Better Analytics Make Decision Support Smarter
When it comes to the value of data analytics, fictional detective Sherlock Holmes offers a simple analogy in The Adventure in the Copper Beaches. “‘Data! Data! Data!’ he cried impatiently. ‘I can’t make bricks without clay!’” While Holmes was bemoaning his lack of relevant data to solve a mystery, hospitals are instead struggling to “make bricks” from an overabundance of data.
The key to honing in on the right data for informing decision-making is effective data analytics.
From leveraging patient data to impact population health to gaining insights into likely consumer behavior through psychographic segmentation, data analytics can help organizations realize the value of current data and determine how data gathering needs to evolve for better decision support in the future.
The Expanding Impact of Technology on the Health Care Industry
Without a doubt, the burgeoning use of EHRs and other health IT is changing how health care is delivered in the U.S. According to PwC’s Fit for the Future: 17th Annual Global CEO Survey, hospital leaders are committed to the trend.
- 89 percent are expanding their ability to innovate
- 93 percent are looking to change their technology investments
- 95 percent are investigating ways to better manage and apply big data
CEOs, CIOs and other health care executives face some serious challenges on the path to improved clinical decision support, as evidenced by the slow-down in Stage 2 meaningful use attestations.
Information Week reports that the CMS requirement to “implement five clinician decision support interventions related to four or more clinical quality measures at a relevant point in patient care for the entire EHR reporting period” has been achieved by very few providers. The article notes that such robust clinical decision support use is a rarity except for alerts related to drug-drug interactions and drug-allergy contraindications.
The Challenges Ahead
The Information Week article also notes that “Meaningful Use really is a euphemism for minimal use.” In order to expand beyond the current requirements, clinical decision support systems must overcome several hurdles.
- Access to relevant, up-to-date information. Howard Goldberg, MD, senior manager at Partners Healthcare's Enterprise Clinical Informatics Infrastructure division told Information Week, “Knowledge is not static. Your content needs to be kept evergreen.” Organizations need always-current research literature and databases to stay abreast of the latest information on topics ranging from immunizations to health care consumer trends.
- Easy-to-use tools.According to CMIO.net, Jonathan Teich, the CMIO for Elsevier and a practicing ER physician at Brigham and Women’s Hospital, clinical decision support needs to function like GPS available in so many cars and mobile phones. While it is based on myriad data and complex technology, the end result for users is simple directions like “In .5 miles, turn left.” In fact, mapping apps are now mining consumer-provided information, along with satellite data, to provide even more useful information, such as suggesting alternate routes based on up-to-the-minute traffic flow.
- Better Sharing of Information and Solutions. Despite advances that have been made in health IT development, organizations — from hospitals to EHR vendors — develop proprietary solutions that lack interoperability. The lack of sharing is holding back innovation, creating a problem so significant even Congress is addressing the issue. In the recently passed 2015 Omnibus Appropriations bill, the ONC has been directed to decertify any EHR products that block the sharing of health information. Teich suggests that the health care industry as a whole needs to do better, saying “If you look at standardized methods, standardized technologies, new ways of sending things back and forth, and new approaches for selecting the best thing for the right situation, we should be a in a position where we can actually have more hospitals, more organizations, more practices and more patients be able to get the right information they need at the right time.”
Another consideration is that the data captured and communicated via EHRs tell WHAT a patient is doing, not WHY he or she is doing it. In other words, it lacks the context necessary to understand the reasons for patients’ behavior. “Small Data” such as market research can help decipher what “Big Data” is telling you.
Tools like c2b solutions’ psychographic segmentation model can be leveraged to draw critical insights into patients’ motivations and what individual healthcare consumers need to become engaged in their own health care. Clinical decision support, after all, will have little impact if health care providers are unable to connect with their patients in meaningful ways that drive more positive outcomes.