Henk de Man of Cordys and I are preparing a draft RFP for issue by the Object Management Group (OMG) calling for a Knowledge Driven Process Modeling (KDPM) specification. This generalizes the requirements of case management modeling previously discussed as a draft OMG RFP (see Case Management: The Missing Link in BPM). There are many potential applications for knowledge-driven processes. In health care, a knowledge-driven process capability could support more effective management and innovation in treatment of medical conditions resulting in reduced costs and improved patient value.
The long-term remedies for rising health care costs in the US are innovation and economies of scale. In the book, Redefining Healthcare: Creating Value-Based Competition on Results ( Harvard Business School Press, 2006), Michael Porter and Elizabeth Olmsted Teisberg describes the need for specialization focused on treatment of medical conditions, and scale that supports development of expertise and optimization of processes. The focus on medical conditions supports performance measurement and billing based on results rather than procedures. This provides incentives for improvement. The emphasis on scale supports, collaboration and specialization for deeper understanding of the medical conditions, and innovation in treatment approaches. The processes by which medical conditions are treated must evolve from decisions and procedures by experts, to prescribed best practices and automation. Knowledge-driven process automation will support this evolution.
Clayton Christensen, in his book, Innovator’s Prescription: A Disruptive Solution for Health Care (McGraw-Hill, 2009), the propose is that disruptive innovation is essential for the advancement of health care both for reduced cost and improved quality. Disruptive innovation involves a change in the business model. The current emphasis on funding for procedures emphasizes cutting costs of doing the same procedures. It also creates an incentive for doing more procedures. A focus on outcomes and funding for treatment of medical conditions emphasizes quality improvement and business savings from new, more repeatable treatment approaches.
Christensen notes further that significant changes come from a transformation of treatment from “intuitive to precision medicine.” Insights, experiences, and technology should be incorporated into practices over time to reduce the level of skill required and improve cost and quality. Disruptive innovations imply significant changes to provider practices. For example, laboratory costs have been eliminated for routine monitoring of diabetic conditions as a result of technology that enables patients to perform their own blood tests.
In Andrew Corn's blog “Healthcare: Innovation Is Necessary But Not Cheap,” he considers disruptive innovation as proposed by Christensen, but observes that there has been reluctance for investment in new medical technology. He sees some improvement in investment as a result of economic recovery and the low acquisition cost of struggling start ups. However, I think he has missed the point.
Both of the above books describe barriers to change and a failure of competition to drive change. The costs and risks associated with new technology and innovation would be much smaller barriers if it were not so difficult to gain adoption. For example, laboratories do not have incentives to develop or adopt technology that allows patients to do it themselves. However, the cost and outcomes of treatment of diabetes is significantly improved because patients can monitor themselves and adapt to their eating habits. Often regulations or standards of care that are reinforced by clinicians are barriers to improvements. A focus on treatment of medical conditions enables innovation in the specific practices and circumvents in some of the barriers. Business savings and patient value from improved treatment of medical conditions will provide needed incentives for adoption.
This is where knowledge-driven processes fit in. Knowledge-driven processes for treatment of medical conditions will support intuitive medicine with improved tracking and record-keeping while supporting performance measurement and encoding of insights to improve and streamline practices. Specifications of activities can easily be changed to introduce new technology or to provide guidance in the use of certain procedures or medications. More precise processes can be incorporated using conventional business process modeling technology, but processes can still be adjusted to deal with unforeseen circumstances. Both intuitive and precise processes can be interwoven and evolved as improved techniques that are discovered or developed.
The draft RFP for a Knowledge Driven Process Modeling specification will be considered at the OMG meeting in September.
