Congressional Budget Office: Care management programs only work if care managers have face to face contact with patients and substantial interaction with physicians

This month, Lyle Nelson of the Congressional Budget Office (CBO) released a “working paper” summarizing the results of a decade of experience with 6 care management demonstration projects in the Medicare population.  These demonstrations included a total of 34 disease management or care coordination programs. Nelson briefly summarized the working paper in a recent blog post.

All of the 34 care management programs were designed to reduce Medicare costs primarily by maintaining or improving the health of the Medicare beneficiaries, and thereby reducing the need for expensive inpatient hospital stays.  As shown the graph below, different programs showed different effects on the rate of hospital admissions.  On average, the programs showed no effect.
 

Effects of 34 Disease Management and Care Coordination Programs on Hospital Admissions (Percentage Change in Hospital Admissions)

 

The CBO analyzed whether specific characteristics of programs led to better or worse results. They found that programs where the care management provider’s fees were at risk did not perform better or worse than those with fees not at risk.  However, they did find two things that worked.  They found that programs in which care managers had substantial direct interaction with physicians and those with significant in-person interaction with patients reduced hospital admissions by an average of 7%, while programs that did not have these features had no impact on hospital admissions.

But, after subtracting the cost of the programs themselves, almost none of the programs achieved net savings.

The programs with the most compelling performance included:

  • Massachusetts General Hospital and its affiliated physician group reduced hospital admissions between 19-24% among patients selected as “high risk” using a program that was far more tightly integrated with the health care delivery system.  Physicians in the group were involved in the design of the intervention, and care managers were staff members in primary care physicians’ practices.  The patients received the vast majority of their care within the integrated delivery system, so almost all of their health information was available and up-to-date in an electronic medical records system.  Care managers were notified immediately when a patient was admitted to the emergency room or hospital.  They had an opportunity for face-to-face interaction with patients in the clinic.  And, they had access to a pharmacist to address medication issues.
  • Two multi-specialty group practices in the Northwest reduced hospital admissions by 12-26% among high risk patients using a program that involved telemonitoring with the “Health Buddy” device that transmitted symptoms and physiologic measurements to a care manager
  • Mercy Medical Center in rural Iowa reduced hospital admissions by 17% among patients hospitalized or treated in the ER in the prior year for CHF, COPD, liver disease, stroke, vascular disease, and renal failure using a program that involved care managers, many of which were located in physician offices and/or accompanied patients on their physician visits.

The methods used for these evaluations were far stronger than those used by the self-evaluations typically advertised by vendors of care management services.  In the CBO reports, 30 of the 34 programs were evaluated based on a comparison to a randomly selected comparison group.  The remaining 4 programs were evaluated using a concurrent comparison group selected using the same selection criteria.  In all cases, the programs were evaluated on an “intent to treat” basis, where study subjects were included in the evaluation regardless of whether they participated in the voluntary programs, thereby removing a source of bias that causes mischief in less rigorous evaluations.

To me, the take-away message is that provider-based care management is promising, but health-plan-style telephonic care management has not been successful, even in a senior population, where finding high risk targets is far easier and even when the care management services provider is highly motivated to succeed.

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The debate about what to maximize when selecting candidates for care management programs: Accuracy? ROI? Net Savings? Or Cost-Effectiveness?

When doing analysis, it is really important to clarify up front what it is you are actually trying to figure out.  This sounds so obvious.  But, I am always amazed at how often sophisticated, eager analysts can zip past this important first step.

Health plans, vendors and health care providers are all involved in the design and execution of wellness and care management programs.  Each program can be conceptualized as some intervention process applied to some target population.  A program is going to add more value if the target population is comprised of the people for whom the intervention process can have the greatest beneficial impact.

I have found it useful to conceptualize the process of selecting the final target population as having two parts.  The first part is the process of identification of the population for whom the intervention process is deemed relevant.  For example, a diabetes disease management program is only relevant to patients with diabetes.  An acute-care-to-ambulatory-care transitions program is only relevant to people in an acute care hospital. A smoking cessation program is only relevant to smokers.  The second part is to determine which members of the relevant population are to actually be targeted.  To do this, a program designer must figure out what characteristics of the relevant candidates are associated with having a higher opportunity to benefit from the intervention.  For example, in disease management programs, program designers often use scores from predictive models designed to predict expected cost or probability of disease-related hospitalization.  They are using cost or likelihood of hospitalization as a proxy for the opportunity of the disease management program to be beneficial.  Program designers figure that higher utilization or cost means that there is more to be saved.  This is illustrated in the graph below, where a predictive model is used to sort patients into percentile buckets.  The highest-opportunity 1% have have an expected annual admit rate of almost 4000 admits per 1000 members, while the lowest 1% have less than 200 admits per 1000 members.  A predictive model is doing a better job when more of the area under this curve is shifted to the left.

Although it is common to use expected cost or use as a proxy for opportunity, what a program designer would really like to know is how much good the intervention process is likely to do.  Other variables besides expected cost or use can contribute to a higher opportunity.  For example, in a disease management program, the program might be more worthwhile for a patient that is already motivated to change their self-management behaviors or one that had known gaps in care or treatment non-compliance issues that the intervention process is designed to address.

Applying micro-economics to care management targeting

Once the definition of “opportunity” is determined and operationally defined to create an “opportunity score” for each member of the relevant population, we can figure out which members of the relevant population to actually target for outreach for the program.  Conceptually, we would sort all the people in the relevant population by their opportunity score.  Then, we would start by doing outreach to the person at the top of the list and work our way down the list.  But, the question then becomes how far down the list do we go?  As we go down the list, we are investing program resources to outreach and intervention effort directed at patients for which the program is accomplishing less and less.   Economists call this “diminishing returns.”

As illustrated by the red line in the graph above, there is some fixed cost to operating the program, regardless of the target rate.  For example, there are data processing costs.  Then, if the program does outreach to a greater and greater portion of the relevant population, more and more people say “yes” and the costs for the intervention go up in a more or less linear manner.  As shown by the green line, the savings increase rapidly at first, when the program is targeting the candidates with the greatest opportunity.  But, as the threshold for targeting is shifted to the right, the additional candidates being targeted have lower opportunity, and the green line begins to flatten. The blue line shows the result of subtracting the costs from the savings to get net savings.  It shows that net savings increases for a while and then begins to decrease, as the cost of intervening with additional patients begins to outweigh the savings expected to accrue from those patients.  In this analysis, net savings is highest when 41% of the relevant population of diabetic patients is targeted for the diabetes disease management program.  The black dotted line shows the result of dividing savings by cost to get the return of investment, or ROI.   With very low target rates, too few patients are accumulating savings to overcome the fixed cost.  So the ROI is less than 1.  Then, the ROI hits a peak at a target rate of 18%, and declines thereafter.  This decline is expected, since we are starting with the highest opportunity patients and working down to lower opportunity patients.   Note that in this analysis, increasing the target penetration rate from 18% to 41% leads to a lower ROI, but the net savings increases by 24%.  So, if the goal is to reduce overall cost, that is achieved by maximizing net savings, not by maximizing ROI.

Should we try to maximize accuracy?

In a recent paper published in the journal Population Health Management by Shannon Murphy, Heather Castro and Martha Sylvia from Johns Hopkins HealthCare, the authors describe their sophisticated methodology for targeting for disease management programs using “condition-specific cut-points.”  A close examination of the method reveals that it is fundamentally designed to maximize the “accuracy” of the targeting process in terms of correctly identifying in advance the members of the relevant disease-specific population that will end up being among the 5% of members with the highest actual cost.   In this context, the word accuracy is a technical term used by epidemiologists.  It means the percentage of time that the predictive model, at the selected cut-point, correctly categorized patients.  In this application, the categorization is attempting to correctly sort patients into a group that would end up among the 5% with highest cost vs. a group that would not.  By selecting the cut point based accuracy, the Hopkins methodology is implicitly equating the value of the two types of inaccuracy: false positives, where the patient would be targeted but would not have been in the high cost group, and false negatives, where the patient would not be targeted but would have been in the high cost group. But, there is no reason to think that, in the selection of targets for care management interventions, false negatives and false positive would have the same value. The value of avoiding a false negative includes the health benefits and health care cost savings that would be expected by offering the intervention. The value of avoiding a false positive includes the program cost of the intervention.  There is no reason to think that these values are equivalent.  If it is more important to avoid a false positive, then a lower cut-point is optimal.  If it is more valuable to avoid a false negative, then a higher cut-point is optimal.  Furthermore, the 5% cost threshold used in the Hopkins methodology is completely arbitrary, selected without regard to the marginal costs or benefits of the intervention process at that threshold.  Therefore, I don’t advise adopting the methodology proposed by the Hopkins team.

What about cost-effectiveness?

The concept of maximizing ROI or net savings is based on the idea that the reason a health plan invests in these programs is to save money.  But, the whole purpose of a health plan is to cover expenses for necessary health care services for the beneficiaries.  A health plan does not determine whether to cover hip replacement surgery based on whether it will save money.  They cover hip replacements surgery based on whether it is considered a “standard practice,”  or whether there is adequate evidence proving that the surgery is efficacious.  Ideally, health care services are determined based on whether they are worthwhile — whether the entire collection of health and economic outcomes is deemed to be favorable to available alternatives.  In the case of hip replacement surgery, the health outcomes include pain reduction, physical function improvement, and various possible complications such as surgical mortality, stroke during recovery, etc.  Economic outcomes include the cost of the surgery, and the cost of dealing with complications, rehabilitation and follow-up, and the savings from avoiding whatever health care would have been required to deal with ongoing pain and disability.  When attempting to compare alternatives with diverse outcomes, it is helpful to reduce all health outcomes into a single summary measure, such as the Quality-Adjusted Life Year (QALY).  Then, the incremental net cost is divided by the incremental QALYs to calculate the cost-effectiveness ratio, which is analogous to the business concept of return on investment.  If the cost-effectiveness ratio is sufficiently high, the health service is deemed worth doing.  There is no reason why wellness and care management interventions should not be considered along with other health care services based on cost effectiveness criteria.

The idea that wellness and care management interventions should only be done if they save money is really just a consequence of the approach being primarily initiated by health plans in the last decade.  I suspect that as care management services shift from health plans to health care providers over the next few years, there will be increased pressure to use the same decision criteria as is used for other provider-delivered health care services.

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Hospitalists have been focused on reducing hospital length of stay, but not so much on smooth transitions to ambulatory care

A new study published in the  Annals of Internal Medicine compared the economic outcomes of hospital episodes managed by hospitalists to those managed by the patients’ primary care physicians in a Medicare population. Previous studies focused only on the cost of the hospital stay itself, and showed that hospitalists were able to reduce length of stay and hospital cost. These economic savings accrue primarily to hospitals who are reimbursed with a fixed DRG-based payment for most hospital stays. These hospital savings have motivated hospitals to hire more hospitalist physicians. According to the Society of Hospital Medicine, 80 percent of hospitals with more than 200 beds now have hospitalists.  There are now 30,000 hospitalist physicians, and the specialty continues to grow more rapidly than any other.

But, the new study measures the economic outcomes of the entire hospital episode, including care received after the patient is discharged from the initial hospital stay.   The study shows that hospital stays managed by hospitalists had an average length of stay that was 0.64 days shorter, saving an average of $282. But, those patients were more likely to return to the emergency department and more likely to be readmitted to the hospital, leading to post-discharge costs that averaged $332 higher than for hospital episodes managed by the patients’ own primary care physicians. Thus, the use of hospitalists ends up costing Medicare $50 more per episode, increasing overall costs by $1.1 billion annually.

The study authors, Yong-Fang Kuo, PhD and James S. Goodwin, MD from the University of Texas Medical Branch in Galveston, hypothesized that ”hospitalists, who typically are employed or subsidized by hospitals, may be more susceptible to behaviors that promote cost shifting.” The implication is that, if hospitalists were employed by primary care-based Accountable Care Organizations (ACOs) that were being held responsible for the total cost of care for a defined population of patients, they might be more strongly encouraged to focus on improving care transitions to reduce downstream complications and associated emergency department visits and hospital re-admissions.

Even without ACOs, there has been a great deal of effort over the last few years to improve transitions of care for patients discharged from acute care hospitals.  Most of these efforts attempt to improve both the “pitch” by the hospital-based team and the “catch” by ambulatory care providers.  But, some efforts, such as the BOOST program funded by the Hartford Foundation, have a primary emphasis on the pitch.  Other efforts, such as the STAAR program of the Commonwealth Fund and the Institute for Healthcare Improvement (IHI), tend to emphasize the catch. Hopefully such programs will lead to wide-spread improvements in quality of care and reductions in total cost of hospital episodes.  ACOs could catalyze and accelerate those improvements by linking hospital care more tightly to primary care and supporting this linkage through investments in health information exchange (HIE) capabilities designed to foster thoughtful, smooth transitions of care.

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Why “case load” is not a good metric for case management productivity or intensity

As shown in the following graphic recently published by the Healthcare Intelligence Network, it has become common practice to use “case load” as a metric for the productivity of nurses in case management programs or as a measure of the intensity of a case management intervention.  More broadly, case load has been used for these purposes across many wellness and care management interventions, including chronic disease management, high risk care coordination, wellness coaching, care transition coordination, and other types of programs involving nurses, physician assistants, nutritionists, social workers, and physicians.

HIN Study Results - Case Manager Monthly Case Load

If everyone is doing it, it must be right.  Right?

In my opinion, case load is almost always the wrong measure to use to assess productivity or intervention intensity.   The graphic above indicates that 38% of case management programs have a case load between 50 and 99. Let’s say that one of those case management programs told you that they had a case load of 52.   That literally means that the average nurse in that case management program has a list of 52 patients (on average) that are somewhere between their enrollment date and their discharge date for a the program.  That could mean 52 new patients every day for an intervention consisting of a single 5 minute telephone call.  Or, it could mean 1 new patient each week for a year-long intervention involving an extensive up-front assessment and twice-monthly hour-long coaching calls.   Or, it could mean 1 new patient each week for a year-long intervention consisting of a 20 minute enrollment call and a 20 minute check-up call one year later.  In that context, if I tell you that my case management program has an average case load of 52, how much do you really know?

Some case management programs try to fix this problem by creating an “acuity-adjusted” or “case-mix-adjusted” measure of case load.  In such a scheme, easier cases are counted as a fraction of a case, and more demanding cases are counted as more than one case.  Such an approach requires some type of a point system to rate the difficulty of the case.  Some case management vendors charge a fee for each “case day,” with higher fees associated with “complex” cases, and lower fees for “non-complex” cases.  You can imagine how the financial incentives associated with the case definition can affect the assessment, and how reluctant the case management vendor would be to discharge cases, cutting off the most profitable days in the tail-end of a case when the work is light.

But these “adjustments” are missing the fundamental point that case load is a “stock” measure, while the thing you are trying to measure is really a “flow.”  A stock is something that you count at a point in time, like the balance of your checking account or the number of gallons of gas left in your tank.  A flow is something that you count over a period of time, like your monthly expenses or the number of gallons per hour flowing through a hose to fill up your swimming pool.  The work output delivered by a case management nurse is something that can only be understood over a period of time.

In my experience, the best approach to measuring case management productivity and intensity is to follow “cohorts” of patients who became engaged in the intervention during a particular period of time to track how many minutes of work are done for each period of time relative to the engagement month, as shown below.

 

This graph shows that, during the calendar month in which a patient became engaged, an average of 50 minutes of nursing effort was required.   For individual patients, the nursing effort could, of course, be higher or lower.  Some patients require more time to assess.  Some patients may have become engaged at the end of the calendar month.  Some patient may drop out of the program after the first encounter.  But, on the basis of a cohort of engaged patients, the average was 50 minutes.  In subsequent months, the average minutes of nursing effort typically decreases, after initial enrollment, assessment and care planning effort dies down.  Over time, depending on the intended design of the intervention, the effort falls off as most patients have either been discharged by the nurse, have chosen to drop out, or have been lost to follow up.  This graph is a description of the intensity of the case management intervention.  In this example graph, the cumulative nursing time over the first 16 months relative to the month of engagement is 234 minutes, which could serve as a summary measure of intervention intensity.  If nursing minutes data are not available, some “work-driver” statistics could be substituted, such as the number of face-to-face or telephonic encounters of various types.  These could be converted to minutes based on measured or estimated average minutes of effort for each of the statistical units.

This type of graph can be created for different engagement months and compared over time to determine if the intervention intensity is changing over time.  It can be created for one nurse and compared to all other nurses to determine if the intervention process delivered by that nurse appears to be similar or different than other nurses.

Then, to assess productivity, the total number of nursing minutes can be measured, and compared to the expected number of minutes for cases of the same type and the same mix of “month numbers” in the case intensity timeline.

The implications of this for Accountable Care Organizations is that information systems to support wellness and care management should be designed to explicitly capture the engagement date and the intended type of wellness or care management intervention in which the patient is becoming engaged. Such systems should also capture the discharge dates, statistics about the quantity and types of wellness or care management services delivered to engaged patients, and preferably the number of minutes of effort required for each of those services.

Some people may object to this approach because it implies that the patient is being subjected to a “cook-book” intervention that fails to take into account the uniqueness of each patient.  And, they argue that they cannot specify up front the type of wellness or care management program in which the patient is becoming engaged, because they have not yet completed an assessment.  But, I would argue that nothing in this approach assumes that each patient is treated the same.  This approach merely looks at a population of patients receiving a particular intended type of intervention program.  Although each patient may follow a different path and receive a different mix and quantity of case management services, the overall mix and timing of services for a population of patients can be assessed.  If this mix and timing is not generally constant over time, then you are dealing with a process that is not in control, a problem that must be solved before meaningful program evaluation of any type can be done.

 

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The Smoking Intervention Program, a Provider-based Care Management Process

Smoking cessation is an important public health concern, and has been the subject of a recent Agency for Health Care Policy and Research (AHCPR) guideline, as well as a HEDIS measure.   A point prevalence study conducted with the Henry Ford Health System found a 27.4% prevalence of smoking, and an additional 38.6% former smokers.

The CCE developed a first-generation smoking-dependency clinic which was staffed by trained non-physician counselors and overseen by a physician medical director. The original intervention was a 50-minute initial evaluation and counseling visit, with nicotine replacement therapy prescribed for all patients with a high level of nicotine dependency. This intervention was subsequently updated to reflect the AHCPR recommendation that, unless contraindicated, all smoking cessation patients be prescribed nicotine replacement therapy.

Because relapse is a normal part of smoking cessation, the intervention was explicitly designed to address relapse. This was done through return visits, an optional support group, and follow-up telephone counseling calls throughout the year, as illustrated in the following figure.

The program was designed to be inexpensive and simple to execute within the clinic. This was accomplished by automating the logistics of both the intervention and the collection of outcomes measures. The Flexi-Scan System, an internally developed computer application which helps automate outcome studies and disease-management interventions was used to automate (1) data entry through a scanner, (2) prompting of follow-up calls and mailings, and (3) the generation of medical-record notes and letters to the referring physicians. A database that can be used for outcomes-data analyses is acquired as a part of this process.

As illustrated on the figure below, this first-generation program achieved a twelve-month quit rate of 25%. Such a quit rate is about twice as high as the rate achieved with brief counseling intervention.

To evaluate the cost-effectiveness of this program, a decision analytic model was constructed. This model was constructed using the Markov method.  Key assumptions of the model include the following:

  • One year quit rate for usual care (optimistically assumed to consist of brief physician advice) was 12.5%.
  • Spontaneous quit rate of 1% per year in “out years.”
  • Relapse rate for recent quitters of 10%.
  • Age, Sex distribution based on Smoking Clinic patient demographics
  • Life expectancy of smokers and former smokers by age and sex based on literature (life tables).
  • Cost of clinic intervention – $199
  • Cost of nicotine therapy Smoking Clinic – $101 (Assuming 0.9 Rx/Patient)
  • Usual Care – $33 (Assuming 0.3 Rx/Patient)
  • Future health care costs were not considered
  • Annual discount rate of 5%

The results of this model were presented at the annual meeting of the Society for Medical Decision-Making.  The model results are presented in the form a table called a “balance sheet” (a term coined by David Eddy, MD, PhD).  As shown below, the model estimated that the first-generation smoking-dependency clinic cost about $1,600 for each life year gained.

To help evaluate whether this cost-effectiveness ratio is favorable, a league table was constructed (see below).  The league table shows comparable cost-effectiveness ratios for other health care interventions.  Interpretation of the table suggests that the smoking cessation intervention is highly favorable to these other health care interventions.

League Table

Intervention Cost per Quality-adjusted Life Year Gained
Smoking Cessation Counselling $6,400
Surgery for Left Main Coronary Artery Disease for a 55-year old man $7,000
Flexible Sigmoidoscopy (every 3 years) $25,000
Renal Dialysis (annual cost) $37,000
Screening for HIV (at a prevalence of 5/1,000) $39,000
Pap Smear (every year) $40,000
Surgery for 3-vessel Coronary Artery Disease for a 55 year-old man $95,000

Although this first generation program was effective and cost-effective, it was targeted only at the estimated 16,500 smokers in the HFMG patient population who were highly motivated to quit.

The estimated 66,000 other smokers in the HFMG patient population would be unlikely to pursue an intervention that involved visiting a smoking dependency clinic. Even for the smokers who were highly motivated to quit, the smoking cessation clinic had the capacity to provide counseling to about 500 people each year, or about 3% of these highly motivated smokers.

Second Generation Smoking Intervention Program

In response to this problem, the CCE developed a “second generation” Smoking Intervention Program.” This program uses a three tiered approach which includes (1) a “front-end” process for primary care and specialty clinics to use to identify smokers and provide brief motivational advice, (2) a centralized telephone-based triage process to conduct assessment and make arrangements for appropriate intervention, and (3) a stepped-care treatment tier.

In the “front-end” process, clinic physician and support staff were trained to screen their patients from smoking status and readiness to quit and provide tailored brief advise. Each participating clinic was provided with a program “kit” including screening forms, patient brochures, and posters to assist them in implementing the program. Patients who are interested in further intervention are referred to a centralized triage counselor for further assessment and intervention. These counselors are trained, non-physician care providers. They proactively call each patient referred, conduct an assessment of the patients smoking and quitting history and triage into a stepped-care intervention program.

An important part of this intervention has been providing information to clinicians, including a quarterly report showing the number of patients they have referred to the Smoking Intervention Program, the status of those patients, the type of intervention they are receiving, and the number of patients who report not having smoked in the preceding six months.

The clinician-specific data is presented in comparison to data for the medical group as a whole. These reports have a strong motivational effect on clinicians, as evidenced by a sharp increase in Smoking Intervention Program referrals after each reporting cycle.

As shown above, the second generation program achieved a six month quit rate of about 25%. This rate is virtually identical to the first generation program.  The new program, however, has much larger capacity and lower cost per participant. Patient satisfaction with the Smoking Intervention Program is encouraging, with 85% reporting that they would refer a friend to this program.

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Implementation of Best Practices

The implementation of best practices to improve the quality and efficiency of health care involves making changes to both decision-making and care-delivery processes. The care management methods for improving these processes are largely borrowed from the work of Deming, Crosby, Juran, Ishikawa, and other practitioners of industrial process improvement. These methods are alternatively described as Continuous Process Improvement (CQI) and Total Quality Management (TQM). Berwick, James, and others described the adaptation of these methods for health care.

On a practical level, clinicians apply these methods by working with other members of their clinical team to make local improvements, or, in larger health care organizations, by participating in multi-disciplinary teams organized at an organizational level. These teams often require expert consultation in analytical and statistical methods. Statistical process control involves the application of quality engineering concepts to administrative, support and care delivery processes in order to (1) detect changes in process performance over time, (2) identify assignable causes of variation, and (3) adjust relevant process input variables so as to maintain a process performance criterion within a desirable range.

Another implementation approach is staff training or continuing medical education. This involves the development and delivery of classes and organized curricula, with the objective to improve knowledge and skills to increase the effectiveness of clinicians, administrative staff, and support staff. Some training is intended to increase knowledge and skills in process improvement methods, to improve the effectiveness of CQI or TQM teams. Other training is intended to have a more direct effect to improve decision-making and care delivery processes by teaching clinicians about specific diagnosis and treatment strategies. Traditionally, this latter approach has been accomplished through grand rounds and seminars. Another approach, based on methods developed for the marketing of pharmaceuticals and devices, is academic detailing. Academic detailing is a form of educational outreach involving the the personal delivery to clinicians of brief educational messages designed to change clinical practice behaviors.

Another approach to implementation involves patient education. Therefore, the methods of patient education materials development represent an important domain of care management. These methods involve the development and pilot testing of brochures, pamphlets, audio and video tapes, class materials, web-based materials, and other products designed to provide useful and timely information to patients regarding their health. An understanding and appreciation of the principles of adult learning are essential to this process.

Finally, clinical policy implementation requires the effective application of information technology. Medical informatics is the term used to describe the broader field of information technology applications to health and medical care. Managing intranet resources is an emerging methodological domain of care management, involving the development of internal web-based materials, such as a best practices library or a care management support system. Another important medical informatics method involves the development of computer-based decision aids such as reminders, alerts, and prompts. These are incorporated into the electronic health record as well as associated structured data capture into the clinician workstation and other information systems used by care providers as part of routine practice.

A review of rigorous evaluations of clinical policy implementation methods conducted by Grimshaw, et al. revealed the importance of the methods of guideline development, dissemination, and implementation in predicting clinician behavior change (see table below).

Effectiveness of Guideline Development, Dissemination, and Implementation Methods in Terms of Clinician Behavior Change (adapted from Grimshaw, et al)

Most Effective Moderately Effective Least Effective
Guideline Development Internal development External, local development National development
Guideline Dissemination Specific educational interventions Continuing education

Targeted mailing

Publication in Journals
Guideline Implementation Patient-specific reminders at the time of the clinical encounter Feedback measures General reminders

In general, multi-faceted implementation approaches have been found to be most effective for improvement processes that involve physician behavior change.

These clinical policy implementation methods are used to effect change in clinical processes. However, many care management initiatives, including those described in this web site, involve the establishment and ongoing management of a new set of resources to support improved clinical processes, including the following:

  • Call centers, which receive customer calls and either provides customer service or directs the customer to appropriate staff or resources. Such services may include on-call nurse advice, appointment scheduling, lab result reporting, directions to facilities, billing inquiries, etc.
  • Telephone survey and counseling staff make outgoing calls to patients for the purpose of acquiring information from the patient, such as for a health risk appraisal, survey or follow-up call. They also offer information or services to the patient, such as counseling, needs assessment, and patient education.
  • Distribution of care management materials, including the management of a mail room and stock room to efficiently route care management materials to patients, including patient educational materials and self care supplies.
  • Case managers, typically nurses or other allied health professionals, are required to track patients with defined conditions, assess patient needs, solve problems and deliver other patient services such as counseling, patient education, and social services.

In large health care organizations and health plans, these resources may be developed internally.  In small and medium size organizations, these services can be outsourced to a growing number of external service suppliers.

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