Don Morris, is vice president of scientific product and technology development at Archimedes Inc., a health care modeling and analytics company based in San Francisco. He leads development of IndiGO, Archimedes' clinical-decision support tool, and other products for individualized risk prediction and decision support. He contributed this article to LiveScience's Expert Voices: Op-Ed & Insights.
Here's a typical scenario in health care: When a patient undergoes testing to rule out a stroke, the diagnostic code used on medical records may ambiguously suggest the patient actually suffered one. Is this accurate? No. Is it a serious problem? Perhaps. It depends whether the information is used to help decide a patient's need for treatments or for billing purposes.
The accuracy of patient data is just one of a number of obstacles to the evolving role of health care analytics detailed in a recent paper issued by the Institute for Health Technology Transformation (iHT^2), "Analytics: The Nervous System of IT-Enabled Healthcare," which offered strategies for managing sophisticated analytic tools in the health care industry.
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Analytics — the use of data analysis methods, detailed data and computers to track, understand and improve processes — has been key to the recent improvements in quality and efficiency realized in industries ranging from agriculture to the airline industry. But, despite its reputation for advanced science, the health care industry has failed to make those sorts of strides. In fact, the reverse has happened: Quality has stagnated while costs skyrocketed, fueled by a reimbursement system that is based on volume rather than impact on health. [5 Ways Computers Boost Drug Discoveries]
The Health Care Reform Act attempts to address that with two major initiatives. The first evolves health care reimbursement from volume to value, putting a premium on quality and cost-effectiveness. The second initiative has spurred an industrywide adoption of electronic patient-health records in order to enable the application of analytics (as well as for patient safety and portability of records, among other reasons).
Inaccurate data is just one example given in the iHT^2 report of the growing pains the medical industry is experiencing as it retools its analytics systems from billing to the problems of improving the quality and efficiency of medical care processes and medical decisions. An even greater problem may be missing information: An estimated 80 percent of patient data within electronic records still exists in "unstructured" form — physician-dictated notes and reports — that are not accessible for analytic methods. It's a costly predicament. A 2011 McKinsey report estimated that $300 billion is spent unnecessarily each year because of a failure to fully leverage existing patient and clinical data.
But, there is much reason for optimism. Slow as progress may seem, most major health care providers are well on their way to collecting and integrating different sources of clinical data, and they are starting to use it to avoid medical errors and redundant tests, improve communication between doctors, stay connected to patients between visits and engage patients in their own health and wellness . Ultimately, the data could be used to learn which medical treatments work for which people and rapidly apply that knowledge to make better medical decisions for individual patients.
A successful transformation to quality and analytics-driven health care will require a few additional changes. Data on the same patient from different health care systems needs to be integrated so one doctor can see what the patient's other doctors are doing: This is being addressed by health care information exchanges, another initiative of the Healthcare Reform Act. An important issue not mentioned in the institute's report is that clinical data still focuses very much on the provider's perspective — what's been done and prescribed — and it needs to become more patient-centered.
Understanding what a patient is doing is vital: Is he exercising? What's the family history? Is he smoking? Is he taking his medications, and, if not, why? Did he have side effects? Can't afford the treatments? Were they discontinued by another doctor? All of those factors are underreported in the clinical database.
Another critical issue touched on in the report is quality measurement. Sophisticated analytics will be required to develop comprehensive measures of health care quality to be used for reimbursement. The National Committee for Quality Assurance has just initiated a program to develop measures that assess the quality of care from electronic medical records. Adoption of sub-optimal measures could lead to a misalignment of health care, with health outcomes similar to what we now have in the fee-for-service system.
There also will need to be an important shift among health care providers in how they view the scope and focus of their role. In the old fee-for-service model, the physician's responsibility ended when she gave the right advice to her patients, and prevention was not well reimbursed. When health care providers have more responsibility for the ultimate health of their patients, prevention, education and patient engagement between visits will become much more important. This is a shift that is well under way at Kaiser Permanente, an organization that has had more time to adapt to value-based reimbursement, and it is a major reason that organization now leads the nation in health care quality scores. This is another way health care will evolve as a result of better analytics — a paradigm shift that will lead to better results.
Changes in reimbursement and in the accessibility of patient data have moved medicine to the verge of a new era in which its impact on improving patient health can be measured and optimized and improved just as it has been in other industries.
Health care is complicated, and changes require caution, so it makes sense that the introduction of modern analytic methods may lag behind other industries. But, as the transformation gets under way, the community is approaching those changes with enthusiasm and optimism that they will drive our health care in new and positive ways.
The views expressed are those of the author and do not necessarily reflect the views of the publisher. This article was originally published on LiveScience.com .
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