Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions

National Research Council, part of our National Academies of Science, released the report of a committee chaired by Bill Stead of Vanderbilt University. The report addresses healthcare IT from the policy perspective. Below is the author's summary of the report:

Two key statements:

• page S-2: “current efforts aimed at the nationwide deployment of HCIT will not be sufficient to achieve the vision of 21^st century health care, and may even set back the cause…”
• page S-8:  “… government institutions—especially the federal government—should explicitly embrace measurable health care quality improvement as the driving rationale for its health care IT adoption efforts, and should shun programs that focus on promoting adoption of specific clinical applications.” 

 

Observations & reasoning behind the statements:

• page 1-5:   “Many health care institutions, especially large ones, do spend considerable money on IT, but the IT is implemented in ways that make even small improvements hard to introduce.  Even across the systems within an institution, interoperability is often awkward and slow.  Information exchange with the information systems of other institutions is rare.” 
• page S-3:  “IT applications appear designed largely to automate tasks or business processes.  They are often designed in ways that simply mimic existing paper-based forms and provide little support for the cognitive tasks of clinicians or the workflow of the people who must actually use the system.  Moreover, these applications do not take advantage of human-computer interaction principles, leading to poor designs that can increase the chance of error, add to rather than reduce work, and compound the frustrations of doing required tasks. …”
• page S-4:  “Health care IT was rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process improvement; or to link clinical care and research.  Health care IT rarely provided an integrative view of patient data.”
• page 3-2:  “Care providers spent a great deal of time in electronically documenting what they did for patients, but these providers often said they were entering the information to comply with regulations or to defend against lawsuits, rather than because they expected someone to use it to improve clinical care.”
• page S-2:  “Success … will require greater emphasis on providing cognitive support for health care providers and for patients/family caregivers … which refers to computer-based tools and systems that offer clinicians and patients assistance for thinking about and solving problems related to specific instances of health care.”
• page 3-3:  “The majority of today’s health care IT is designed to support automation, with some investment in supporting connectivity, and little in support of decision support or data mining.   Yet the IOM’s vision for 21^st century health care expects health care IT capable of supporting cognitive activities and a learning health care system.  These activities are much more about connectivity, decision support and data mining than they are about automation.  The health care IT investment portfolio must be re-balanced to address this mismatch.”
• page S-8:  “In focusing on the goal to be achieved, namely better and/or less expensive health care, clinicians and other providers will appropriately be drawn to IT only if, where, and when it can be shown to enable them to do their jobs more effectively. Blanket promotion of IT adoption where benefits are not clear or are over-sold—especially in a non-infrastructure context—will only waste resources and sour clinicians on the true potential of health care IT.”

 

What health care needs from IT that today’s systems rarely provide (pages S-3 to S-4):

• “Comprehensive data on patients’ conditions, treatments and outcomes;
• Cognitive support for health care professionals and patients to help integrate patient-specific data where possible and account for any uncertainties that remain;
• Cognitive support for health care professionals to help integrate evidence-based practice guidelines and research results into daily practice;
• Instruments and tools that allow clinicians to manage a portfolio of patients and to highlight problems as they arise both for an individual patient and within populations;
• Rapid integration of new instrumentation, biological knowledge, treatment modalities, and so on into a “learning” health care system that encourages early adoption of promising methods but also analyzes all patient experience as experimental data;
• Accommodation of growing heterogeneity of locales for provision of care, including home instrumentation for monitoring and treatment, lifestyle integration, and remote assistance; and
• Empowerment of patients and their families in effective management of health care decisions and their implementation, including personal health records, education about the individual’s conditions and options, and support of timely and focused communication with professional health care providers.”

 

Making progress in the near term:

Page S-5: “Principles for evolutionary change:

• Focus on improvements in care - technology is secondary.
• Seek incremental gain from incremental effort.
• Record available data so that today’s biomedical knowledge can be used to interpret the data to drive care, process improvement, and research. 
• Design for human and organizational factors so that social and institutional processes will not pose barriers to appropriately taking advantage of technology.
• Support the cognitive functions of all caregivers, including health professionals, patients, and their families.”

 

While preparing for the long term:

Page S5:  “Principles for radical change:

• Architect information and workflow systems to accommodate disruptive change.
• Archive data for subsequent re-interpretation, that is, in anticipation of future advances in biomedical knowledge that may change today’s interpretation of data and advances in computer science that may provide new ways extracting meaningful and useful knowledge from existing data stores.
• Seek and develop technologies that identify and eliminate ineffective work processes.
• Seek and develop technologies that clarify the context of data.”

 

And (page S-9) “Encourage interdisciplinary research in three critical areas: (a) organizational systems-level research into the design of health care systems processes and workflow; (b) computable knowledge structures and models for medicine needed to make sense of available patient data including preferences, health behaviors, and so on;  and (c) human-computer interaction in a clinical context.”