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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Improving Total Hip Replacement Surgery

Background

Joint replacement is a costly and frequently performed inpatient procedure.  In 1995, Henry Ford Hospital carried out 280 total hip replacements and 225 total knee replacements.  To decrease unnecessary inpatient utilization and enhance functional outcomes of joint replacement surgery, Henry Ford Hospital established a multi-disciplinary improvement team, including representatives from Orthopedic Surgery, Physical Therapy, Home Health Care,  Social Work, Utilization Management, the Center for Clinical Effectiveness, and Marketing.

Methods

The team developed and implemented a best practice guideline in the form of a “care-map”, describing the default plan of care for delivering multi-disciplinary services to the patient for each day of the planned length of stay.   These interventions included outcomes assessments, tests & diagnostics, consults, treatments, procedures, medications, diet & nutrition, elimination goals, activity goals, and safety goals, skin condition goals, educational interventions and learning goals, and discharge planning.

To support the evaluation of the work of the team, comparative data was used.   These data included comparative outcomes data obtained through a collaborative Outcomes Measurement Consortium organized through the American Medical Group Association (AMGA).  Comparative process data was obtained through the Group Practice Improvement Network (GPIN).  In addition, a periodic audit process was used to measure variance from the care-map.  Additional process and outcomes data were collected and managed using software applications developed by the Center for Clinical Effectiveness:

  • Complications Tracking System used to enter and report on trends in various joint-replacement complications. This system was used to support the Department of Orthopedic Surgery morbidity and mortality conferences.
  • Outcomes data acquisition was accomplished using the “Flexi-Scan” forms scanning and study management software.
  • Cross-institutional pooling of outcomes data data pooling and quarterly analysis was accomplished by staff of the American Group Practice Association usuing the Flexi-Scan analytic dataset builder and other tools developed by the Center for Clinical Effectiveness.

Finally, patient satisfaction and subjective feedback data was obtained using patient focus groups arranged by Center for Clinical Effectiveness and staff from the Marketing Department.

Results

The implementation of the care-map led to a further one-day reduction of the length-of-stay, as illustrated in the following figure.

Functional outcomes data revealed that hip replacement surgery led to rapid reduction in bodily pain to normal age and sex-adjusted levels. (Note that in the following graph, pain is expressed on the SF-36 pain scale, in which higher numbers represent better functional status, or less pain).

As shown in the following figure, physical function is also improved, although not as rapidly nor as dramatically as bodily pain.

The following figure shows the distribution in bodily pain and physical function, showing that although the average improvement is favorable, 15% of patients have worse pain one year after surgery, and 22% have worse physical function after surgery.

An analysis was conducted to identify baseline variables (collected prior to the surgery) which could predict failure to acheive improvement in pain and function.   As shown in the following table, mild pre-operative pain was among the strongest predictors of failure to achieve an improvement in pain.

In order to optimize the ability to predict which patients would fail to achieve a pain reduction from hip replacement, a neural network was trained based on 13 baseline variables collected from the patient before surgery.  The neural network was then used to calculate a predictive score for each of the patients.  The frequency distribution of predictive scores for patients that did achieve a pain benefit, as well as the distribution for those that did not experience a pain reduction are shown in the following figure.

If the neural network was perfectly predictive, these two distributions would not overlap at all.  A threshold predictive score was selected such that the model could be said to be 85% sure about making a prediction that a given patient would not achieve a pain reduction from surgery.  Of the 185 patients that experieced an improvement, only 2 had a neural network predictive score below 0.3.  Of the 36 patients that experienced an improvement, 13 of them had scores under 0.3.  So, for the 15 patients with a score below 0.3, 85% failed to experience a pain reduction.   The sensitivity of the model in predicting this type of treatment failure was 35%.   In other words, of 100 patients considering hip replacement therapy, about 15 of these will fail to achieve a pain reduction.  Of the 15, about 5 of these patients can be identified in advance using this neural network and using a cut-off threshold of 0.3.  If implemented in clinical practice, this model could lead to the elimination of the treatment cost and risk of complications associated with these 5 patients.  On the down side, approximately one patient who would have benefited from the surgery would have been incorrectly told that they were unlikely to benefit.  Such a patient would unnecessarily suffer pain that could have been relieved by the surgery.

As shown in the following figure, analysis also revealed that patients had unrealistic expectations of the time it would take for them to feel fully recovered after the surgery.

Finally, the data revealed that, compared to other institutions, Henry Ford experienced an unusually long physical function recovery period among patient receiving revision total hip replacement (operations on patients that had previously received hip replacement surgery).  Such data, shown in the following figure, led to a re-evaluation of Henry Ford’s approach to post-operative rehabilitation.

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