“In the next 10 years, data science and software will do more for medicine than all of the biological sciences together.” – Vinod Kholsa
Many market factors make it essential for managers of organizations (both payer and provider organizations) to incorporate better, faster decision support tools into their operational processes: Cost pressure on health plan premiums (see Employer Health Insurance Premiums Rose 62% Between 2003 & 2011 and Narrow Networks Happening By Design & By Default). The push for “better performance” and the emergence of rating and ranking systems (see What’s New With CMS Quality Initiatives? and “Quality” Is In The Eye Of The Beholder). The move to value-based payment (see ‘Pay For Value’ Making Its Way To Practice and 40% Of Commercial Insurance Reimbursements Tied To Performance Incentives). In this market, the competitive edge will go to the organization that can provide “more” for the same amount of financial resources.
Yet going from “big data” through predictive analytics to “decision support” is not a small task. Assuming you have the right data and the analytics in place, the question is how best to apply them. This is where decision support tools come into play. How to make decision support tools work, and some examples of decision support innovators, was the focus of a recent presentation, Clinical Decision Support Systems: Changing The Future Of Behavioral Health Treatment, by Melanie Wilson, Ph.D., Director of Benchmarking & Analytics at Netsmart, at The 2014 OPEN MINDS Technology & Informatics Institute presentation.
In the presentation, Dr. Wilson outlined the range of clinical decision support tools – from basics like information retrieval and automated alerts and reminders; to diagnostic assistance; to therapy planning and prescribing decision support systems, to image recognition and interpretation. Regardless of the tool that is needed, she emphasized the importance the decision support construct developed by the Centers for Medicare & Medicaid Services’ “CDS Five Rights” concept (see Clinical Decision Support: More Than Just ‘Alerts’ Tipsheet):
- The right information (evidence-based guidance, response to clinical need)
- To the right people (entire care team – including the patient)
- Through the right channels (e.g., EHR, mobile device, patient portal)
- In the right intervention formats (e.g., order sets, flow-sheets, dashboards, patient lists)
- At the right points in workflow (for decision making or action)
But to incorporate this construct, health and human service organizations need to “reinvent” their process – moving from a linear treatment process to an interactive process that incorporates use of data and analytics. Unfortunately, there are a number of impediments, from lack of EHR systems, to poor design of the treatment process workflow or the interface of the data system and clinical professionals, to organizational culture.
I thought one of the most interesting pieces of Dr. Wilson’s presentation were her case examples of current uses of decision support tools in the field – including:
- The National Action Alliance for Suicide Prevention’s Zero Suicide Initiative (see What Is Zero Suicide?)
- The Centers for Medicare and Medicaid Services Clinical Quality Measures (see The 2014 Clinical Quality Measures Tipsheet)
- The Grafton’s Reboot/Goal Mastery Program (see AudioEye & Grafton Launch REBOOT Care Management Platform For Behavioral & Developmental Disabilities).
I left the day with the sense that we are starting to see decision support tools make their way to consumers (see Why Is It So Hard To Get Health & Human Service Organizations To Try Something New? and Proactive Wellness Or Creepy?)… Just not fast enough.