Making the Case For Moving from Health IT to Health Analytics
Health Information Technology (IT) and Health Informatics degree programs, both at undergraduate and graduate levels, have significantly grown in popularity in the past decade. The rate at which hospitals, clinics, and support facilities have implemented Electronic Health Records (EHRs) and increased digital sharing of clinical data over the past ten years has been nothing short of remarkable. As a result of this digital transformation, demand for new hires trained at the nexus of health, technology, and informatics has rapidly increased. In response, many educational programs were developed to train those excited by health IT and health informatics.
However, while health IT and health informatics workforce demand are likely to sustain in the near term, an important shift is occurring with vital implications. The market for EHRs and related technologies is maturing and plateauing. This shift from drastic to gradual growth is typical of IT adoption in any industry, where the growth of innovation is typically slow at first, then rapid, and then ultimately matures (i.e., the “S” shaped model of IT adoption). We claim that health IT growth is entering the plateau phase. Educators must now recognize how this inevitable shift will impact workforce demand.
You may be thinking, are skilled, technical workers really going to be in less demand in the future in health care? The short answer is, “No!” at least not in our opinion, but we argue that an important shift is occurring. The next wave of growth, in our projection, is the use of digitized health information to improve healthcare performance and outcomes. In particular, while demand for technology-specific skills is plateauing, demand is rapidly growing for a workforce skilled in health analytics.
Health analytics focuses on the analysis of health information with the goal of generating insights that will aid in solving clinical and business problems. For instance, in the context of population health and accountable care (i.e., the emerging requirements that healthcare providers be more proactive and provide more value rather than just volume), how should healthcare systems equitably allocate resources and provide care management services? Which geographical areas should be focused on for additional primary care services? What types of patients present the most morbidity and mortality risk, with the most potential benefit from specialized care management services? Which cohorts of patients are the highest utilizers of high-cost services (e.g., the emergency department) that could be substituted with lower-cost services (e.g., remote patient monitoring, nurse visits, primary care)? When and how should healthcare systems proactively reach out to patients in need of preventative care services?
Health analytics focuses on the analysis of health information with the goal of generating insights that will aid in solving clinical and business problems
Many such fascinating questions and opportunities abound in this space, which can only be effectively answered (or informed) through in-depth analysis of existing digital data. What is now increasingly demanded is experts in exploratory, descriptive, visual, predictive with machine learning, and finally, prescriptive analytics for generating insights and action items.
Healthcare systems now want to visualize key metrics, have real-time (or near real-time) descriptions of their populations and subpopulations, and are developing and incorporating predictive risk models (e.g., readmission risk, sepsis risk, ED utilization risk, etc.) into clinical decision-making systems and processes. No longer is a reactive strategy acceptable in health care, where patients are only considered when they walk in the door. Now, a proactive strategy is a must, where healthcare systems must use data to better understand not only the present, but also possible futures where unacceptable risks (e.g., too many readmissions, not enough prevention, etc.) can be mitigated before costs get out of control. This strategic shift, from fee-for-service to fee-for-value as well as from reactive to proactive, accountable approaches, will only be successful if digital information is not only collected but effectively used to inform decision-making. This, of course, was always the goal behind digitizing health information. Now we are finally at the point of using health information to make a positive impact. Educators, and ultimately the skills of the emerging workforce, will be central to the overall success of this strategic shift.
How, then, can educators best meet these exciting, new needs? Here at the Robinson College of Business at Georgia State University, we offer dedicated Health Analytics courses, specializations in Health Analytics, and industry partnership opportunities through “sprints.” In the core Health Analytics course, using data generated from the open-source tool Synthea (https://synthea.mitre.org/) and tools including SQL, JupyterLab, and Tableau, students are taught how to leverage data pipelines, exploratory data analysis, visual analytics, and ML to inform population health program design. Every semester, we also partner with health care intuitions, such as hospitals, treatment clinics, and aid-based institutions, such as food banks, to take on data-based projects. In each of these “sprints,” the partnering institution provides data related to a specific clinical or business problem, faculty and students analyze the data and generate insights, and, at the end of the semester, final analyses and insights are presented to leadership at the sponsoring organization. The results include valuable experiences for students, insights for improving processes and outcomes at health organizations, and relevant projects for faculty to contribute to and learn from. This virtuous cycle improves the educational process itself as well as the output: a skilled and in-demand workforce trained for the needs of today and tomorrow.
This is a fascinating time of growth and needs in the healthcare industry. It has been a long road to digitization in health care. The next decade, in our view, will be characterized by demand for analytics professionals as strategic shifts toward fee-for-value, population health, and accountable care continue. Further new opportunities will emerge in areas such as public health, precision medicine, social determinants of health, and global health. However, the information itself, especially in high volumes and in disparate formats and locations, will not often provide value on its own.
The true value will come from relevant and timely insights generated by a workforce skilled in health analytics. This also aligns well with the increasing application of machine learning and artificial intelligence in the healthcare industry as supporting tools to help physicians and nurses in patient diagnosis, treatment, and care. Educated healthcare professionals with data science and analytics skills can help renovate the healthcare industry by utilizing big data of either structured and/or unstructured texts and images to benefit patients, organizations, and society as a whole.