In just a few short years, jobs in analytics have become some of the most competitive postings in health IT. Recent iterations of Gartner's Hype Cycle have placed “big data” and “advanced analytics with self-service delivery” at the peak of inflated expectations; “machine learning” now sits at the top of the curve. At HIMSS17, a vast array of IT solutions and educational presentations were in some way connected with analytics. So, why is analytics such a hot area?
To start with, medicine has become way too complex to practice well or safely without support from IT. Just as modern combat aircraft have incredible performance due to “hypercritical” wings, no human pilot can fly them without computers. So too, practicing medicine has long since passed human ability to memorize, recall or apply such complex data.
Not having rich, healthcare decision support at our fingertips comes at a cost, too. We have known for over a decade that the quality of care in the United States has many gaps (2005 RAND Study), and a more recent study shows that the U.S. ranks near the bottom on many health outcomes when compared with other industrialized countries (2014 Commonwealth Fund Study).
Adding to the challenge, despite advances in research, defining “best practice” across most areas of healthcare still has many gaps and weaknesses. Too many recommendations are based on “expert opinion,” rather than data or have limited practical application (see Problems with evidence-based practice). Deming’s quote, “In God We Trust, All Others Bring Data,” rings all too true here.
Even though we have known for some time that “medical care” determines at most around 20% of health outcomes, only recently has analysis of the role played by social determinants of health started to take its place alongside conventional studies of which drug, treatment, or procedure is best.
Why is analytics so hyped right now? I don’t think that’s a hard question to answer. We have a growing treasure trove of data that many believe holds the answers to better health at more sustainable costs. Unfortunately, much of this data is either not easily accessed, shared, compared or analyzed.
The challenges of mining unstructured data from doctor’s notes, exchanging data across systems via APIs and FHIR, mapping “apples to apples” via shared concepts, such as SNOMED CT, and applying data science (machine learning and artificial intelligence) combine to make analytics the key to unlocking these insights.
Talented volunteers on the HIMSS C&BI committee are applying their expertise to these problems. Examples include:
- creating a toolkit for staffing analytics programs,
- cataloguing potentially useful data sources beyond the electronic medical record, and
- exploring how non-traditional data could help provide earlier detection and better mitigation of public health crises.
If you work in healthcare, chances are you use or will use analytics to develop or apply insights that will lead to better health for your community, friends and loved ones. Opportunity is knocking.
- What does opportunity look like to you? What does the future hold?
- How are you using healthcare analytics?
- What new data sources are you using to advance care and delivery?
Join the HIMSS C&BI Community today to learn more about applying data and analytics to improve health, and other critical topics that will help you on your journey to Turn Data to Action.