SHARP Focus: Patient-Centered Cognitive Support

Last December the Office of the National Coordinator announced the Strategic Health IT Advanced Research Projects (SHARP) Program. The SHARP Program was created to fund research focused on achieving breakthrough advances to address well-documented problems that have impeded adoption of health IT. With scalpel-like precision, the hope is that this research will accelerate progress towards achieving nationwide meaningful use of health IT in support of a high-performing, continuously-learning health care system. Under the recently announced SHARP funding program the University of Texas Health Science Center at Houston will get $15 million to focus on the research area of Patient-Centered Cognitive Support. Along with $15 million each for the University of Illinois at Urbana-Champaign to study Security of Health Information Technology, Harvard University for research on Healthcare Application and Network Platform Architectures, and the Mayo Clinic of Medicine to focus on Secondary Use of EHR Data.

While all of these other areas may be familiar to most, it seems not many are aware of patient-centered cognitive support. So what is it and why should we focus on this area of study? The report by the National Research Council (pdf) of the National Academies concluded that a serious gap in in the implementation of health IT is the failure to deliver patient-centered cognitive support. According to the report:

During the committee's discussions, patient-centered cognitive support emerged as an overarching grand research challenge to focus health-related efforts of the computer science research community, which can play an important role in helping to cross the health care IT chasm...

Today, clinicians spend a great deal of time and energy searching and sifting through raw data about patients and trying to integrate the data with their general medical knowledge to form relevant mental abstractions and associations relevant to the patient's situation…The health care IT systems of today tend not to provide assistance with this sifting task…

The availability of these models would free clinicians from having to scan raw data, and thus they would have a much easier time defining, testing, and exploring their own working theories. What links the raw data to the abstract models might be called medical logic—that is, computer-based tools examine raw data relevant to a specific patient and suggest their clinical implications given the context of the models and abstractions. Computers can then provide decision support—that is, tools that help clinicians decide on a course of action in response to an understanding of the patient's status. At any time, clinicians have the ability to access the raw data as needed if they wish to explore the presented interpretations and abstractions in greater depth…The decision support systems would explicitly incorporate patient utilities, values, and resource constraints…They would support holistic plans and would allow users to simulate interventions on the virtual patient before doing them for real.

We can conclude from this that patient-centered cognitive support can be of great value to successfully using health IT. According to their definition of the patient-centered cognitive support process, it would use a computerized model of a "virtual patient" that reflects an actual patient. The health IT tool would use this virtual patient to guide the selection and analysis of data. These targeted data would be:

• relevant to a specific patient and suggest their clinical implications
• provide decision support
• help clinicians decide on a course of action in response to an understanding of the patient's status
• take into account a patient utilities, values, and resource constraints…
• support holistic plans of care

The report also states:

These virtual patient models are the computational counterparts of the clinician's conceptual model of a patient. They depict and simulate the clinician's working theory about interactions going on in the patient and enable patient-specific parameterization and multicomponent alerts. They build on submodels of biological and physiological systems and also exploit epidemiological models that take into account the local prevalence of diseases. The availability of these models would free clinicians from having to scan raw data, and thus they would have a much easier time defining, testing, and exploring their own working theories. What links the raw data to the abstract models might be called medical logic—that is, computer-based tools examine raw data relevant to a specific patient and suggest their clinical implications given the context of the models and abstractions. Computers can then provide decision support—that is, tools that help clinicians decide on a course of action in response to an understanding of the patient's status. At any time, clinicians have the ability to access the raw data as needed if they wish to explore the presented interpretations and abstractions in greater depth.

With the deluge of data surging through EHR systems healthcare providers are struggling to stay afloat with all the clinical information and they inevitably become overloaded. This can impede the adoption of evidence-based research in clinical practice. I believe using innovative patient-centered cognitive support tools can help providers overcome some of the barriers that have stood in the way of health IT adoption. I'm really looking forward to seeing the results of this research. As Dr. Blumenthal said, "this is not ivory tower research; its goal is to quickly infuse the dynamic health IT sector with new thinking, ideas, and solutions."