Don’t pave the cow paths: The challenge of re-conceptualizing health care processes

There is a popular adage for information technology professionals: “Don’t pave the cow paths.”

I recently worked with a client from Texas, and they were fond of this adage. They said it with the most excellent drawl, giving it extra credibility, as if they may have actually learned it the hard way on their own ranches.

In the IT context, I interpret the adage to mean:

When designing an information technology solution for a business area, don’t just learn how they did the process manually and then create a software application to transfer that same process to computers. Rather, try to understand the underlying problem that the business process is trying to solve and the value that the business process is intended to create, and then take the opportunity to design a different processone that is rendered feasible with enabling information technology and that delivers greater value.

When designing a new process to replace an old one, the starting point is re-conceptualization. The process designer must shed some of the terminology used to describe the old process when that terminology is too strongly tied to the details of the old process. Rather, the designer must dig down to the more essential concepts, and choose labels that are simpler and purer, seeking fresh metaphors to provide cleaner terminology. Then, the new process and the associated data structures can be re-constructed on top of a conceptual foundation that is easier to talk about, simpler to manage, and more stable.

Once we have a strong conceptual foundation, we can then flesh out the details of how the process can be made leaner and more effective, enabled by information technologies. Obviously, the proposed new process design influences the selection and configuration of enabling technologies. But, awareness of the capabilities of various technologies can also help generate ideas about candidate process designs that will be rendered feasible by the technologies. Therefore, this process is inherently iterative. The old-school philosophy of getting sign-off on detailed system requirements before considering the technology solution was a response to a valid concern that people will fall in love with a technology and then inappropriately constrain their process design accordingly. But, applying that philosophy too rigorously causes the opposite problem. If process designers don’t know what’s possible, they naturally stick with their old conceptualization, which also serves to inappropriately constrain their process design. As with most hard things, effectiveness requires finding the right balance between two undesirable extremes.

An example: the case of “registries.”  

A “registry” is a list of patients. The list includes the evidence-based services they need and whether or not they have received them. It is like a tickler file to help members of the clinical team remember what preventive services and chronic condition management services need to be done, so the team can improve their performance on quality of care metrics and provide better care to their patients.

But, if you dig down, the more fundamental purpose of the registry can be conceptualized as enabling care relationship management and care planning processes. Conceptually, health care providers need to know which patients they are taking care of. That’s care relationship management. It involves integrating different sources of information about care relationships, including derived care relationshpis based on claims data (also called “attribution”) and declared care relationships from health plans, patients and physicians. Part of the function of a registry is to clarify and make explicit those care relationships. This simple function can be considered  radical to clinicians who have become accustomed to an environment where such care relationships have been ambiguous and implicit.

If a physician has a care relationship with a patient, then, conceptually, he or she has a professional responsibility to make and execute a plan of care for that patient. Care planning is the process of determining which problems the patient has and what services are needed to address those problems. Conceptually, a good care planning process also includes provisions for multi-disciplinary input by members of the clinical team.  And, a good care planning process also includes decision support, including alerts for necessary things missing from the care plan, and critique of things that have been put on the care plan.  Such critique can be based on clinical grounds, such as drug-drug interactions, drug allergies and drug dosing appropriateness. Or, they can be based on evidence-based medicine or health economic grounds, such as in utilization management processes.

The name “registry” is tied historically to the word “register” which is a type of paper book used for keeping lists of things. In the health care context, “registries” were used by public health officials to track births, outbreaks of infectious diseases and cancer tumors. So, when people think about chronic disease registries, their mental model of keeping a paper list is a barrier to their willingness to re-conceptualize the underlying function differently.  But, more fundamentally, a “registry” is just one type of tool to facilitate care relationship management and care planning — a tool designed to be used for a narrowly defined list of problems and services, rather than being designed for more general use.

Today, there is no single care plan for most patients.  The function of keeping track of the problems that need to be addressed is either not done or it is done in a haphazard way, peppered across various structured and unstructured encounter notes, scribbled on problem lists, auto-generated in clinical summaries based on diagnosis codes on billing records, checked off on health risk appraisals, etc.   The function of figuring out which services are necessary to address each problem is peppered across numerous clinical notes, requisition forms, e-prescribing systems, order entry systems, care management “action lists” and in the fields of registry systems.  The function of facilitating interdisciplinary input to a patient’s care occurs informally in hallway conversations, morning rounds, tumor board meetings, or, most commonly, not at all.  These are all care planning functions, but most clinicians have no familiarity with the concept of linking these diverse bits of data and process in a cleaner, simpler notion of managing a single care plan to be used and updated over time by the entire care team.  As far as they are concerned, such a notion is probably infeasible and unrealistic.  They’ve never seen a technology that can enable it to become reality.

Choosing the right leap distance.

Of course, when re-conceptualizing processes, it is possible to go too far.  Old habits, mental models, terminology, and processes die hard.  If your re-conceptualization is a great leap to a distant future state of elegantly conceptualized processes, it might end up being too difficult to convince people to take the leap with you.  Other adages come to mind:  “Don’t get in front of your headlights.” Then there is President Obama’s version: Don’t get “out over your skis.”  And my favorite, often quoted by Tom Durel, my old boss at Oceania, “Never confuse a clear vision for a short distance.”

The optimal “leap distance” is a function of motivation to change.  If people start to experience great pain in their current state and begin to fear the consequences of sticking to their old ways, change happens.  As we move forward to a world where providers are taking more economic risk and facing more severe consequences for failing to improve quality of care, we will be able to pursue bolder innovation and leap greater distances in our process and technology improvements.

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Reports of the death of Cost-Effectiveness Analysis in the U.S. may have been exaggerated: The ongoing case of Mammography

Guidelines for the use of mammograms to screen for breast cancer have been the topic of one of the fiercest and longest-running debates in medicine.  Back in the early 1990s, I participated in that debate as the leader of a guideline development team at the Henry Ford Health System.  We developed one of the earliest cost-effectiveness analytic models for breast cancer screening to be used as an integral part of the guideline development process.  I described that process and model in an earlier blog post.  Over the intervening 20 years, however, our nation has fallen behind the rest of the world in the use of cost-effectiveness analysis to drive clinical policy-making.  As described in another recent blog post, other advanced nations use sophisticated analysis to determine which treatments to use, while Americans’ sense of entitlement and duty have turned us against such analysis — describing it as “rationing by death panels.”  Cost-effectiveness analysis and health economics is dead.

But, maybe reports of its death have been exaggerated.

recent paper published on July 5, 2011 in the Annals of Internal Medicine described the results of an analysis of the cost-effectiveness of mammography in various types of women.  The study was conducted by John T. Schousboe, MD, PhD, Karla Kerlikowske, MD, MS, Andrew Loh, BA, and Steven R. Cummings, MD.  It was described in a recent article in the Los Angeles Times.  The authors used a computer model to estimate the lifetime costs and health outcomes associated with mammography.  They used a modeling technique called Markov Microsimulation, basically tracking a hypothetical population of women through time as they transition among various health states such as being well and cancer free, having undetected or detected cancer of various stages and, ultimately, death.

They ran the models for women with different sets of characteristics, including 4 age categories, 4 categories based on the density of the breast tissue (based on the so-called BI-RADS score), whether or not the women had a family history of breast cancer, and whether or not the women had a previous breast biopsy.  So, that’s 4 x 4 x 2 x 2 = 64 different types of women.  They ran the model for no-screening, annual screening, and screening at 2, 3 or 4 year intervals.  For each screening interval, they estimated each of a number of health outcomes, and summarized all the health outcomes in to single summary measure called the Quality-Adjusted Life Year (QALY).  They also calculated the lifetime health care costs from the perspective of a health plan.  Then, they compared the QALYs and costs for each screening interval, to the QALYs and costs associated with no screening to calculate the cost per QALY.  Finally, they compare the cost per QALY to arbitrary thresholds of $50K and $100K to determine whether screening at a particular interval for a particular type of women would be considered by most policy-makers to be clearly costs effective, reasonably cost-effective, or cost ineffective.

The authors took all those cost effectiveness numbers and tried to convert it to a simple guideline:

“Biennial mammography cost less than $100 000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50 000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.”

Not exactly something that rolls off the tongue.  But, with electronic patient registries and medical records systems that have rule-based decision-support, it should be feasible to implement such logic.  Doing so would represent a step forward in terms of tailoring mammography recommendations to specific characteristics that drive a woman’s breast cancer risk.  And, it would be a great example of how clinical trials and computer-based models work together, and a great example of how to balance the health outcomes experienced by individuals with the economic outcomes borne by the insured population.  It’s not evil.  It’s progress.

It will be interesting to see if breast cancer patient advocacy groups, mammographers and breast surgeons respond as negatively to the author’s proposal as they did to the last set of guidelines approved by the U.S. Preventive Services Task Force which called for a reduction in recommended breast cancer screening in some categories of women.


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Improving Adult Flu Immunization


The US Preventive Services Task Force established national evidence-based guidelines for adult immunization as part of their 1993 and 1996 review processes. After a review by Henry Ford Health System (HFHS) general internists, family practitioners, and infectious disease specialists, these guidelines were endorsed by the HFHS Prevention Committee. The Prevention Committee’s recommendations were subsequently accepted by the Henry Ford Medical Group Clinical Practice Committee and by the Health Alliance Plan Quality Management Committee to form practice guidelines for the medical group and health plan, respectively.


A multi-faceted implementation approach was used to improve immunization performance within the Henry Ford Medical Group, one of the provider groups that serve the Health Alliance Plan. Multi-faceted implementation efforts have been found to be most effective for improvement processes that involve behavior change. The implementation effort involved staff training, patient and member education, continual quality improvement, and medical informatics facets.

  • Staff Training.  All senior medical staff within the Henry Ford Medical Group were sent a copy of internal adult immunization guidelines, which were incorporated into a larger preventive health services manual. In addition to this mailing, a number of clinicians had the opportunity to discuss these guidelines in an on-site continuing medical education program on the provision of preventive health services.
  • Patient education was accomplished by publishing an article in the Health Alliance Plan’s member newsletter, which is mailed to all health plan members. A slogan and cartoon character were also developed for a program to promote immunization compliance. This content was incorporated into posters and tent cards, which were placed in clinics lobbies and waiting areas.
  • Local continuous quality-improvement teams were established to develop and implement process changes. These teams launched Saturday morning walk-in flu immunization clinics during flu season. By using this approach to immunization, wait times were reduces and patients were able to avoid setting up an appointment to get the immunization.
  • Medical informatics approaches were pursued, leveraging the capabilities of the clinical information system and data depositories that are available within the insitution. Computer-generated reports were created, listing immunization status for HFHS adult patients. These reports were used by staff of the Saturday morning walk-in flu clinics. In addition, computer-generated reminder postcards and letters to patients at high risk for influenza were printed and mailed.


These mailings were formally evaluated in a randomized trial conducted during the first year of the HFHS flu immunization program. The entire patient population for whom flu immunizations were indicated were randomized into four groups. The control group (usual care) received only the posters and tent cards in the clinic. Patients in each of three treatment groups received this same clinic-based intervention and either (1) a generic postcard, (2) a tailored postcard, or (3) a tailored letter containing an explicit statement of why flu immunization was indicated for them. Indications included age over 65 and the presence of one of a number of disease states, as ascertained from diagnosis coding of ambulatory visits and inpatient admissions. These letters or postcards were addressed from the patients’ primary care clinicians. The results of randomized trial showed a 5 percentage point increase in the rates of immunization in the patient population receiving the tailored letter compared to those receiving usual care.

The cost of the letter, including printing and postage, was 42 cents. The vaccine costs just over $4.00. From the literature, it is known that annual hospital costs are reduced from an average of $355 to $215 as a result of flu immunization. Therefore, during a non-epidemic year, the flu immunization program was calculated to save the HFHS over $180,000, net of the cost of the intervention. During an epidemic year, this savings is increased to almost $400,000. Therefore, the flu immunization project serves as an example of a clinical-practice improvement effort that simultaneously benefits the health status of patients and reduces health care cost.

The National Committee for Quality Assurance (NCQA) adopted in 1996 a new adult immunization performance measure which is included in the Health Plan and Employer Data and Information Set (HEDIS 3.0). These quality indicators have been reported by hundreds of health plans across the country, and are compiled in the NCQA Quality Compass, a national health plan quality database.

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