed by a cross-industry group as a guide to classifying groups of analytics capabilities and providing systematic sequencing for adopting them. It provides a structure for evaluating the industry’s adoption of analytics, a roadmap for organizations to measure their own progress toward analytic adoption and a process for evaluating vendor products.”

Leveraging Healthcare Analytics for Big Data

Big data is the future of the healthcare industry and those who do not adapt and embrace it will get swallowed by it. There are many insights that big data holds and the potential can already be seen with the insights gotten from electronic health records EHR but that is only a glimpse of what is to come.

Emerging data sources are challenging healthcare organizations to broaden the scope of their data gathering and analysis and to embrace more technology. In the next five years, more data sources will emerge and will gain more ground. It has already started happening actually. The patient-generated healthcare data (PGHD) is relatively new but it is gaining more grounds and is beginning to be seen more and more is an important data source. The future is that more and more patient data will be used to predict outcomes.

A recent report titled “Care Redesign: What Data Can Really Do for Health Care” states that, in a survey of healthcare professionals by NEJM Catalyst, a part of the Massachusetts Medical Society, respondents identified clinical data, cost data and claims data as the three most useful data sources today for effective care design and management.

Leveraging Healthcare Analytics for Social Determinants of Health

The role of social determinants in healthcare is a topic that is being explored more and more in the healthcare industry. The truth of this is that it has been a long time coming. Perhaps the medical industry and medical field have ignored it for so long because they do not like to deal with qualitative aspects like society and social science variables that are not easy to quantify.

But the undeniable and self-evident truth is that factors such as where people are born, grow up, live, work and their age have an impact on their health and also on the healthcare they have access to. These factors were expected to have a direct impact on mortality and healthcare expenditures. Harnessing the data sources that provide data on these qualitative variables is very important for the future of this field. There are health risks that come with people who are in a low-income population.

Can you really say population health is great if a certain group of people is significantly below average? The low-income population segments are seen to be more at risk of the adverse impacts of their socio-economic status. Addressing this gap presents an enormous opportunity for health plans and health systems alike in progressing to the next stage of evolution in their care management and intervention models.