The federal government’s $20 billion stimulus programs for health IT (HIT) is on its way. Called Hitech—for Health Information Technology for Economic and Clinical Health Act—it will fund the development of innovative HIT and use a “carrot & stick” financial approach to encourage clinicians to use HIT in meaningful ways. A debate now raging is how to define “meaningful use.”1
Meaningful use, to me, means using HIT in ways that are of great value to the patient and other healthcare consumers. It doesn’t matter what types of software tools are used, what communication infrastructure is used, what standards are used, or what certifications are used. It just means that the using HIT should result in ever more effective and efficient (i.e., ever greater value) care delivery.
Increasing care value is unlikely unless clinicians obtain information and guidance assisting them in answering difficult questions, making tough diagnostic and treatment decisions, collaborating effectively, and taking competent action. In addition, healthcare consumers (patients, clients, customers, etc.) would benefit from assistance in selecting the most cost-effective treatment options for existing conditions, and in managing their own health in ways that prevent illness, control chronic conditions, and increase their wellbeing.
Such assistance is crucial because the unaided human mind simply cannot handle the overwhelming details and complexity of many health problems. Consider the following section I wrote at www.wellness.wikispaces.com, which contains important quotes from Dr. Larry Weed and others:
The volume of clinical information expands exponentially with more than 150,000 medical articles published each month in more than 20,000 biomedical journals. Healthcare providers struggle to stay current with the clinical information, but inevitably become overloaded. This may contribute to the slow adoption of evidence-based research in clinical practice. There is just too much clinical information being generated for providers to incorporate into their internal base of knowledge.
As a group, healthcare providers care about patients and take pride in doing an excellent job in caring for their well-being. Nevertheless, the “…task of knowing every detail is way beyond the [ability of] human mind… For example…diabetes care ought to take into account any of 120 management options and 380 possible patient conditions associated with the disease. …the unaided mind cannot reliably recall all the causes or management options that should be considered for each patient, nor can it recall all the findings in the patient needed to discriminate among those options, nor can it reliably match findings to options under the time constraints of practice.”
In addition, “…most physicians are able to take into account only a portion of the diagnostic and management options potentially relevant to their patients and only a fraction of the evidence needed for choosing among those options. …Physicians do little better with the usual aids to medical decision making, such as practice guidelines and use of Internet resources. Those aids provide general knowledge, but do not ensure that the physician will recall all the data or successfully link it with a particular patient’s specific problem.” Problems that cross specialty boundaries and require multiple specialists, yet the current healthcare system does a poor job at supporting communication between providers and assuring continuity of care. And primary care physicians are not equipped with the information tools necessary to grapple with the information overload, nor do they have a system for coordinated care within which to function.
“Because physician time is expensive and scarce, their initial workups can be meager [as they] …act according to their own preconceived notions about what history, physical, and laboratory findings are worth checking. Equally idiosyncratic are the conclusions they draw from whatever data they select. Both selection and analysis of data are influenced heavily by their medical education, prior clinical experience, specialty orientation, contradictory clinical guidelines, financial concerns, cultural background, personal biases, and day-to-day time constraints, all of which vary enormously among individual practitioners.”
With this in mind, consider the recent report by the National Research Council of the National Academies, which concluded that a serious gap in health information technology (HIT) today is the failure to deliver patient-centered cognitive support (also called consumer-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…[We need] computer-based tools [that] 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.2
It’s logical to conclude from the information above that patient-centered cognitive support is the kind of value-enhancing assistance needed, but largely missing from, today’s HIT.
Thus, “meaningful use of HIT” can be translated into “using HIT to increase care value (efficiency and effectiveness) by providing ever-better patient-centered cognitive support.”
The HIT Gap and How to Fill it
Is it reasonable to expect the healthcare industry to fill the patient-centered cognitive support gap? According to a recent report by the Congressional Budget Office titled, Evidence on the Costs and Benefits of Health Information Technology,3 HIT systems have the potential to provide such cognitive support by, for example, reminding physicians to schedule tests, helping them diagnose complicated conditions, assisting them in implementing appropriate treatment protocols, and promoting research focused on developing and evolving evidence-based guidelines.
Having made a case for defining HIT as meaningful if it provides patient-centered cognitive support, I now offer an answer to the thorny question: What kind of HIT innovation is needed to fill the current cognitive support gap?
I contend that we should follow a path of radical innovation focused on developing secure, economical, interoperable, speedy, convenient, flexible, modular, ever-evolving, and easy-to-use HIT systems that fit into clinician’s natural course of work (workflows). These radical innovations should provide continually evolving patient-centered cognitive support by:
- Managing complete personal health information (PHI)
- Supporting researchers and clinicians collaborating in loosely-coupled professional networks
- Enabling development and management of computational models used by clinicians for cognitive support
- Exchanging the computational models in loosely-coupled networks of clinicians and researchers
- Fitting into clinical workflows.
Managing complete personal health information
These innovative systems should securely manage complete biomedical and psychological personal health information (PHI) over people’s entire lifetimes to provide a detailed holistic picture the whole person, both mind and body, which is essential for delivery of high value healthcare. They should enable biopsychosocial PHI—including biomedical, psychological, and social health related information—to be exchanged wherever and whenever it is needed, and protect it via HIPAA-compliant security and “granular level” privacy methods.
Supporting researchers and clinicians collaboration in loosely-coupled professional networks
HIT systems that support researchers and clinicians collaborating in loosely-coupled professional networks (as opposed to technical networks) enable people from multiple locations—who have different roles, responsibilities and experiences—to work together to make decisions beyond the knowledge or skills of any individual. These loosely-coupled networks would enable clinicians and researchers to pool their wide diversities of knowledge, ideas, and points of view, thereby providing a larger collection of intellectual resource, as well as offering access to a greater variety of non-redundant information and knowledge on which to base decisions. As such, these loosely-coupled networks provide the greatest opportunities for emerging creative solutions.
Furthermore, these systems would foster the emergence of health science knowledge by facilitating the sharing, analysis, discussion, and interpretation of (a) clinical process data related to diagnosis, treatment selection/prescription, and treatment delivery, and (b) clinical and financial outcome data related to quality and cost. This information would used to guide the ongoing development and evolution of computational models supplying cognitive support for continuous improvement of care effectiveness and efficiency.
Enabling development and management of computational models used by clinicians for cognitive support
It is important that the computational models providing patient-centered cognitive support do a good job in obtaining, analyzing, and supply information relevant to a specific patient in the context of the person’s particular situation and in relation to the whole patient and his or her predispositions. In other words, the models should do the following:
- Obtain comprehensive PHI from any data streams, manual inputs, biometric sensors, and data stores (either centralized or decentralized). In addition to patient status and health history, this information should encompass clinical process data, as well as results tracking, which includes outcomes data, guideline compliance rates, and the reasons for variance (departures) from the guideline recommendations.
- Analyze the data to identify important patterns (e.g., trends, associations, clusters, and differences) useful for making predictions, linking diagnosis to cost-effective treatments, conducting health-related surveillance (biosurveillance and post-market drug & medical device surveillance), etc. And test the data for statistical relevance to determine which information provides reasonable explanations. The results of such analyses would help determine, for example:
- Whether a person’s risk factors and changes in lab test results or vital signs over time indicate an imminent or worsening health condition
- How a person’s attributes (e.g., gender, age, medical history, conditions, vital signs, symptoms, genetics, attitudes, etc.) compare to people in different diagnostic groups
- What treatment options and self-management approaches are most likely to result in the best outcomes for a particular person
- If a medication currently in the market is evidencing side-effects at a higher rate than found in clinical trials
- If clusters of a particular illness is indicative of a pandemic.
- Provide patient-centered cognitive support through feedback mechanisms (including suggestions and reminders) and guidance (e.g., diagnostic aids and evidence-based guidelines). This feedback and guidance should be presented in personalized views that facilitate decision making, care coordination, and competent care delivery, which help:
- Clinicians (a) make valid diagnostic decisions; (b) make evidence-based preventive and therapeutic determinations; (c) deliver appropriate care cost-effectively through efficient, safe and effective procedures; and (d) avoid under-testing, over-testing, under-treating, and over-treating their patients.
- Patients understand their diagnoses, risks, and treatment options, as well as learn how to self-managements their own health wisely and responsibly.
These computational models, therefore, would provide patient-centered cognitive support through useful personalized information that increases the likelihood of positive outcomes.
Exchanging the computational models in loosely-coupled networks of clinicians and researchers
Systems that enable the exchange of computational models are essential for the continual improvement of the models’ ability to provide cognitive support. When people share and “play with” models, they compare models and test them for their ability to reflect reality accurately; they manipulate the models to represent different scenarios, such as “what if” scenarios about the probability of future occurrences; and they discuss the assumptions and results the models produce. When they find models that disagree or generate invalid results, they examine the fundamental assumptions built into the models, looking for logical flaws and inconsistencies, questioning the authors’ perception of reality, and debating about the assumptions and practical value of the model. By challenging their assumptions, useful counterintuitive insights often emerge, innovative thought is sparked, new questions arise, relationships are developed, the influence of an organization’s culture and politics are revealed, and compelling and unexpected management issues are discovered. This means that sharing and playing with models is an effective path to innovation, risk management, and value creation.4
Fitting into clinical workflows
Systems that offer cognitive support to clinicians during their natural course of work would make them more likely to take advantage of that support.
A New Cyber-Infrastructure
Furthermore, the meaningful use of such an innovative software system should be supported by a cyber-infrastructure that “…combines computing, information management, networking and intelligent sensing systems into powerful tools for…collecting and analyzing large volumes of data, performing experiments with computer models and bringing together collaborators from many disciplines.”5 An example of such an infrastructure is a secure, economical, easy-to-use peer-to-peer (P2P), publisher/subscriber architecture in which networks of nodes use e-mail attachments to exchange computational models providing cognitive support.
A Different Definition from HIMSS
While I proposed a path of radical HIT innovation, the Healthcare Information and Management Systems Society (HIMSS) Board of Directors recently proposed a more conventional path, which includes these seven HIT requirements:
- EHR certification by the Certification Commission for Healthcare Information Technology (CCHIT)
- Standardized patient data conforming to the Healthcare Information Technology Standards Panel’s (HITSP)
- Interoperability specifications based on the Integrating the Healthcare Enterprise’s (IHE) frameworks
- Use of an EHR including CPOE (computerized practitioner order entry) functionality
- Electronic exchange of patient summary information as specified in the Continuity of Care Document (CCD) standard
- Support for a subset of existing National Quality Forum-endorsed process and care measurement
- Use of clinical decision support (CDS) systems providing clinicians with clinical knowledge and intelligently-filtered patient information to enhance patient care.6
Interestingly, only item #7 relates directly to the delivery and continual evolution of patient-centered cognitive support, even though it barely scratches the surface as to what a CDS system should do. And while item #6 is also important, the subset of measures is grossly inadequate for delivering and evolving patient-centered cognitive support. Furthermore, it doesn’t push for significant implementation until 4-7 years from now.
Item #1 refers to an expensive certification process that stifles radical innovation by forcing out small HIT companies, including open source developers. I can see the benefit of testing HIT vendors product to see how good they work (like Consumer Reports does with cars and appliances), but vendors shouldn’t have to pay for it (Consumer Reports doesn’t make the manufacturers pay). Instead, a government agency (FDA?) could probably do it. Furthermore, the certification process to which HIMSS refers has nothing to do with patient-centered cognitive support.
Items #2 & 3, which refer to data and technology standards, present a double-edged sword for reasons I discuss in a series of posts. Rather than limiting HIT developers to a specific set of global standards, it would be better to allow them to use local data standards and any technology standards, as long as their tool can exchange required information with tools other vendors develop. That’s because well-designed innovative HIT tools should be able to provide patient-centered cognitive support without the constraints of particular data and technology standards.
Items #4 & 5 refer to particular types of HIT, which I agree are important. The problem with making exiting EHRs, CPOEs, and CCDs a requirement for “meaningful use” is (a) the current crop of HIT provides little, if any, patient-centered cognitive support and (b) this constraint may hamper innovation by impeding the invention of alternate types of HIT able to provide superior cognitive support.Conclusion
HIT is used meaningfully if it focuses on increasing care value (efficiency and effectiveness) to patients and other healthcare consumers by providing ever-better cognitive support. The smart path to meaningful HIT use is one that promotes the kinds of radical innovation that enable widespread collaboration and the application of good science focused on providing continually evolving patient-centered cognitive support. Following this path means (a) accepting that the unaided human mind, no matter how competent, simply cannot handle the incredible amount of complex information that must be processed to make wise decisions in difficult situations, (b) doing more to link scientific research and clinical practice, and (c) encouraging truly creative HIT solutions.
2. Stead, W.W. and Lin, H.S. (Eds.) (2009). Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council from http://www.nlm.nih.gov/pubs/reports/comptech_prepub.pdf
3. Congressional Budget Office. (2008, May). Evidence on the Costs and Benefits of Health Information Technology. Retrieved from http://www.cbo.gov/ftpdocs/91xx/doc9168/05-20-HealthIT.pdf
4. Gottesdiener, E. (2002). Requirements by Collaboration: Workshops for Defining Needs. Indiana: Pearson Education, Inc.
5. National Science Foundation. (2008). Special report: Cyberinfrastructure. Retrieved from National Science Foundation website: http://www.nsf.gov/news/special_reports/cyber/index.jsp
Steve Beller is a clinical psychologist, practitioner, researcher, software inventor, and last, but certainly not least, President/CEO of National Health Data Systems, Inc.