Saturday, December 16, 2006
One type of model is a patient profile report (from an EMR/EHR). These reports may be designed to assist providers in making diagnostic and treatment decisions, as well as tracking treatment progress and outcomes. As such, providers in different healthcare disciplines require different information about their patients. A primary care physician, being a generalist, would benefit from a broad spectrum of information, covering biomedical, psychological, and environmental factors. While all providers would benefit from information about current medical conditions, medications being taken, alergies, vital signs, basic lab results, medical history, etc., a specialist would benefit from a more in-depth sub-set of information related to their area of specialization.
For example, a cardiologist would benefit from data related to heart functioning, such as location of chest pain, ST elevation or depression, Q waves or left bundle branch block, T wave inversion or hyperacusis, CKMB and Troponin T or I levels, and heart imaging studies, etc. A dentist would benefit from information about previous dental work done, dental x-rays, exiting medical conditions affecting teeth and gums, etc. A mental health practitioner would benefit from detailed information about the relationship between a patient’s thoughts, emotions, and behavior, as well as psychosocial data, etc. An integrative/integrated medicine practitioner would benefit from addition information about the mind-body connection, metabolic functioning, etc. And a personal health profile report (from a PHR) would benefit the patient most if it included risk appraisal and self-management information in lay language. And so on … Different models for different folks.
So, what’s the best way to decide what the contents of specific typpe of a health profile report model should be? I suggest I good way to do this is with loosely connected groups of individuals connected through virtual communities who collaborate to develop useful models. They would create, exchange, compare, discuss, debate and evolve the models, as I discussed in the previous post.
As way of example, here’s a link to a holistic (mind-body-environment/biopsychosocial) personal health profile report model I developed. Imagine how much could be accomplished if networks of loosely connected healthcare professionals and consumers were to collaborate around such models by ripping it apart, modifying it, and rebuilding it meet their particular needs.
Saturday, December 02, 2006
The first is “loosely-coupled social networks” in which people from multiple locations and with different roles, responsibilities and experiences work together to make decisions beyond the knowledge or skills of any individual. Collaboration among people with wide diversities of knowledge, ideas and points of view provides a larger collection of intellectual resource, and offers access to a greater variety of non-redundant information and knowledge on which to base decisions. Compare this to a tightly-coupled network that limits participation to people within the same discipline, department, region, etc. and with people who have access to the same information sources and who share similar experiences. In the loosely-coupled social networks are the greatest opportunities for stimulating multifaceted discussions, out-of-the box thinking, and creative solutions.
The second concept is “sharing & playing with models.” There are many different types of models used in healthcare, including models for defining health problems/diagnoses (e.g., ICD and DSM codes) and treatments (e.g., CPT and ABC codes), for assessing and managing clinical and financial issues/risk (e.g., retrospective encounter and claims data analyses), for evaluating performance (e.g., variance analysis and risk-adjustment), for deciding the interventions to render and procedures to follow (e.g., clinical guidelines and pathways), for testing hypotheses and assumptions, for paying for care (e.g., HSA/HDHP and traditional indemnity insurance), for rationing care (e.g., QALY), and so on. 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.
Conclusion: By encouraging people in loosely coupled social networks to share and play with models, radical innovation is fostered by disrupting of status quo, which enables the models upon which decisions are made to evolve continuously. The bottom line is that connecting diverse groups of people and giving them the ability to model-play would produce continually improving models; and using these models to support decision would result in safer, higher quality, more cost-effective care.
In my next post, I give a practical example of the value of exchanging healthcare models.
Wednesday, November 15, 2006
“The most obvious difference between [European] health care systems and ours — that their governments provide universal insurance — certainly plays a big role in the cost differences. Look behind the receptionist at your doctor’s office, and you will very likely see a staff of people filing claims to different insurance companies. The insurance companies, meanwhile, employ a small army charged with figuring out how to avoid covering the unhealthy. The administrative costs of our patchwork bureaucracy eat up about 25 percent of health spending… Even in Europe’s single-payer systems, administrative costs account for about 15 percent of health spending, once everything is included, according to the Lewin Group, a consulting firm…. Medicare, which has administrative costs roughly as low as those of other countries’ universal plans. Younger Americans, by contrast, have private insurance, with all its inefficiencies. Yet elderly Americans’ share of national health spending is similar to that of the elderly in other countries, as Arnold Kling, an economist, has noted.”The comment section of the economistsview blog (link above) included a discussion of the higher cost of pharmaceuticals.
“So something beside administrative costs is at work here, and it involves a basic cultural difference. Americans seem to be less willing to take no for an answer and more willing to try almost anything, no matter how expensive or how slim the odds, to prolong life. … It has made us obsessed with medical advances and turned this country into the world’s research laboratory. …But much of it is simply wasteful. Expensive procedures …are often no more effective than basic ones, according to research. Yet doctors can keep on getting reimbursed for the expensive ones. ‘Basically, anything that doesn’t kill patients is paid for by Medicare and insurance companies,’ said Jonathan Skinner, a health care researcher at Dartmouth College. …’We Americans tend to treat any rejection of a health claim as some conspiracy by insurance companies, the government, doctors and the pharmaceutical industry. In other countries, people have arrived at a better understanding that health care necessarily involves economic triage …’”
At the Economist.com, at www.economist.com/world/displaystory.cfm?story_id=5436968, they explain it this way:
“The Bush team argue that ‘fairer’ tax treatment will slow cost rises and enable more people to get basic insurance. The opposite is more likely. Bigger tax subsidies for health care are, if anything, likely to raise overall spending. Worse, since most tax breaks benefit richer people most, more tax incentives are likely to bring more inequality. They will also reduce tax revenue and worsen the budget mess. Mr Bush's health-care philosophy has a certain political appeal. It suggests incremental change rather than a comprehensive solution. It reinforces existing industry trends. And it promises to be pain-free. Unfortunately, it will not work. The Bush agenda may speed the reform of American health care, but only by hastening the day the current system falls apart.”Others have argued that direct-to-consumer advertising by pharmaceutical companies also drive up costs because more patients demand from their doctors medications they don’t need. In addition, some make the case that by focusing costs are increased because our healthcare system rewards mediocrity through a “fix it and pay for it again when it breaks” process, rather than focusing on wellness/prevention and rewarding cost-effective sick-care.
In summary, the reasons given for the exceptionally expensive cost of healthcare in the US include: Waste, administrative overhead, Americans refusal to accept economic triage (take no for an answer), taking on the role of the world’s research laboratory, our attitude toward end-of-life spending, cost of prescriptions drugs, HSA tax-based incentives, direct-to-consumer advertising, and a system that rewards mediocrity rather than cost-effective care.
What do you think?
Friday, November 10, 2006
The moral hazard idea — which states that insurance encourages risky and wasteful behavior by the insured person since the cost of consumption is paid by someone else — is considered a myth by some when applied to healthcare and is not a reason to assume Health Saving Accounts/High Deductible Helath Plans (HSA/HDHPs) or other methods of cost-shifting will reduce utilization and control costs by making people pay more out of their own pocket for care. They claim this is because, unlike other consumer goods, insured people don’t go to healthcare providers just because it’s free; in fact, most people don’t like to go to the doctor or take medications. Instead, what is most likely to happen when more costs are shifted to consumers is that they will forego routine preventive care and delay getting care for their health conditions. They way this will actually end up increasing overall costs because people will be sicker when finally going for treatment they needed all along. In addition, HSA/HDHPs, etc. replace the “social insurance” model of coverage, which equalizes the financial risk between the healthy and sick by having the well help pay for the care of ill people, with an actuarial model in which older and sicker people pay much higher premiums than the young and healthy who can accept bare bones policies.
What do you think?
Saturday, November 04, 2006
The main rebuttal to my proposed strategy is that there is no need for profound changes since our healthcare system is fine the way it is -- there is not healthcare crisis, it’s largely media hype. We should, therefore, let things change incrementally (step-by-step manner) as they have in the past. After all, we’ve made great strides in our healthcare technologies, medicines, and procedures over the years and will continue to do so if we just let market forces do their thing. And on top of that, our country doesn’t have the money to drive profound change with comprehensive strategies even if we wanted to do it. So, instead of discussing comprehensive strategies, we should limit our focus to a few tactics aimed primarily at controlling costs without spending a lot of money in the process. In terms of dealing with safety problems, it was suggested that we settle for now on getting rid of dangerous providers.
I will briefly discuss how I responded to each of these and look forward to you comments.
The vast majority of healthcare providers are intelligent and compassionate people who work very hard and do the best they can in a broken system that reward mediocrity and waste. Dedicated researchers have made wonderful breakthroughs in medicine, genetics, and medical devices that help keep us alive longer than ever with an improved quality of life. And health IT companies are developing ever better tools. Nevertheless, there is a healthcare crisis as discussed here and here.
While I agreed that changes should be done incrementally since we can’t do it all at one time, and while I agreed that we’ve made great advances over the centuries, I argued that the incremental changes should be accomplished whenever possible with leaps, not baby steps. The first leap would be to develop a “big picture view” of all the complex interacting problems with our current system. The second leap would be to use this broad & deep understanding to define and endorse a comprehensive strategy detailing all the changes necessary to solve the healthcare crisis in ways that bring the most benefit to the most people, including universal coverage and continuous quality improvement. The third leap would be to prioritize the tactics from most to least important and likely to succeed. The forth leap would be implement those tactics. And the fifth leap would be to learn from our successes and failures in a knowledge feedback-loop process that continually improves the strategy and tactics.
One reason for leaping ahead with a sense of urgency, imo, is that failure to do so will just prolong and exacerbate our problems by fostering inertia and complacency. I say this because our country has a tendency to seek superficial, short-sighted, failure-prone solutions designed to maintain much of the status quo; we tend to shy away from profound changes that “rock the boat.” Secondly, if we have the technical ability to make profound improvements in care safety, effectiveness and efficiency – which I claim we do – then why wait? Is it because we lack the will? Lack the money? Lack the leadership? Are afraid? All these things? Probably. So, shouldn’t we be focusing on ways to overcome these constraints, rather than giving into them without a fight?
Let’s assume for a moment that we have the will, courage, resources, and leadership to realize profound change. What might we focus on first so we can deal with the healthcare crisis in “bite sized pieces” rather than all at once?
Well, a majority of healthcare spending in the U.S. has been attributed to people with chronic (lifelong) conditions that can be especially difficult and expensive to treat, especially since patients do not always comply with the medical regimens, and because they may have multiple comorbidities for which evidence-based guidelines don’t exist, and there are difficulties coordinating care among many different providers working with the same patient. What should be done?
I suggested that one key factor common to dealing with all this is knowledge. Treating chronic and complex conditions safely, effectively and efficiently requires that we know a great deal about such things as: (a) patients’ problems, strengths, weaknesses over extended time periods, including physical and psychological signs & symptoms, genetic markers, attitudes and emotions, social support networks, etc.; (b) patients’ preferences (e.g., regarding quality of life issues as related to the consequence of treatments); (c) appropriate evidence-based guidelines and how to implement them; (d) self-care methods and motivators; (e) patients’ medical history; (f) what all the providers treatment a patient are doing so care can be coordinated across the entire healthcare continuum; (g) the effectiveness of care delivered through ongoing feedback; (i) whether medications prescribed are contraindicated (e.g., are likely to cause unacceptable side effects or and adverse event by interacting with other meds); (j) providers most qualified to deliver the care; etc.
Obtaining and using this knowledge effectively may require: (a) a commitment to ongoing clinical research; (b) development, evolution, and dissemination of evidence-based guidelines (including outcomes studies and consensus conferences); (c) cooperation and collaboration among healthcare professionals; (d) case management; (e) information exchange technologies (including interoperable EHR/EMRs); (f) next-generation personal health records (PHRs) that give patients ongoing feedback and reminders; (g) clinical decision support technologies (including diagnostic aids); (h) clinical guideline and outcomes research technologies; (i) patient education technologies; (j) methods for fostering patient compliance; (k) provision of mind-body medicine (e.g., http://www.thenewmedicine.org/); (l) transparency tools; (m) greater understanding of complementary and alternative interventions; (n) patient advocacy; and more. An enhanced disease management program would offer some this (see http://curinghealthcare.blogspot...-and-what.html/).
This is a comprehensive solution of profound changes. But not everything needs to be done at once, and not every patient needs it all. The objective would be to make it all available as soon as possible, so every patient could get what they need when they need it.
Some of the most pressing things to do, imo, are to administer comprehensive biopsychosocial diagnostic assessments, use and evolve existing evidence-based guidelines and develop new ones that address multiple comorbidities, enable better patient data sharing, provide ongoing feedback to patients about their health status and maintenance, learn how to motivate patients to comply with the medical regimens, develop and use effective decision-support tools, implement patient safety processes, supplement sick-care with well-care, and empower consumers to select the providers and health plans best suited to their needs.
I don’t see this as being biting off more than we can chew, but I do think it requires profound changes. Can our country afford to pay for these profound changes? Should we even bother discussing how to do it? Are there easier and cheaper solutions worth consideration? What do you think?
Tuesday, October 24, 2006
Issues we've discussed include:
- How to define a sensible rallying point -- something the public can understand and support, and something that is powerful enough to withstand the push-back from the mighty self-interests gaining from the status quo who will resist such change.
- What must be done for patients/consumers to feel confident and secure in the belief that they do/will receive the best possible care when sick -- tailored to their particular needs, characteristics, and preferences -- which is delivered in a safe, timely, and efficient (cost-effective) manner.
- How to help the public learn about and understand the serious problems with our broken healthcare system and collaborate to come up with possible solutions.
- How to empower patients/consumers to have an informed and meaningful say in their own health and healthcare.
Saturday, October 07, 2006
Following are several methods for minimizing information overload:
- Filtering. This involves defining what is useful (e.g., relevant and valid) and what isn’t, and then allowing only the useful information to be accessed. There are many different ways to filter information using software applications, which may include active or passive methods, and personal or social methods (including subject matter experts). See, for example, Collaborative Filtering, Information Filtering, and Intelligent Agent Filtering.
- “Just-In-Time” (JIT) delivery. This involves delivering information in a “just-in-time” (JIT) manner, i.e., having the particular information you need “served to you” when you need, rather than having to search for it.
- Competency-based instruction. This involves tailoring the level of instruction to one’s ability to learn. Imagine an e-learning (distance learning) system that keeps track of your knowledge level about a particular topic (domain) in the curriculum using tests to evaluate what you’ve learned after receiving instruction. You do not receive instruction on subsequent topics until you’ve learned the preliminary information you need to know. And it makes sure you recognize what you still need to learn for a particular situation.
- Personalized presentation. This involves presenting information in a manner tailored to a person’s preferences, i.e., customizing the way information is shown to minimize confusion and maximize clarity, and for maximum ease-of-use.
- Using summary/aggregated data with “slicing, dicing and drill-down” capabilities. This involves combining lots of data into a few aggregate summaries and statistical analyses that give a bird's-eye view,” identify patterns and make predictions, test for statistical significance, and enables people to examine the data from different perspectives, as well as to see the data in “finer levels of granularity” (i.e., view the underlying details). OLAP (On-Line Analytical Processing) tools and spreadsheet pivot tables are technologies that do this through data mining. It is also common to “digital dashboards.”
- Increase your level of knowledge and understanding. While the methods above rely on technology to avoid information overload, strengthening your mind by increasing what you know and understand about a topic/domain enables you to absorb (assimilate) more information in that area without becoming overloaded.
Imagine a person with a complex health problem being seen by his practitioner. A computerized diagnostic assessment tool such as the Problem Knowledge Couplers software is used to obtain comprehensive information from the patient and practitioner. It then analyzes all the patient information, matches it with an extensive healthcare database, and presents specific recommendation concerning diagnosis and treatment, with links to relevant studies and other supporting documentation, thereby focusing attention on what’s most important (via information filtering). This information, along with any other relevant patient data stored in the practitioner’s EMR/EHR (electronic medical record/health record) and the patient’s PHR (personal health record), is then display in a patient profile tailored to his particular preferences (via personalized presentation).
Once an appropriate diagnosis and treatment approach are identified, a computerized clinical guidelines system is used to recommend particular evidence-based interventions, including specific protocols to follow. Upon the practitioner’s approval, the system uses this information to generate a targeted plan of care. If the practitioner needs instruction to assist in the delivery of the selected treatment regimen, the system determines what s/he has already leaned (via competency-based instructional methods) and what s/he needs to learn now in order to deliver quality care; it then serves him/her the additional information (via JIT delivery).
When the episode of care is completed and clinical outcomes data are collected, other software application analyzes all the data and presents summary data showing how well the patient responded to treatment compared to very similar patients (via a digital dashboard) on key measures.
By having these outcomes data de-identified and sent to a central data warehouse for research and analysis, they contribute to an evolving base of clinical information that increases knowledge and understanding, thereby enabling the assimilation of even more information, resulting in ever-improving guidelines and decision support processes.
Friday, September 22, 2006
If our goal is improve healthcare quality and control costs, I contend that we should collect all the relevant data humanly possible and turn it into useful information and knowledge that increases understanding for wise decision making. But how can this be done without creating information overload?
To answer this question, let’s re-examine the definition of information overload: It is a state of having more information available than one can readily assimilate, that is, people have difficulty absorbing the information into their base of knowledge. Well, what has to happen for people to increase people's ability to assimilate information?
I contend that people with more valid knowledge about a particular knowledge domain (i.e., field or branch of knowledge, such as diagnosing medical problems), and the more they understand that domain (e.g., the better able they are to use their knowledge to answer questions about prevention, diagnosis, and treatment), then the more they information they can absorb about that domain and use it to improve their decisions. In other words, the stronger one’s foundation of knowledge about something and ability to utilize that knowledge effectively, the more one can learn and integrate into one’s existing base of knowledge without experiencing information overload.
This means that a consequence of the knowledge gap in healthcare today is people’s susceptibility to information overload. This creates a viscous cycle of information input --> information overload --> information rejection --> inhibited knowledge growth. This results in a tendency to minimize information input, e.g., by focusing on minimal data sets rather than the collection and integration of comprehensive, multidisciplinary sets of data across patients’ lifetimes described in the previous post, including patient results (clinical outcomes & costs), provider characteristics and treatment methods/processes, and patient attributes.
Breaking out of this knowledge-inhibiting cycle requires a dramatic shift in the way we view and approach health information management. This topic continues here.
Please feel free to share your comments.
Saturday, September 09, 2006
With the push to improve decision-making with electronic health records and related health information technology, a key question to be answered is: How should we deal with information overload?
I’m defining information overload as a state of having more information available than one can readily assimilate, that is, people have difficulty absorbing the information into their base of knowledge. This hinders decision-making and judgment by causing stress and cognitive impediments such as confusion, uncertainty and distraction.
Information overload can adversely affect several types of data-intensive health-related decisions, including:
- Decisions about wellness (preventing illness, maintaining health), which ought to take into account information such as a person’s behavioral and genetic risk factors, degree of physical activity/exercise, stress and emotional distress levels, use of vitamins and dietary supplements, etc.
- Decisions about diagnoses (identifying an existing health problem), which ought to consider information such as a person’s physical and psychological symptoms, lab test results (of which there are over 4,000), medical history, allergies, demographics, psychosocial problems, genetics, the mind-body connection, etc.
- Decisions about treatment selection and implementation (intervening to treat a medical and psychological health problem), which ought to be based on a person’s diagnostic information, evidence-based guidelines, personal preferences, social support network, available resources, etc.
Obtaining all this information requires the collection and analysis of a wealth of diverse data, including (but not limited to):
- Physiological/biomedical problems and risk factors, e.g., body organ and system dysfunctions/disturbances; physical pain; energy and attentional excesses and deficits; eating, sleeping, and sexual disorders; mobility problems; allergies; etc.
- Vital signs (e.g., heart beat, breathing rate, temperature, and blood pressure)
- Lab test results (e.g., general blood & urine screenings, microbiology, virology, cytopathology, histopathology, cytogenetics)
- Imaging studies
- Medications being taken
- Interventions being rendered
- Dietary supplements being used
- Medical/treatment history and personal demographics
- Affective-motivation-characterological dysfunctions/problems, e.g., intensity, frequency, and duration of negative affect and emotional stability; maladaptive and dangerous behaviors including impulsivity, compulsions, and suicidality; personality and psychiatric disorders; etc.
- Psychological vulnerabilities, e.g., sense of helplessness and hopelessness; ineffective coping strategies; low frustration tolerance; disturbing thoughts and negative emotions associated with them; traumatic experiences; self-image problems; etc.
Psychosocial distress, e.g., occupational, educational, and social/interpersonal dysfunctions; current life-stressors; etc.
- Psychoactive substance use, including alcohol & substance abuse, dependency, withdrawal
- Psychological-physiological (mind-body) interactions, including (a) biomedical illnesses/traumas that may cause or exacerbate psychological symptoms, (b) medication side-effects that may cause or exacerbate psychological symptoms, and (c) psychological factors that may cause or exacerbate physical symptoms
- Genetic markers
- ICD and DSM diagnostic codes; CPT procedures codes
- Intake and discharge/outcomes data
- Healthcare utilization data
- Consumer satisfaction
- Motivation for self-care.
If a person has a health problem for which a substantial portion of this information would improve decisions, information overload becomes a real risk because there is simply too much information for a human mind to handle. So, shouldn’t we use computers to collect and analyze all the data that may be relevant to a person’s condition?
I bet most would say use of computers to collect volumes of data about a person'e health problems makes sense if they could : (a) obtain, organize, and analyze all the relevant data without great difficulty, inconvenience, and expense; (b) keep sensitive patient data secure; (c) allow the data to be shared with authorized persons; and (d) use artificially intelligent software programs to make sense of it all and help people make better decisions.
Unfortunately, this rational vision has not been realized. While computer power and artificial intelligence capabilities continue to increase exponentially (e.g., see Ray Kurzweil’s book “The Singularity is Near”), and while there are efficient and effective ways to collect, organize, analyze, and share all these data, humanity currently lacks the knowledge and understanding needed to develop a software system able to incorporate all this information to help guide health-related decisions.
So, what should we do? Focus on collecting “minimal standard data sets” that provides some useful information and avoids overload, but are not enough to improve health decisions substantially? Or should we begin collecting comprehensive data even though we lack the ability to use it all to support decisions, even at the risk of information overload? What do you think?
This topic continues here.
Saturday, August 05, 2006
We argue the best approach is a federated “node-to-node mesh” architecture because it can incorporate all other architectures and meets these RHIO business needs:
- Has maximum reliability
- Is the least expensive to deploy
- Is the most robust since there is no single point of failure
Has unlimited scalability
- Promotes shared governance, while it supports rapid decision making at the level of the individual
- Provides early detection and correction of healthcare errors when and where they occur
- Enables patient profile data to be linked with RFID, thereby supporting information exchange in both IT-competent organizations and paper-based ones.
We discuss this issue on a new page in our wellness wiki at http://wellness.wikispaces.com/Network+Architectures
Monday, July 31, 2006
This model enables financial stability and sustainability to the membership by bringing measurable value to each of their constituencies – including healthcare providers across all disciplines, employers/purchasers, payers/insurers, researchers and educators, and patients/consumers – though the way it benefits each constituency varies. It stresses active buy-in and participation of the employer/purchasing community, which enables it to enforce disciplines of transparency/accountability on the health care community, thereby encouraging market stability. It enables and reward providers for delivering top-quality care, e.g., through building a high-fidelity healthcare system, offering P4P incentives, building practitioner-researcher collaborative networks, and complementing sick-care with well-care. And it operates through a community-based, not-for-profit organization hosted under the auspices of a neutral party, like a university.
The audience was very receptive to this RHIO model.
What I love about this model is its economic sustainability and focus on delivering health and financial benefits to all community stakeholders, while focusing on continually improving care saftety, effectiveness and efficiency through the implementation and evolution of scientific knowledge.
Here are three recent articles in the press:
Monday, June 26, 2006
“… a system of coordinated health care interventions and communications for populations with conditions in which patient self-care efforts are significant.
- Supports the physician or practitioner/patient relationship and plan of care;
- Emphasizes prevention of exacerbations and complications utilizing evidence-based practice guidelines and patient empowerment strategies; and
- Evaluates clinical, humanistic, and economic outcomes on an on-going basis with the goal of improving overall health.
Disease management components include:
- Population identification processes;
- Evidence-based practice guidelines;
- Collaborative practice models to include physician and support-service providers;
- Patient self-management education (may include primary
prevention, behavior modification programs, and compliance/surveillance);
- Process and outcomes measurement, evaluation, and management;
- Routine reporting/feedback loop (may include communication with patient, physician, health plan and ancillary providers, and practice profiling).
Note: Full service disease management programs must include all six components. Programs consisting of fewer components are disease management support services. ”
According to this definition, many disease management practices are actually providing support services only; there are few full service disease management programs in operation today.
Nevertheless, this is an excellent definition, and it is quite comprehensive. It suffers from several crucial gaps, however, which is endemic of American healthcare today:
- It doesn’t include personalized care, which means generic guidelines are used instead of developing guidelines tailored to a person’s particular needs and preferences. This means that with disease management every person with a particular illness (diagnosis) receives the same basic treatment, even though no two people are exactly alike. It’s like a sledgehammer approach to care, rather than a precise scalpel-like approach. For example, might a HemoglobanA1c of 7.5 be perfectly OK for some Type 2 diabetics, and for others 6.5 is too high, even though they have the same blood pressure and cholesterol readings, because other factors are having a affect? Since evidence-based guidelines change as new evidence is discovered, there needs to be much more research focusing on the differences between people with the same diagnosis, which disease management doesn’t address.
- It doesn’t stress the importance of practitioner-researcher collaborative networks facilitate the development and evolution of evidence-based guidelines by, for example, including patient data and lessons learned from everyday practice, and by having clinicians offer ideas for research. This also addresses the need to complement administrative (claims) data with comprehensive encounter (clinical) data.
- Nor does it address the health information technology gap, which must be bridged in order to support effective disease management programs. For example, more advanced software tools for decision-support, care-execution management, data management and sharing, and public health protection are needed.
- And it doesn’t stress the importance of supporting research on complementary and alternative medicine/interventions, which are not currently considered part of conventional healthcare.
Friday, June 16, 2006
This transformation would change American healthcare from an overly expensive and error-prone system to a value-based system that supports and rewards high-quality — effective, efficient, safe, timely and affordable care.
This can only be accomplished using a “high-fidelity” healthcare model that offers financial incentives and health information technologies to collaborative networks of practitioners, researchers, patients/consumers, and health plans that focus on reducing healthcare costs and improving outcomes through:
- Continual learning and knowledge-building
- Advanced decision-support and information-sharing
- The use of evolving evidence-based guidelines
- Integrated, coordinated, multidisciplinary care.
Tuesday, June 06, 2006
- Our country continues to focus on fiscal maneuvering, which is little more than playing with the numbers to find a short-term fix for escalating insurance costs for certain segments of the population (i.e., the healthy and wealthy) and increased liability for other segments.
- We continually fail to address key underlying problems: We don’t focus adequately on learning how to keep healthy and at-risk people well, and on discovering the safest and most cost-effective ways to treat ill patients. There is not enough investment in gaining and implementing the necessary knowledge through clinical research, evidence-based decision-support, and financial incentives.
I contend that it is impossible for any fiscal policy to improve the healthcare system long-term until we broaden our collective focus and start concentrating on doing what’s necessary to transform the system. This transformation should focus on bringing sustained improvements in healthcare safety, effectiveness, efficiency, affordability, timeliness, and availability – which is how I define high-quality care.
In addition to reforming current economic models, the transforming steps should include:
- Investing heavily in useful clinical outcomes research and evolving evidenced-based care aimed at overcoming the knowledge gap
- Funding the development and implementation of next-generation health information technologies – having advanced decision-support, knowledge generation, and public protection capabilities – which bridge current-day gaps
- Helping people stay healthy longer, recover more quickly, and avoid complications of chronic illness by balancing reactive “sick-care” with proactive well-care
- Increasing healthcare fidelity and collaboration
- Redirecting competition
- Focusing on unversal, consumer-centered, personalized care
- Addressing the mind-body connection
- Understanding and taking advantage of the value of complementary and alternative medicine and human genetics and genomics.
Friday, May 26, 2006
Consumer-directed healthcare reform models, such as health savings accounts with high deductibles, depend on giving consumers the information they need to select the providers best suited to their needs and pocketbooks. This requires transparency of cost and effectiveness. In addition, such models are designed to rewards providers with incentives for doing good work, such as "pay for performance."
This post is not focused on the debate about whether providers' performance should be evaluated. Rather, it addresses the issue of using insurance claims (administrative) data to evaluate provider effectiveness and improve the quality of care.
Claims data provide some useful measures of clinicians’ performance, including mortality rates, complications, and cost of care. These data are grossly inadequate metrics, however, for incentives, transparency of cost & effectiveness, and continuous quality improvement.
This is because claims data do not include information necessary to determine, for example, how much a patient improved after treatment, if errors were made, if lower cost treatments of equal or greater effectiveness could have been used, if the patient was educated adequately in self-care and complied with the prescribed plan of care, and if coexisting conditions affected results. Without such clinical outcomes data, it isn't possible to evaluate a provider's performance accurately nor gain the knowledge needed to improve healthcare effectiveness and efficiency.
So, instead of using claims data in isolation, they should be augmented with detailed clinical outcomes data that (a) offer more valid measures of performance, and (b) enable researchers to establish and evolve evidence-based practice guidelines.
For an in-depth technical discussion of these issues, see this WellnessWiki page.
Wednesday, May 17, 2006
It is the first comprehensive health IT (HIT) blueprint to address the six unmet needs of mainstream HIT.
The PLCW system overlaps some other HIT systems in that it:
- Provides a collaborative space with single sign-on portal technology supporting EHR/EMR/PHRs, CPOEs, e-mail, forums, HIPAA compliant file transfer, voice over IP, and end to end encryption
- Promote secured access to specific patient data using authorization rules for Trusted Partners, as well as biometrics scans and SMART Cards for authentication
- Protects patient privacy by allowing only the information a patient permits to be shared with each authorized recipient (i.e., “Trusted Partner”)
- Interoperates with third-party applications.
But what makes the PLCW blueprint unique, is it is the first to satisfy the following six unmet HIT needs:
- Bridging the Knowledge Gap
- Managing Plan of Care Execution
- Coordinating Care
- Protecting public health
- Enabling complete connectivity
- Management of extensive data sets.
Wednesday, May 10, 2006
The HIT gap is a result of not making six essential needs a top priority; that is, current HIT does not adequately focus on:
- Bridging the knowledge gap — using comprehensive, detailed knowledge of each person and the scientific research to (a) make the best possible treatment decisions within a personalized care framework, (b) deliver that care efficiently and effectively, and (c) enable all consumers to be informed participants in the healthcare decision process and in promoting their own health.
- Managing care execution — Helping providers execute their plans of care.
- Coordinating care — Coordinating care across multiple providers in the healthcare continuum, so such tools are needed.
- Protecting public health — Implementing processes for ongoing biosurveillance, post-market surveillance, and first-responder assistance in case of emergencies, so such tools are needed.
- Enabling complete connectivity — Enabling all stakeholders — patients, providers (including RHIOs, facilities, and individuals across all healthcare specialties/disciplines), purchasers, and payors — to compile and share all the data they need for which they are authorized.
- Managing extensive data sets — Fostering the fluid access, exchange, analysis and reporting of an enormous diversity of healthcare data sets, including a wide range of physiological (medical and non-medical) and psychosocial data, across patients’ entire lifetimes, about (a) people's disease/dysfunction-specific symptoms and functioning levels; (b) treatment-specific process, clinical outcomes, and practice guideline variance data; (c) genetic data; and (d) expense/financial/utilization data.
Thursday, May 04, 2006
What it Needs to Be
As the USA struggles to deal with the healthcare crisis, mandates for change — such as the National Health Information Infrastructure (NHII) initiative — focus on the use of HIT to help increase healthcare effectiveness and safety, and reduce errors and costs, by:
- Deploying decision support tools with guidelines and research results
- Fostering collaboration and accelerating diffusion of knowledge
- Improving use of resources
- Increasing workflow efficiencies
- Reducing variability in care quality and access
- Advancing the consumer role
- Strengthening privacy and data protection
- Promoting public health and preparedness.
Achieving these objectives requires changes in healthcare policies and practices, as well as interoperable HIT that:
- Helps people know the safest and most cost-effective ways to care for each patient and deliver that care in a coordinated manner across the entire healthcare continuum with minimal error and omissions (see Consumer-Centered Care).
- Helps people understand each patient’s health problems and needs in fine, clear detail, to support accurate diagnostic and treatment prescription decisions (see Personalized Care).
- Helps people create and use evidence-based practice guidelines.
Helps people know how to prevent illness and promote wellness for each person, and deliver such wellness/prevention programs.
- Promotes consumer/patient participation through increased knowledge and decision-support, which benefits them by increasing their ability to select the right providers and health plans, prevent illness/complications/accidents by focusing on self-care and wellness, and reduce complications of chronic disease by complying with plans of care.
- Promotes provider participation through increased knowledge, decision-support, and workflow efficiencies, which benefits them by increasing their ability to deliver more cost-effective treatment and increase patient safety (reduced errors and omissions).
- Promotes payer participation through increased knowledge, decision-support, and workflow efficiencies, which benefits them by increasing their ability to contain costs and take advantage of new business opportunities.
- Promotes purchaser participation through delivery of more cost-effective care to employees, which benefits them by reducing healthcare expenditures, absences, and turnover, as well as improvements on-the-job productivity.
- Enables collaborative networks to improve healthcare quality by helping them;
- Perform clinical research in the field and lab through streamlined collection, sharing, and analysis of large quantities of diverse clinical outcomes data
- Build evolving health science knowledgebases based on the clinical research and transform this knowledge into evidence-based practice guidelines
- Disseminate, implement, and evolve the guidelines
- Communicate easily and efficiently to share observations and lessons learned, handle disputes, and help run collaborative organizations.
- Protects populations by offering an efficient and effective way to obtain, transmit, and analyze biosurveillance and post-market surveillance data and by assisting first responders in the event of a wide-spread emergency (e.g., bioterrorism, epidemic).
- Helps utilize resources more efficiently.
- Helps people transfer data and information in a shared environment.
- Helps people use scaleable, integrated software applications.
What HIT is Now
Efforts these days focus on the most basic functional level of HIT, i.e., the development of interoperable architectures and the use of applications for inputting, validating, storing, securing, and exchanging basic patient data. Current HIT also offers some decision-support through reminders (e.g., of follow-up appointments, inoculations, etc.) and alerts via medication prescription checks, and streamline certain workflows. All this is a necessary first step, but it is grossly insufficient.
Does anyone disagree?
In my next post, I define the HIT gap and what can be done to bridge it.
Friday, April 28, 2006
One way to keep such a perspective is to see the solution as a series of changes in healthcare policies and practices focused on enabling all patients to receive the most cost-effectiveness care – be it well-care (i.e., prevention), catastrophic care, and compassionate end-of-life care. This means:
- Patients and providers must know the best (most cost-effective) treatments/interventions for each particular health problem/risk
- Providers must be able and motivated to deliver that care
- Patients must able to select the best providers to treat them and be motivated to comply with their plans of care.
1. We have to replace ignorance (see the Knowledge Gap) with a concerted collaborative effort to gain the knowledge needed to make better decisions. Two tactics to achieve this are;
- The development, dissemination, and implementation of evolving evidence-based practice guidelines, which includes dealing with the difficulty of using outcomes data to evaluate performance. Implementing this tactic requires addressing several problems with current practice guidelines and quality improvement (QI) programs.
- The use of evolving health information technology (HIT) tools, including advanced knowledge tools for supporting diagnostic and treatment prescription decisions and driving an evidence-based decision support process. There are several concerns about HIT and solutions.
3. Treatment decisions should be tailored to the specific needs of the individual patient taking into account the person’s age, gender, race, genetics, environment, concomitant treatments, quality of life preferences, and other factors that may be relevant to a high-quality plan of care (see Personalized Care ).
4. Increasing provider motivation to change is another issue needing resolution. This is related to the Pay for Performance (P4P) issue.
5. Creating a sane payment system would certainly help, including addressing the issue of transparency of care cost and effectiveness.
6. Consumers should be better informed so they can distinguish among levels of quality by knowing the relative cost and degree of defect (underuse, overuse, and misuse) of healthcare resources.
7. Consumer education and wellness programs are also important so people can help prevent their own health problems and managed chronic conditions effectively.
Friday, April 14, 2006
Why is this? The authors conclude a lack of training and knowledge about how to adopt patient centered care into their practices, as well as a concern about costs.
"With the right knowledge, tools, and practice environment, and in partnership with their patients, physicians should be well positioned to provide the services and care that their patients want and have the right to expect."
Another way of saying this is that we need a "high-fidelity" healthcare system. Fidelity exists only when healthcare systems enable:
- Patients to make their care needs known to providers through adequate access and communication
- Clinicians to have the time, knowledge, skill, and attention necessary to recognize a patient needs and intervention
- Interventions to be delivered properly, safely, and in a coordinated manner.
A high-fidelity healthcare system:
- Makes it possible for coordinated teams of clinicians to render care across the entire healthcare continuum
- Assures that providers have adequate resources, and competent information and decision support tools
- Is fully committed to consumer-centered care.
Thursday, April 06, 2006
Collaborating communities of healthcare professionals and consumers are essential to solving the healthcare crisis. Through news feeds, conversation and knowledge-sharing, these communities help increase people's understanding, spark innovation, provide decision-support, enable better problem-solving, and mobilize grass-roots efforts.
The most practical way to do this is through "virtual communities" using communication vehicles such as blogs, wikis, forums, list serves, and real-time workspaces. Each of these technologies has certain unique capabilities, as well as overlapping functions.
Following is a proposal that combines these different vehicles in an optimal way enabling virtual communities to generate, evaluate, evolve, and implement strategies and tactics for solving the healthcare crisis from the "bottom-up" through grass-roots action.
Unlike a blog, a wiki is an excellent vehicle for organizing content, like an encyclopedia, using links to pages via a table of contents or taxonomy, which gives a comprehensive “whole-picture” view. It enables the reader to navigate quickly and easily from topic to topic, category to category, and can handle many different links to reference materials and other external information sources. And it allows collaborative editing by authorized contributors who can modify and add content right on the wiki pages. They also enable comments, as does a blog.
Blogs, on the other hand, do not organize content into a comprehensive view. Instead, posts appear chronologically, not organized by topic. This is a good way to deliver a variety of different issues for review and discussion, like a news feed. The reader scans through the issues of the day, reads the ones they want, and post comments as desired. When the comments stimulate community dialogue, blogs help surface new ideas, challenge people’s assumptions, and lead to greater learning.
So, wikis are like encyclopedic organizers and blogs are like newsfeeds, both of which enable conversations. How can they be used together for maximum benefit?
Well, when it comes to focusing virtual communities on something as complex as finding solutions to the healthcare crisis, I suggest using our Wellness Wiki as a vehicle for organizing and growing content, and using a community of blogs to “feed” the wiki. What I mean is that the blog posts would:
- Present timely information (news, studies, insights, opinions, experiences, questions, etc.) on different topics of interest (some of which might even be based on content from the wiki itself).
- Engage people in short-term dialogue about that information.
- Have links to them from the appropriate wiki pages, so people reading a topic on the wiki can easily navigate to the associated blog posts. If a post does not have a corresponding page on the wiki, because it introduces a topic absent from the wiki, then the wiki should be expanded to include that topic.
- Contain links to specific pages in the wiki as is appropriate.
In some ways, this is like a blogs aggregator, except that each blog post is organized in the wiki by topic/category, not by the time period in which the posts happen to appear.
If certain topics in the wiki require deep continuous dialogue, lengthy posts, branches into sub-topics, voting capabilities, file exchange, etc., then a virtual forum can be set up for that purpose.
If any bloggers are interested, please contact me.
Friday, March 31, 2006
Professionally, I’m a clinical psychologist, healthcare practitioner, researcher, and software inventor who serves as the President/CEO of National Health Data Systems, Inc. (NHDS), a privately held company founded in 1994.
In 1981, while a practicing psychologist, I began developing a healthcare information system to help me deliver the best possible care by better understanding my patients' problems, determine the best courses of action, evaluate outcomes (the results/consequences of such actions), and continually learn from experience.
By the mid 1980’s, I had developed the key components of the Psychological Services Index™ (PSI) System and began using it in my practice. I soon realized there was more I wanted to know. Not only did I want a way to learn about my patients’/clients’ mental health problems, but I also wanted to a way to know about any related physiological (bodily, medical, somatic) factors that were affecting them. To accomplish this, a team of colleagues and I set out to create the first information technology providing a comprehensive, in-depth, “biopsychosocial” view of patients’ conditions and treatments. This led to a 15-year journey of intensive, cross-discipline R&D (research and development). In the late 1990’s, we succeeded in developing a universal lifetime computerized patient record system with advanced decision support capabilities and a virtual forum supporting interdisciplinary collaboration. We named this software technology the Health Information Index™ (Hii™) System.
In the early 1990’s, as our country attempted to deal with the healthcare crisis of the 20th century, I realized that the efforts being proposed — managed care and capitation — would have to fail because these fiscal strategies didn’t focus on improving care effectiveness and safety. Neither did these strategies promote continuous quality improvement through the implementation of evidence-based practice guidelines, nor the use of information technology for knowledge-building and decision support. And they were fraught with dangers in which those who need healthcare most are the least likely to get it due to things like “cherry-picking,” in which insurers recruit the healthiest clients and avoid chronic patients with expensive health care needs and when providers focus on offering only the most profitable healthcare services while selectively choosing not to provide services that involve more risk, more medical attention or time, more expense those services that do not have a handsome return on investment; a problem that continues today. Another serious problem is that these strategies squeeze providers by paying them to treat as many patients as possible for lowest cost, without adequate focus on the quality of care delivered. We now see the results of such failed strategies in our current 21st century healthcare crisis.
In 1993, I attempted to reach our country’s leaders with a healthcare reform proposal centered on a “national health data system” and creation of an “electronic health information network” which, by the way, is eerily similar our government’s recent call for a “national health information network” (NHIN). The proposal laid out a strategic blueprint for a system supporting collaborative teams of practitioners and researchers across the country using advanced information technologies to build a storehouse of scientific healthcare data. These data would be analyzed, discussed, and transformed into evidence-based practice guidelines, which would be disseminated to all providers. The technology I’d been developing was a step toward realizing this vision. I received no response from the government, however. A year later, we founded our company and named it National Health Data Systems (NHDS).
At the same time, we had begun introducing the PSI System to the mental healthcare field in an attempt to recruit a large group of healthcare professionals to form a collaborative practitioner-researcher network. Our mission was to have this network help evaluate and evolve the technology, and to use it for building a large biopsychosocial knowledgebase. A key strategy of the network was to take a proactive approach with managed care companies by obtaining and using a wealth of scientific evidence and decision tools to support and justify clinical interventions. Unfortunately, the mental healthcare field was generally opposed to this approach and our attempts to establish the network failed. We then shifted our focus away from mental healthcare, per se, to opportunities in other healthcare fields, and beyond.
In 1997, I used the knowledge gained over the years to write a patent for the CP Split™ technology, which was granted a year later. The patent describes a uniquely flexible and efficient process for exchanging and presenting information, which is an ideal platform for supporting healthcare decision-making and knowledge-building in collaborative environments.
In 1998, we developed the Joint Commission on Accreditation of Healthcare Organizations’ IMSystem, which evaluates hospital performance, and NHDS became an approved vendor. That same year, we developed a clinical pathways system for Merck UK, in alliance with UK physicians, which helps diagnose and treat certain heart problems, as well as determine which interventions are most cost-effective. We later developed computerized practice guidelines, case management, and treatment planner tools — all of which also focus on quality improvement. Because of these developments, we were able to integrate the PSI system with biomedical applications, to generate the Hii System, with its universal life-time, electronic health record with built-in decision support.
Sadly, I came up against great resistance from the American healthcare system for the past two decades as I presented our ideas and technologies. Although supported by a small network of healthcare visionaries, we were generally scorned or simply ignored by the healthcare industry — not because of poor technology or faulty ideas, but because the American healthcare system simply wasn’t ready for this type of change. So, while we continued to develop innovative solutions, we were rendered powerless as our healthcare system continued to deteriorate and our company struggled to survive. Why didn’t I give up long ago? Many said I should … it was a losing battle … the system would never change!
What kept me motivated during all these years of disappointment and frustration is a personal life mission to do whatever I can to help improve the world’s health and well-being by enabling delivery of affordable, high-quality healthcare to all people in all nations. If our country focuses sincerely on the same mission, I believe many of the problems we face at home and abroad would begin to repair themselves, and we wouldn’t have to be ashamed of the world we’re leaving our children.
Thankfully, a window of opportunity, for which I’ve been waiting a quarter century, has opened in the spring of 2005 with our government’s initiative to build a national health information network and other strategies to improve healthcare quality and control expenditures. We are responding to this opportunity by presenting a solution evolving over the past 15 years — focusing on a wellness model and quality through knowledge strategy that benefits all healthcare stakeholders — which is aligned with our mission to help improve the world’s health and well-being.
I welcome your comments on this blog, and invite you to post any suggestions, critiques, and questions.
To your health,