To estimate incidence rates and quantify excess medical and productivity cost of community-acquired pneumonia (CAP) in a commercially insured, working-age population.

 Using the Thomson Reuters MarketScan Commercial Claims and Encounters Database, we estimated the annual incidence of CAP from 2003 through 2007 and evaluated its excess direct medical and productivity costs due to short-term disability and workplace absenteeism for adult patients aged 18 to 64 years. A cohort of CAP patients was 1:3 propensity score matched to a control cohort without pneumonia. Both excess direct medical costs and excess productivity costs were estimated in multivariate ordinary least squares (OLS) and generalized linear model (GLM) adjusting for demographic and clinical characteristics.

 A total of 402,831 patients with CAP and 1,208,231 matched controls were included. Overall annual CAP incidence was 4.89 cases per 1000 person-years. Excess annual medical cost of CAP ranged from $7220 by OLS to $11,443 by GLM. Inpatient costs, outpatient costs, and pharmacy costs explained 34%, 51%, and 15% of this excess cost of the GLM models, respectively. CAP patients had an additional productivity cost of $2391. Among adults younger than 65 years, these estimates imply an incidence of CAP of 950,000 annual cases at a cost of $10.6 billion (range: $9.04-$13.1), of which 80% are direct medical costs and 20% are productivity costs.

 CAP is a frequent and costly event in a working-age population with a national cost of $10.6 billion. Interventions that could successfully prevent CAP could have a significant impact on healthcare costs and productivity.

This study estimated the burden and incidence of community-acquired pneumonia (CAP) among non-elderly adults (younger than 65 years) in the United States and determined the following:

		 CAP is common among non-elderly adults (4.89 cases per 1000 person-years). There are approximately 950,000 cases of CAP among non-elderly adults annually.

		 CAP costs approximately $10.6 billion annually.

		 CAP has high excess medical costs, both per case (range: $7220-$11,443) and projected nationally ($6.8-$10.8 billion).

		 Approximately 20% of the burden of CAP comes from indirect costs. Preventing CAP could have a substantial impact on healthcare costs and productivity.

Pneumonia ranks among the top causes of death and hospitalization and is a major driver of healthcare costs. Most cases are classified as community-acquired: pneumonia occurring in a patient who is not hospitalized or residing in a long-term facility. In the United States, 55,000 deaths were attributed to pneumonia in 2006, with 1.2 million hospital discharges.

 While the burden of this disease is highest among the elderly, adults younger than 65 years accounted for 6000 deaths and 325,000 hospitalizations in 2006.

Despite the obvious burden of this disease on the healthcare system, surprisingly few studies have attempted to estimate the cost burden. Available estimates come from the mid-1990s where the total cost burden of treating community-acquired pneumonia (CAP) was estimated at $12.2 billion,

excluding productivity costs. Productivity costs were estimated for a Fortune 100 company where the annual total employer costs for pneumonia patients were $14,837, with nearly 30% attributed to work absence and disability (disability claims were assigned the cost of 5 workdays in addition to imputed illness absence days).

 This current analysis addressees this limitation by evaluating the total cost burden of CAP among non-elderly adults including both direct medical costs and productivity costs in a large cohort of commercially insured employees and dependents, geographically dispersed throughout the United States.

This study used the Thomson Reuters MarketScan Commercial Claims (Commercial) and Encounters database to determine the incidence and medical costs of CAP. The Commercial database contains adjudicated medical and outpatient prescription drug claims of several million employees and their dependents (annually), covered under a variety of health plan structures. In 2008, there were over 34 million covered lives in the Commercial database. Productivity costs were assessed on a subset of patients with data available in the Thomson Reuters Health Productivity and Management (HPM) database. The HPM database contains employeelevel workplace absenteeism and short-term disability data for a subset of approximately 1.4 million patients in the Commercial database.

The Commercial and HPM databases have been de-identified and are in compliance with the Health Insurance Portability and Accountability Act (HIPAA) regulations; this analysis was exempt from Institutional Review Board approval.

We selected adults aged 18 to 64 years in the Commercial database between July 1, 2002, and December 31, 2007. We identified all claims for pneumonia based on the 

International Classification of Diseases, Ninth Revision, Clinical Modification

 diagnosis codes (codes: 480.xx-486.xx). The index date for any patient with pneumonia was the date of the first diagnosis of pneumonia following 6 months of continuous medical and prescription coverage. This means that while pre-index periods could go back into 2002, index dates had to occur between January 1, 2003, and December 31, 2007. Patients with a diagnosis of pneumonia in the 6-month pre-index period were excluded. There was no minimum requirement for post-index continuous medical and prescription coverage; patients were followed up to 12 months, depending on data availability.

Pneumonia was categorized as community-acquired if the patient had either a principal or non-principal diagnosis of pneumonia on an outpatient claim or a principal diagnosis of pneumonia on an inpatient claim with no evidence of an inpatient or long-term care stay in the previous 30 days. (For inpatient admissions, principal diagnosis is the condition established after study to be chiefly responsible for occasioning the admission of the patient to the hospital or for care, which may or may not be the admitting diagnosis for the inpatient admission. In the case of outpatient services, principal [or primary] diagnosis is the main condition treated or investigated during the relevant episode of outpatient [ambulatory] healthcare.) The remaining patients with a diagnosis of pneumonia were assumed to have healthcare-associated pneumonia or hospital-acquired pneumonia (HAP) and were excluded from this analysis. Patients in the database without a diagnosis of pneumonia were included in incidence calculations and as controls in cost analyses. Control patients were randomly assigned an index date between January 1, 2003, and December 31, 2007, and were also required to have 6 months of continuous medical and prescription coverage preceding the index date.

This study employed the most recent HPM data available, which covered January 1, 2003, through December 31, 2006. Patients could contribute work absence, short-term disability, or both forms of data; work absence and short-term disability data were analyzed separately to maximize the sample sizes. While the direct costs analysis includes data on both employees and dependents, indirect cost data is only available for employees.

Healthcare expenditures were derived from all inpatient medical, outpatient medical, and outpatient pharmacy claims data captured in the Commercial database during the study period. (Emergency department [ED] visits that did not lead to an inpatient stay were classified as outpatient medical visits. ED visits were classifed as inpatient medical if they led to an inpatient stay.) Medical expenditures were defi ned as total gross payments to a provider, including deductibles, copayments, and coordination of benefits. Pharmaceutical expenditures were the actual reimbursement to the pharmacy, including the drug’s administrative dispensing fee, ingredient cost, patient portion of costs, and sales tax. Expenditures for services provided under capitation arrangements were estimated using payment proxy based on procedure code and region of care by the data provider. All expenditures were inflated to 2008 dollars by multiplying each year’s cost by the US Bureau of Labor Statistics Medical Care Specific Consumer Price Index.

Productivity costs were quantifi ed using the number of employer-recorded days of absence from work and short-term disability days during the post-index period. A wage constant of $41 per hour was used in this analysis. This was based on a previous benchmarking study of health- and productivityrelated costs for 32 companies across the United States and adjusted to 2008 US dollars.

 It was assumed a patient would only receive 70% of wages while on disability, thus a $28.70/hr wage constant was used to valuate short-term disability claims.

Patient demographic characteristics were measured on the index date and included age, gender, US Bureau of Census region of residence, health plan type, insurance plan capitation, population density status (urban vs rural), industry type, employee classification (hourly versus salary), and union classification (union vs non-union). Clinical variables calculated during the baseline period included the Deyo Charlson Comorbidity Index (CCI); comorbidities of chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), cancer, cardiovascular disease (CVD), other musculoskeletal surgery, diabetes, and asthma; pre-period use of antibiotics, long-acting beta-agonists, short-acting beta-agonists, and systemic corticosteroids; indicator of whether there was a hospitalization in the 1 month prior to index date; 6-month pre-period costs for inpatient, outpatient, and pharmacy; and time observed for follow-up.

The incidence of CAP for the 5-year study period was calculated based on all the eligible benefi ciaries in the Commercial Database between January 1, 2003, and December 31, 2007, that had at least 6 months of uninterrupted enrollment preceding the index date for pneumonia or controls. The numerator was the number of cases during the study period. The denominator was the total enrollment time during the study period in the Commercial Database among potentially eligible patients. Incidence rates were reported as CAP cases per 1000 person-years and stratifi ed by gender and age categories.

In order to estimate the direct medical cost and productivity burden of pneumonia, we generated a control group from all beneficiaries without a pneumonia diagnosis using nearest neighbor propensity score matching without replacement in a 3:1 control:case matching ratio. The propensity score was estimated by using a logistic regression modeling the presence of pneumonia as the dependent variable with the following covariates: index year; quarter; age; gender; geographic region; plan capitation status; industry; length of follow-up; baseline Deyo CCI; and baseline comorbid conditions, including asthma, cancer, CVD, CHF, COPD, diabetes, and HIV. Propensity score matching was performed and reported separately among the subset of patients (and controls) with work absence and short-term disability data. Despite the propensity score match, imbalances in risk will remain, and with the highly skewed distribution of costs, these imbalances may have an important infl uence on the estimate of excess costs. Therefore, in addition to the excess costs estimated from the observed differences between the pneumonia and non-pneumonia matched groups, we estimated excess costs in multivariate analyses by ordinary least squares (OLS) and generalized linear model (GLM). Separate models were run for total inpatient cost, ambulatory care excluding pharmacy cost, and pharmacy cost due to the distinct cost distribution for each of these categories. These predicted costs were then summed in order to assess total costs. Similarly, we used separate models to analyze absenteeism and short-term disability. Two-part models were used for inpatient costs, absenteeism, and short-term disability.

For GLM, the log link and gamma variance functions were used. The control variables for all multivariate models included: age; gender; insurance type; region; urbanicity; employee class (union vs non-union); employment status; employee relationship; Deyo CCI score in pre-period; COPD; CHF; cancer; CVD; other musculoskeletal surgery; diabetes; asthma; pre-period use of antibiotics, long-acting beta-agonists, short-acting beta-agonists, systemic steroids; indicator of whether there was a hospitalization in the 1 month prior to index date; total inpatient costs in pre-period; total outpatient costs in pre-period; total pharmacy costs in pre-period; and time observed for follow-up. All cost estimates are in 2008 US dollars.

To quantify the total annual cost of CAP that occurs in the US population aged 18 to 64 years, we multiplied our estimate of cost of an episode of CAP by our estimate of the incidence of CAP and by the number of people in the United States in this age group. We quantify total annual costs of CAP with and without productivity costs. We express our range of methodological uncertainly by producing estimates for each of our 3 methods of estimating medical and productivity costs. We quantify uncertainty to stochastic variation in our estimates through Monte Carlo simulation based on the means and standard errors of inputs with distributional assumptions. Because each input could be skewed to the right and had to be non-negative, we fi tted lognormal distributions to each major input and ran a simulation with 20,000 iterations. The simulation was done in Crystal Ball, Fusion Edition, version 11.1, and Excel 2007 software packages.

 contains a summary of the incidence of CAP among patients in the MarketScan Commercial Database, in total and stratified by age. Annualized overall incidence for this population was 4.89 cases per 1000 person-years. This incidence calculation was based on the 402,831 cases of CAP identified from 2003 to 2007 in our sample of 36.25 million commercially insured adults aged 18 to 64 years, with a total enrollment time of about 83 million years of risk. We project this annualized rate to the current US adult non-elderly population to arrive at an estimate of 947,000 cases of CAP in 2010 among adults aged 18 to 64 years. This analysis suggests increasing incidence with age; incidence among patients aged 55 to 64 years was 3.3 times the incidence of the group aged 18 to 34 years.

Demographics and Baseline Clinical Characteristics of CAP Sample

Articles

CLINICAL

Approximately 5% of non-elderly adults have a community acquired pneumonia (CAP) annually, with an annual total of $10.6 billion in direct and indirect costs.

Incidence and Cost of CAP in a Large Working-Age Population

 Determine the viable yield of screening electronic Veterans Health Administration (VHA) records to identify patients who stop taking a long-term medication for reasons that might be addressed by healthcare providers.

 Prospectively screened cohort with mailed follow-up of positive screens.

 Electronic healthcare records were screened to identify patients receiving care in a Veterans Administration (VA) Health Care System who became past due for resupply of medication (statin) prescribed to reduce cholesterol and risks of adverse cardiovascular events. Subsequently, administrative data were used to classify and characterize patients as true or false positive screens. A follow-up survey mailed to the fi rst 1000 positive screens asked them if they were still taking a statin provided by the VHA, and if not, why?

From February to July 2010, 1000 (4.6%) of the statin-recipient cohort of 21,935 became past due for a resupply. Subsequently 824 (3.8%; 95% confi dence interval [CI] 3.5%-4.0%) were classified as true positives (positive predictive value 82%; 95% CI 80%-85%), and 176 (0.8%; 95% CI 0.7%-0.9%) as false positives. However, the 824 true positives included 95 deceased, 17 long-term care residents, 302 who reported good reasons for no longer getting the statin, and 208 who eventually got another supply. The overall yield of good candidates for efforts to reinstate long-term use of statins was only 20%.

The viable yield from electronically screening VA healthcare records to fi nd patients who stopped taking statins was low. More complete records and sophisticated screening programs are needed to improve the yield.

		 This attempt to screen electronic healthcare records to identify patients who appeared to stop taking a long-term medication identifi ed a large number of patients, but a low yield of viable candidates for efforts to restore use of the medication.

		 More sophisticated screening algorithms and complete healthcare records are needed to improve the actionable yield.

		 Each patient’s reasons for stopping a medication need to be ascertained to guide remedial actions.

Studies have consistently observed that many patients stop taking prescribed medications, thereby reducing the effectiveness of care for chronic conditions.

Investigators have associated numerous factors with discontinuation of medications; however, there are no prediction models that can accurately forecast when an individual will stop taking a prescribed medication or why.

 Integrated healthcare systems such as the Veterans Health Administration (VHA) may be able to use their electronic healthcare records to screen enrolled populations and identify patients as they appear to discontinue a medication.

 However, mass screening programs may compel healthcare systems to follow up a large number of positive screens. Thus, false positives and identifi cation of individuals that most likely would not be amenable to restoration of medication use must be minimized.

We developed and evaluated a screening program for VHA electronic health records. The primary objective was to identify patients as they stopped taking statin medications prescribed by Veterans Administration (VA) healthcare providers to reduce their cholesterol, hence the risk of adverse cardiovascular events including death. Specifi c aims were to estimate the proportions of true and false positive screens and the positive predictive value. A follow-up survey was mailed to positive screens to ascertain the reasons they stopped getting their statin prescriptions from the VHA and to help determine the percentage of positive screens that might be good candidates for remedial intervention.

The screening program was developed and tested in a VA healthcare system. Similar to other VA healthcare systems, the Veterans Health Information Systems and Technology Architecture (VistA) hierarchical files contain the electronic health records for a large hospital with outpatient clinics and several regional community-based outpatient clinics. We focused on continuing use of statins because they are commonly prescribed for long-term use, and lack of persistent use has been well documented in several healthcare systems.

 This evaluation and consent process was reviewed and approved by the local Committee for Human Subjects Research.

Files of VHA prescriptions (new, refill, partial fill) in VistA were screened biweekly beginning in February 2010 with 9 months of prior prescription records. Eligible individuals had received at least 2 separate 30- day supplies of a statin during the screening period to exclude dose adjustments and the impact of side effects that occur early in the course of treatment. A “past due date” was calculated as the date of release of the last statin supply plus the days of supply dispensed and any carryover from previous supplies in the fi le of statin prescriptions accumulated by the screening program. In addition, the calculated past due date was postponed by any inpatient days in the patient treatment file and a 120-day grace period. This grace period was based on past studies of medication persistence,

 and a separate (different data) preliminary examination of how much false positive screening rates increased as the length of the grace period was reduced from 180 to 30 days. Whenever the Minneapolis VistA records indicated a patient was past due for a statin resupply and there was no indication the recipient was deceased, VistA files maintained by other VA healthcare systems were searched to fi nd additional statin supplies, inpatient stays, and deaths. A positive screen was identifi ed whenever a patient’s calculated past-due date occurred before the screening date. Several descriptive variables were extracted from VistA files, including the type of prescriber (physician, other); whether the last supply was a new order or refill, delivered by mail, or on formulary; patient demographics; and address information.

A 3-page survey was mailed to the first 1000 positive screens that had a valid non-institutional postal address in the patient file. An introductory letter was sent, then the survey package, including a cover letter, preaddressed, postage-paid survey return envelope, and a $5 payment. A second survey packet without money was sent if no response was received within about 2 weeks. A third packet was sent via Federal Express or United Parcel Service after another 2 weeks without a response.

The survey instructed the recipient to look at the labels on their prescription containers, and asked whether they still had any of their statin which the VHA had most recently dispensed, and whether they were still taking any of that particular statin. If they were not taking the statin, the survey asked for the reason(s) they stopped it, using a 21-item checklist and an open-ended query. The survey asked everyone if they got their statin supply from any place other than a VHA pharmacy during the past 4 months, whether others (Medicare, Medicaid or state assistance program, employer, union or retirement insurance, military) helped them pay for their medicines, if they had stayed overnight in a non-VHA hospital or traveled away from home for a week or more during the past 4 months, and whether a doctor, nurse, or pharmacist had reduced their statin dose, or if they had done so on their own.

Nine months after the last survey was sent to positive screens, scrambled social security numbers of the 1000 positive screens were used to extract information from national VHA administrative records. The VA Decision Support System (DSS) Outpatient Pharmacy National Data Extract that is extracted from all VistA systems was used to determine whether each positive screen received any more supply of any statin from the VHA before their calculated past due date (a false positive screen) or did not (a true positive screen), and whether they received a VHA statin supply within 6 months after their past due date that would be consistent with a temporary gap in supply.

Baseline variables representing the year prior to the last statin supply dispensed before the patient screened positive were extracted from several sources. Prescription variables from the DSS National Data Extract included the total number of prescription fills, number of statin fills, and whether each subject received a prescription in each of 32 different classes of medications, including those used for atherosclerotic diseases (ischemic heart disease, etc) or risk factors (blood pressure, diabetes, etc) and depression. The VHA Medical SAS data files compiled by Austin Information Technology Center provided national information on VHA outpatient encounters and inpatient stays. All diagnoses recorded during the baseline year were used to identify chronic comorbidities based on an enhanced Elixhauser classification scheme.

 In addition, categories for ischemic heart disease (ICD-9-CM codes 410 through 414), heart failure (398.91, 402.01, 402.11, 402.91, 404.03, 404.11, 404.13, 404.91, 404.93, 425.4, 425.7-9, 428), and dementia (290.1-4, 290.9, 291.1-2, 294.1, 331.0-2, 331.82) were created. Monthly enrollment records were matched to the date of each subject’s last VHA statin supply to determine the duration of VHA enrollment; enrollment priority (1-8), which, in part, determines whether there was a VHA prescription copayment; and other active health insurance including Medicaid. Dates of death were obtained retroactively from monthly enrollment records.

Descriptive statistics, mean ± standard deviation, median (25th-75th quartiles), and proportion (percentage) as appropriate for the level of measurement and distribution of the data are reported. Binomial 95% confi dence intervals are estimated for proportions. We did not collect follow-up data on patients who did not become positive screens, thus the sensitivity, specificity, and negative predictive value could not be estimated. Group characteristics were compared by Student t test for normally distributed continuous variables, Wilcoxon rank-sum test for skewed continuous measures, or Pearson’s 

 values are not adjusted for multiple comparisons. Stata software (version 10.1) was used for all analyses.

Within 5 months of beginning to screen the statin-recipient cohort approximately every 2 weeks, 1000 patients (4.6%; 95% confi dence interval [CI] 4.3%-4.8%) out of 21,935 became past due for a resupply of statin medication, ie, became a positive screen. Their statin prescriptions are characterized in 

. Consistent with the 120-day grace period and the predominant dispensing of a 90- day supply, the positive screens became past due 214 ± 23 days after their last statin supply was sent out. The positive screens received 3.1 ± 1.7 statin supplies and 249 ± 92 days of supply during the baseline year. Thus, the majority appeared to have stopped after receiving more than the required minimum of 2 statin supplies. Subsequently 824 in the statin user cohort of 21,935 (3.8%; 95% CI 3.5%-4.0%) were judged to be true positives based on not getting a resupply of any statin medication from the VHA for at least 4 months after their past supplies should have been consumed. The estimated positive predictive value was 824 out of 1000, or 82% (95% CI 80%-85%). During follow-up we discovered that 11% of the 824 true positives had died (n = 95) before their past-due date, or became nursing home residents (n = 17), where most likely they would not be personally responsible for taking or discontinuing the statin. Later DSS prescription extracts found 176 (0.8%; 95% CI 0.7%-0.9%) had received another supply before their calculated past-due date and therefore were false positives. The statin supplies defi ning false positive screens were all dispensed in February 2010, the first month of screening the prescription records. Characteristics of the false and true positives are compared in 

. There were no major differences in these or any other comorbidities or medication classes that are not shown, or variables listed in Table 1.

The follow-up survey was sent within 18 ± 10 days after the calculated past due date. Excluding the 95 deceased, 

 nursing home residents, and 21 cases with undeliverable addresses, the response rate was 786 out of 867 (91%), including 640 true and 146 false positives. Reasons reported by the true positives for no longer taking the statin provided by the VHA are listed in 

. Of the 640 true positives, 302 reported at least 1 reason that could be diffi cult for healthcare providers to address, including physician orders, side effects, switching to alternative treatments, or using non-VHA sources of supply. Other reasons that were given might be more amenable to efforts to reinstate long-term use of a statin. However, 208 of the remaining true positives that gave reasons that might be more amenable to reinstating use received another statin supply from the VHA within 6 months after becoming past due. Thus, efforts to reinstate their statin would not be required. The 

 summari zes the overall yield of good candidates for efforts by healthcare providers to reinstate long-term statin use.

The majority of patients identifi ed by this particular approach to screening electronic healthcare records were questionable candidates for reinstatement of long-term statin use. The yield could be improved in several ways. Having more up-to-date records of deaths and transitions into long-term institutional care would eliminate some that definitely did not require follow-up. A more advanced screening program that could determine whether VHA providers had discontinued the statin would be very helpful. Most likely this would require a search of textual notes. Searching for side effects such as elevated liver enzymes or a diagnosis of myopathy could exclude those with adverse effects that could have prompted discontinuation of a statin. However, some adverse effects, such as patients not liking how they felt on medication, will not be recorded or may be recorded as textual notes. Likewise, information on alternative treatments might not be readily accessible to an electronic screening program. Searching the healthcare record for cholesterol levels indicating adequate cholesterol control after patients became past due could further reduce the large number of positive screens needing follow-up.

A substantial proportion of the positive screens obtained statin supplies outside of the VHA. Lower out-of-pocket costs provided by insurance coverage or generic supplies provided by discount pharmacies increasingly are reducing the VHA’s ability to rely solely on internal prescription records. Efforts to reconcile medications could alleviate this problem, if reconciliation becomes routine during all patient encounters and the information is readily retrievable.

 Perhaps the reasons patients are no longer taking previously prescribed medications could be ascertained and recorded as well.

Another way to reduce the burden of follow-up of positive screens would be to increase the grace period and thereby exclude patients who eventually obtain another supply of the medication. Thirty-seven percent of the positive screens reported they were still taking the statin they got from the VHA, and 76% of them did get another statin supply within 6 months of their calculated past-due date. Although the follow-up survey may have prompted some to request another supply of statin, the percentage that eventually obtained another statin supply is similar to our preliminary data wherein positive screens did not receive a follow-up survey. Others have noted the dynamic nature of patients’ prescription supplies.

 Other analyses not presented herein suggested that most of the patients that reported they were still taking the statin despite being past due for a refill relied solely on the VHA for this prescription medication and may have accumulated suffi cient supplies to bridge a 4-month gap. Thus, the screening program may have given the wrong impression in these cases. However, accumulation of excess supply could be due to unrecognized self-made or provider-directed dosage reductions. Another study of veterans with hypertension reported nearly 50% disagreement between a 90-day measure of insuffi cient medication supply and a self-reported measure of nonadherence.

 These results along with ours indicate that more information is needed before acting on observed gaps in medication supplies. Conversely, only 22% of those that confi rmed they were no longer taking the VHA statin received another statin supply within 6 months, confi rming the veracity of this type of self-report.

Asking positive screens whether or not they stopped taking a medication provided by the VHA and if they stopped, why, is a reasonable approach to try to reinstate use of a medication.

 The survey respondents gave many reasons for stopping their statin that typically would not be noted in medical records, and therefore would need to be elicited by some sort of follow-up with the patient. Although not responding to a survey about medication beliefs has been associated with worse adherence, the response to our follow-up survey was 91% for both true and false positive screens.

However, the fi rst survey package included a $5 payment and as many as 3 survey packages had to be sent to get a response. This approach may not be practical when there are a large number of positive screens. The effectiveness of a simple refi ll reminder sent to all positive screens may be limited if the reminder does not address the patients’ reasons for stopping the medication, and may enrage those who receive an inappropriate reminder.

This attempt to screen electronic records produced a substantial number of false positives. We were not able to determine why the screening program did not incorporate statin prescriptions that were later found in prescription records extracted from VistA files. None of the comparisons of true versus false positive screens provided much insight into this problem. It is somewhat encouraging that the false positives were limited to prescriptions dispensed during the fi rst month of the screening program when there might have been an unrecognized problem while initiating the screening process. However, if timely transfer of prescription-filling records from the mailorder pharmacy to the local VistA fi les was responsible, false positives could be a recurrent problem.

This attempt to use the VHA electronic health records to monitor continued use of a medication prescribed for a chronic condition is limited to a single class of medications in a single VA Health Care system. However, the electronic health records are used by all VA Health Care systems, and the likelihood of, and reasons for, discontinuing statins are similar to several other long-term medications.

Thus, insights from this evaluation could apply more generally and help guide further development of electronic screening programs. By design, we did not begin to screen patients until they had received at least 2 statin supplies to try to avoid any effects of trial doses, dose titration, early intolerance, and patients that didn’t intend to take the medication that was prescribed. We were most interested in screening patients that at least initially appeared to be taking and tolerating the medication. Beginning to screen patients after they fi ll their fi rst prescription may alter the results reported herein. Not all of the positive screens returned the survey or indicated whether or not they were still taking the statin, making it diffi cult to estimate the overall percentages in the population that stopped statin use for each reason. The viable yield of the screening program could be overestimated for the same reason. Nevertheless, the list of reasons and number of times they were cited provides important insights into longterm statin use and what issues need to be ascertained and addressed by this type of mass screening program. For example, healthcare providers may have to convince some patients that the effort and cost of long-term statin use is worthwhile and make sure to show them how much their cholesterol and risk has been reduced. Other patients need help managing their medications and out-of-pocket costs. Healthcare providers might not be able to resolve some of the reasons patients gave for discontinuing a statin.

In conclusion, the actionable yield of this particular effort to screen the VHA’s electronic healthcare records of patients that were filling prescriptions for a long-term medication was quite low, due to information that was not placed in the electronic records in a timely manner or that would be diffi cult to extract without a much more sophisticated program including text searches. Furthermore, supplemental information is necessary to determine the patients’ undocumented reasons for discontinuing a medication. Mass screening of electronic health records followed by a large number of mailed follow-up surveys may not be an effi cient method for identifying viable

candidates for interventions to reinstate long-term medication use.

 Allocation of limited healthcare resources to this type of mass screening program would have to be justified by the magnitude of improvement in patient outcomes that would depend on improving the viable yield as well as whether healthcare providers could satisfactorily address the variety of reasons patients have for discontinuing a medication.

The excellent work of Andrea Cutting, MA, who compiled the survey data and extracted and processed the data from VHA administrative records, and Hannah Fairman, BS, who managed the follow-up survey, is gratefully acknowledged.

METHODS

Many patients stop taking medications for chronic conditions, thereby reducing the effectiveness of healthcare. An attempt to screen electronic VA healthcare records to identify patients as they discontinued a medication was not an efficient approach to this problem.

Screening Electronic Veterans' Health Records for Medication Discontinuation

Lessons learned from the implementation of a pharmacist-delivered medication therapy management (MTM) intervention in primary care (PC) can inform future MTM studies and be adopted into real-world clinical settings. We sought to describe the variations and challenges of patient recruitment, enrollment, MTM pharmacist visits, and telephone follow-up in a 3-arm randomized trial of MTM interventions conducted at 3 health centers.

Using a post-study structured interview, we interviewed study personnel, clinical pharmacists, and investigators about 5 study domains: recruitment, enrollment visits, MTM pharmacist visits, telephone follow-up, and data collection.

All centers screened clinic schedules and conducted queries of administrative databases to identify eligible participants. Patients were recruited either during existing primary care visits or by mailing letters with telephone follow-up. Patients with many medical problems, with transportation difficulties, or who were unaccompanied by a family member were less likely to enroll. MTM visits scheduled separately from other clinic appointments had higher cancellation or no-show rates. Provider response to pharmacist recommendations was low overall but better when the provider was acquainted with the pharmacist who was making contact.

 Off-site implementation of MTM services results in lower participation by patients and providers. Future MTM studies should consider integrating MTM services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

This paper describes the lessons learned from implementing a pharmacist-delivered medication therapy management (MTM) intervention.

		Patients with many medical problems, transportation difficulties, or who were unaccompanied by a family member were less likely to enroll. MTM visits scheduled separately from other clinic appointments had higher cancellation or no-show rates. Provider response to pharmacist recommendations was better when the provider was acquainted with the pharmacist who was making contact.

		Off-site implementation of MTM services resulted in lower participation by patients and providers. Usual care implementation can be improved by integrating these services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

The Medication Evaluation and Drug Use Problem Identification to Improve Safety in High Risk Medicare Beneficiaries (MEDIS-MB) study was a randomized, multisite trial of different medication therapy management (MTM) strategies conducted at the University of Illinois at Chicago (UIC), the Baylor Health Care System in Dallas, Texas, and the Duke Primary Care Research Consortium (PCRC) in Durham, North Carolina. Patients 65 years or older with 3 or more chronic illnesses, 6 or more medications, and 1 or more risk factors for development of a drug-related problem (DRP) (eg, recent hospitalization or multiple providers) were randomly assigned to 1 of 3 treatment arms: usual care; basic MTM by patient interview only; or enhanced MTM with access to a clinical synopsis of medical history, laboratory data, and medications from the patient’s medical record.1 The overall results showed that MTM reduced DRPs and increased patient satisfaction. Access to the clinical synopsis in the enhanced MTM arm resulted in fewer medication list discrepancies.

 Touchette and colleagues discussed the potential benefits of expanding MTM services to include platforms for clinical record data sharing for community pharmacist access, thus potentially improving patient outcomes by reducing adverse drug events (ADEs).

Communicating the study implementation issues that we experienced can inform clinicians, administrators, researchers, and payers who may be interested in 1) clinical adoption of the intervention, or 2) conducting future MTM studies. In this paper, we intend to describe the variations and challenges of patient recruitment, enrollment, MTM pharmacist visits, and telephone follow-up within this comparative effectiveness trial.

The detailed MEDIS-MB study design has been previously described.

 Institutional review boards at participating health systems approved the study; written informed consent was obtained from all patients. During the enrollment phase, weekly screening and enrollment reports, including reasons for ineligibility and patient refusal, were sent from Duke and Baylor to the UIC coordinating center and reported to the study sponsor. At the end of study, the investigators developed a questionnaire of 5 study domains—recruitment, enrollment visits, MTM pharmacist visits, telephone follow-up, and data collection issues&mdash;to understand the challenges of study implementation and gather feedback on how to improve future studies.

Under the recruitment domain we asked questions about the recruitment method (mail, phone, or in person), time spent on recruitment, previous MTM program enrollment, differences in patient participation, whether incentives were adequate, consent form problems, and how the various study team members approached the clinics and providers. Under the enrollment visit domain we asked questions about the length of visit and space available. Under the MTM pharmacist visit domain we asked about scheduling the visit and the number of visits needed/required, work flow issues, contacting or faxing the provider with recommendations, access to pill bottles, and completing the clinical synopsis, medication lists, and drug-related problem surveys. Under the telephone follow-up domain we asked about the length of the phone call and issues with filling out the symptom survey scale, resource utilization survey, and pharmacist satisfaction form. Finally, under the data collection domain, we asked questions about the methods of data collection (ie, filling out, making copies of, and mailing paper forms to the coordinating center), monthly study calls, and overall clinic participation. An initial conference call with the study personnel, clinical pharmacists, and investigators was held to gather initial responses to the questionnaire items; a second call confirmed their responses and gathered additional feedback. Personnel who were unable to attend the calls submitted written responses to the questionnaire.

The MEDIS-MB study was conducted at 3 institutions to reach the enrollment goal of 600 participants (approximately 200 participants per site). UIC worked with 1 family medicine (FM) and 1 internal medicine (IM) clinic, Baylor enrolled patients from 2 senior health centers, and Duke recruited participants from 6 primary care clinics (1 FM, 5 IM) within its practice-based research network (PBRN). Enrolling at 3 institutions resulted in ethnic diversity among participants (51% black, 48% white, 1% Asian/American Indian).

 outlines the number of patients that were screened, contacted, and enrolled. Of the 3084 patients who were screened, we enrolled 637 (21%) participants. Patients were most often deemed ineligible for participation because they did not meet the inclusion criteria of taking at least 6 chronic medications (42%); patients most commonly refused to participate due to lack of interest (69.5%). Enrollnment lasted 9 months at Duke, 12 months at Baylor, and 13 months at UIC. Both Duke and Baylor enrolled more than 200 participants to help reach the total enrollment goal. Additional subjects (more than 600) were enrolled to replace patients who were lost to follow-up.

Feedback about the 5 study domains from investigators, coordinators, and pharmacists is shown in 

. Under the recruitment domain, study staff reported that administrative and pharmacy databases were useful for identifying eligible patients. All 3 sites initially used recruitment letters and phone calls to mimic real-world pharmacy implementation; however, Baylor and UIC switched to in-person clinic recruitment because response rates were low with the mail approach. Duke was able to continue the letter/telephone recruitment strategy given the larger patient population from 6 participating clinics. The differences in recruitment approach translated into the differences in the proportion of participants enrolled to the patients screened for this study (35.3% at UIC, 30.7% at Baylor, and 13.3% at Duke). Patients who were approached at the clinic visit and accompanied by a caregiver were more likely to participate. These caregivers viewed the MTM intervention as a benefit. The presence of a provider champion and a motivated clinic staff was felt to be useful for enhancing recruitment.

For the enrollment visit domain, patients with more chronic illnesses and multiple clinic appointments were more likely to decline participation due to perceived study visit burden. Patients who were concerned about the consent form language, cost of gas, or transportation difficulties were also less likely to participate. The free MTM intervention was perceived as a benefit; however, the study payment ($10 per completed visit, or $30 total) was perceived as too low by the patients. In addition, some patients in the control group inquired whether the MTM intervention could be offered to them after all study follow-up was completed. The amount of space available to conduct the enrollment and MTM visits varied at the 3 sites. UIC had ample space in the academic clinics, but limited space in the outpatient pharmacy. Baylor

used the clinic room space prior to the provider entering the room to see the patient, so timing was essential to limit interference with clinic work flow. Duke held all study visits at a central location that had available rooms depending on the day of the week.

In the MTM pharmacist domain, MTM visits occurring separate from an existing clinical visit had higher no-show or cancellation rates. Reminder phone calls helped reduce missed visits. A total of 186 study participants (88.6%) in the basic MTM group attended the first MTM visit, and 155 (73.8%) participants completed their second MTM visit. A similar proportion of participants in the enhanced MTM group completed their first (n = 196, 89.9%) and second (n = 165, 75.7%) visits. The first MTM visit lasted 45 to 60 minutes, and the second MTM visit lasted 15 to 30 minutes. Access to pill bottles was essential for delivering the MTM intervention because patients had trouble recalling medication names and dosages. The second visit was often unnecessary because most drug-related problems were identified during the first visit. Therefore, the second visit was often used to reinforce or educate patients on the previous medication recommendations. The DRP and ADE forms were straightforward but perceived as impractical for real-world (non-study) settings. DRP form was based on the Modified Pharmaceutical Care Network Europe (PCNE) Drug Assessment Form V 5.01 (see 

) and served as both a checklist and as a documentation tool for this study.

 The ADE form was used to assess symptoms potentially related to medications and contained questions from parts 2 and 3 of a validated research tool developed by Jarernsiripornkul and colleagues.

) of this questionnaire assesses potential side effects of medications through a system-by-system approach. Part 3 (

) of the questionnaire assesses the status of the side effect if the drug was stopped. Non-study MTM providers are unlikely to be able to use these surveys for assessing DRPs and ADEs in routine clinical practice, given the length and detail of questions contained in these documents. After the MTM visit, pharmacists sent medication recommendations via facsimile to patients’ primary care providers (PCPs). Providers often returned the study facsimiles without indicating whether they accepted the pharmacist’s recommendation, or they failed to return the form. Response to e-mail, phone, or face-to-face communication better ensured receipt of the recommendation and implementation of a plan of action.

Within the telephone follow-up domain, study coordinators reported that these calls ranged from 10 to 60 minutes (average, 20 minutes); the length depended on the number of symptoms discussed. Research coordinators asked patients questions from the symptom, utilization, and patient satisfaction surveys at 90 and 180 days. The ADE symptom survey (19 survey items with multiple potential responses to each item, followed by a 10-item survey for each symptom identified by the participant; see Appendix B) was difficult to administer by phone because the interviewer had to maintain a patient’s focus on whether the symptom was related to medication. Also, patients did not refer to the paper copy of the ADE symptom surveys while they were being asked the questions by the interviewer on the telephone. A study folder with a copy of the ADE symptom survey, patient satisfaction survey, patient visit log, consent form, medication list, and contact information of study personnel was given to each patient at the enrollment visit. For the visit (clinic/emergency department/inpatient) utilization form, patients had difficulty recalling the dates of these visits, but having access to the scheduling system allowed the coordinator to find the information. Patients were given a visit log to write down their visit dates, and patients

were either fully compliant or noncompliant with the visit log. Patients with higher socioeconomic status were more likely to complete the visit log.

Finally, under the data collection domain, the coordinators noted that paper case report forms (CRFs) required them to copy and send forms to the coordinating center, which took a lot of time and effort. Suggestions included 1) using carbonless (no carbon required [NCR]) paper to keep 1 copy of the CRF at the site and send the other to the coordinating center for data entry, or 2) creating a web-based data entry system for electronic data capture (EDC). The EDC system could be complemented by telephone follow-up surveys housed in a computer-assisted telephone interview system, which would allow immediate data entry at the time of the call.

We describe our experience implementing a prospective, randomized study to inform clinicians, researchers, and funders about the challenges and successes of communitybased MTM trials. As noted above, successes included identifying potentially eligible patients via medication and billing databases, participation of 10 community clinics resulting in enrollment of a diverse patient population, and monthly study calls that helped standardize operational issues. Challenges included the need to contact numerous patients (5 screened for every 1 enrolled), identifying space to conduct and schedule the MTM visit, contacting PCPs with medication recommendations, telephone follow-up using lengthy symptom questionnaires with variability in patients’ recall of clinic/emergency department visits or hospitalizations, and a paper-based data collection system. A description of the MTM intervention and tool kit with copies of the study forms is available on the Agency for Healthcare Research and Quality website.

Our enrollment challenges are similar to those experienced by pharmacy benefit managers (PBMs) in contacting a population at high risk for a medication-related adverse event due to multiple chronic conditions and medications. MTM interventions by PBMs who contact eligible patients by phone or mail also experience low participation by highly motivated patients and even lower participation by patients who would most benefit from pharmacy coaching.

 A medication review survey packet mailed to 4000 US Department of Defense beneficiaries resulted in 1469 responses (38.1%) to the consent letter, 606 consents (15.7%) to participate, and 373 (9.3%) completed surveys.

 In this study, mailed letter and telephone contact resulted in less participation (13%) than recruitment from within the clinic during existing appointments (30%-35%). Interestingly, about 70% of eligible patients stated that they were not interested in study participation. Non-participation is likely due to the presence of a consent form, required study visits, and telephone followup. Participation rates in a real-world community MTM intervention will hopefully be higher if conducted outside the context of a clinical research project.

These findings provide specific opportunities to improve the design of future MTM studies and disseminate and implement MTM within the community setting. Under study design considerations, recruiting at-risk patients during an existing clinical appointment and delivering the MTM intervention on the same day can reduce participant burden (removes the need for a visit on a separate day or location) and allow the pharmacist to contact the prescriber onsite (not via facsimile) with any newly identified problems related to medication. Flexibility for the MTM pharmacist to decide whether a second MTM visit (or additional visits for complex cases) is required and whether it is done in person or by phone can optimize the effectiveness and efficiency of visits. Lengthy patient-reported outcome surveys (drug-related adverse event reporting, satisfaction, etc) should be shortened to facilitate administration by phone or at a follow-up clinic visit. Healthcare utilization (clinic or emergency department visits, hospitalizations) is better obtained from review of an electronic health record than from patient recall. Finally, the use of a web-based data entry system is preferred to transmitting paper forms to a central location.

For the dissemination and implementation of this MTM intervention within a usual care setting, the ideal situation would be the colocation of a clinical pharmacist within the primary care clinic to deliver the intervention at the point of care. Incorporating this pharmacist as a team member within the patient-centered medical home may be feasible in integrated health systems. Smaller independent practices without the resources required for a dedicated pharmacist may consider collaboration with the MTM clinicians from PBMs, as long as this includes communication from the PCP to the patient about the importance of participation to reduce ADEs. In a usual care setting, the barriers of patient consent and lengthy study-related forms would be removed; therefore, we expect that patient buy-in would be greater than within a trial. Creation of a quality improvement strategy to reduce drug-related problems by implementing MTM would allow prospective measurement of the intervention over time.

In summary, we found that implementation of MTM services not directly linked to a primary care visit (either geographically or temporally) resulted in lower participation by patients and providers. Therefore, participation in future MTM studies or usual care implementation can be improved by integrating these MTM services within the clinic during existing appointments by a pharmacist familiar to the primary care provider.

The authors acknowledge the following study sites and collaborators:

From the University of Illinois at Chicago (coordinating site), Chicago, IL:

Medication Therapy Management Clinicians: 

Shiyun Kim, PharmD; Jessica Michaud, PharmD; Annette Pellegrino, PharmD; Daphne Smith Marsh, PharmD; Jessica Tilton, PharmD; and Lori Wilken, PharmD.

Vicki Groo, PharmD; and Mary Ann Kliethermes, PharmD.

 Ketsya Amboise, PharmD; Adriana Bautista; Zenobia Dotiwala; Yash Jalundhwala; Xiaochen Luo; Alexandra Perez; Sapna Rao; and Funda Tiryaki, PharmD.

Duke University DEcIDE Center Principal Investigator, past: 

From RTI International, Research Triangle Park, NC:

RTI International DEcIDE Center Principal Investigator, past:

From Baylor Health Care System, Dallas, TX:

Nadine Rayan, MHA. Research Assistants: Louann Cole, BA; Teri Cowling, MS; Kate Newman, RN; Bruce Koehler, MPH.

From the Agency for Healthcare Research and Quality, Bethesda, MD:

WEB EXCLUSIVE

Lessons learned from implementation of a pharmacist-delivered medication therapy management intervention in primary care can inform future studies and be adopted into real-world clinical settings.

Patient Safety-Focused Medication Therapy Management: Challenges Affecting Future Implementation

Provider organizations shifting from a fee-for-service world toward a value-based one face a challenging transition. If done correctly, value-based healthcare can enable patients to gain access to and support in using needed medications while simultaneously lowering cost growth and achieving the quality benchmarks that the marketplace will demand. This article provides:

		 A framework for exploring the role of pharmaceuticals in this value-based transition.

		 Some examples of how the challenge is being navigated in the real world.

To achieve the triple aim of better care for individuals, improved health for populations, and slower cost growth, our healthcare system will likely evolve from volume-based (quantity) to value-based (quality and cost) purchasing. Already, both the public and private sectors are moving in this direction. On the public side, the Centers for Medicare & Medicaid Services (CMS) recently published results from the Physician Group Practice (PGP) demonstration project, achieving strong quality metrics along with mixed results on cost savings. Seven of the 10 organizations met all 32 quality benchmarks at year 5 (the remaining 3 organizations met at least 30), a significant increase from the initial year when only 2 organizations achieved all 32.

While the program achieved an impressive $36.2 million in Medicare savings during year 5, half of these savings were earned by the Marshfield Clinic, with the remainder earned by 4 other participants.

On the private side, there are many ongoing activities, such as the Blue Cross Blue Shield of Massachusetts Alternative Quality Contract, which demonstrated a slowing of spending growth and improved quality of care in the first year of the program.

In contrast to the capitation methods of the 1990s, success in a value-based environment will depend upon providers managing both economic and quality elements. Established by CMS, the Medicare Shared Savings Program for accountable care organizations (ACOs)

 includes both a shared savings component and receipt of payments for achieving quality benchmarks. In an environment where only costs are considered, medications are viewed narrowly as a siloed expense item and medication management efforts traditionally focus on medication selection and their associated cost. In a value-based environment, an integrated approach to medications is critical. Without this integration, it is difficult to calculate and recognize the cost offsets and quality benefits that can occur from optimized pharmaceutical use.

To begin this dialogue, our working group developed a conceptual framework for the role of pharmaceuticals in value-based health care. The working group consisted of 7provider organizations (Baystate Health, Billings Clinic, Fairview Health Services, Geisinger Health System, Marshfield Clinic, Sharp HealthCare, and University of Utah Health Care), Premier (an alliance of hospitals and health systems), the American Medical Group Association (an association of multispecialty medical groups), and the National Pharmaceutical Council (a non-advocacy policy research group supported by the pharmaceutical industry). The working groupencompassed a breadth of value-based knowledge, including experience in the PGP demonstrations, current ACO contracts, and potential participation in the CMS ACO or Pioneer pilot programs. We hope our efforts will be considered by provider organizations and those who set expectations for them, such as payers and patients.

The framework begins with a condition-level set of questions designed to elucidate the role of medications in the achievement of both quality and cost goals (see 

1. Is the condition chronic in nature, or is it an acute hospital-based condition?

2. Are there quality measures relating to this condition that are likely to be impacted by pharmaceutical use, and are these quality measures being requested by payers?

3. How large a role do pharmaceuticals play in the cost of treating the condition? What is their share of the overall cost, and to what extent are medical cost offsets possible from appropriate pharmaceutical use?

As the table demonstrates, the economic and quality impact of medications varies by condition. Although congestive heart failure (CHF) may have acute exacerbations, it is chronic in nature and the emphasis of pharmaceutical management is on the outpatient setting. Proposed CMS ACO quality measures for CHF directly involve pharmaceuticals; ie, beta-blocker therapy for left ventricular systolic dysfunction. Pharmaceutical use will also play an important role in preventing hospital readmissions, influencing a common outcome measure. On the financial side, pharmaceuticals are a small component of overall cost, approximately $440 per patient per year.

 However, they have a tremendous potential to contribute to cost offsets through their role in preventing hospitalizations, a cost on average of $7100 to $8400 (with potential to be much higher).

 In the Medicare program alone, CHF led to nearly 1.4 million hospitalizations and $17 billion in spending in 2007.6 A focus on outpatient medical management and the prevention of hospitalizations rather than the acute CHF exacerbations can thus lead to lower costs and higher quality.

In contrast, for rheumatoid arthritis (RA), pharmaceuticals represent a large share of the treatment costs, and it is not yet known what potential there may be for medical cost offsets. There are quality measures relating to the use of RA medications and their impact on functional status, but these measures are not part of value-based programs. As an example, the proposed CMS ACO rules have no quality measures for RA or other important chronic conditions where medications have a central role (eg, multiple sclerosis, breast/lung/colon/prostate cancer). This scenario where there are possible economic incentives but no quality measures may not be in the patients’ best interest. There could be pressure to reduce spending on pharmaceuticals with no countervailing quality assessment to detect underuse.

Beyond condition-level considerations, success in a value-based environment will depend upon understanding and optimally managing medications across conditions and populations. To do so, patients may be stratified by overall risk (eg, low, medium, or high risk) based upon the number and severity of each patient’s conditions. Even patients at low risk (eg, those with no current illness or those not on medications) would be candidates for assessment. These patients may benefit from vaccines, which are frequently included in quality measures and have the potential for cost offsets through the prevention of disease. Patients at high risk require close medical oversight, as these individuals are more likely to experience drug-drug-, drugdisease-, or polypharmacy-related complications. These patients would also benefit from medication reconciliation as part of their hospital discharge process. In addition to achieving better health outcomes, cost offsets may result from the prevention of these adverse events. This cross-condition view raises medication therapy management considerations not evident in a condition-specific perspective. Moving to proactive cross-condition medical management is far more likely to improve quality and reduce costs than acute care management.

The transition from the fee-for-service world toward a value-based one creates a complicated organizational and economic transition, one which we have begun to navigate. For example, Geisinger Health System has developed a portfolio of ProvenCare, both acute and chronic disease procedures/ processes of care to ensure that patients receive high quality treatment according to recognized benchmarks. This program carries with it a financial warranty—if a patient experiences an avoidable complication within 90 days of the procedure, the cost of follow-up care is provided by Geisinger and is covered in its entirety. Other key investments made by Geisinger include an electronic health record (EHR), a systemwide anticoagulation/ medication therapy management program, and a 24-7 telephone nurse hotline. Marshfield Clinic (the PGP participant responsible for half of the year 5 economic savings) credits several key areas for quality investment: a welldeveloped EHR, a 24/7 telephone nurse line for patient advice and triage, clinic programs for anticoagulation and CHF, a cholesterol management program, and a well-established telemedicine initiative. Finally, Fairview Health Services is introducing a revolutionary approach to primary care compensation. Under the new formula, half of the doctors’ income will be based on quality-of-care scores and patient satisfaction surveys. Although some of the preceding examples are not explicitly related to pharmaceuticals, they are germane to the framework as they provide the infrastructure for the pharmaceutical components to succeed.

Although the triple aim of value-based payment may be clear, the path to get there is not. For those making this important transition, we propose the following implementation considerations:

1. Proactively consider medications an essential part of the full spectrum of condition management, and not just an expense or care silo.

2. The role, impact, and characteristics of medication therapy management will vary by condition, and a “one size fits all” approach will not yield optimal clinical or economic outcomes.

3. Composite risk can be used to identify patients who are candidates for medication management strategies to watch for drug-drug, drug-disease, or polypharmacy concerns.

4. In each circumstance where there are conditionspecific incentives to achieve economic savings, there should also be a quality metric to detect underuse.

The evidence-supported pilot approaches to system redesign and condition/medication management as described above, along with others being pioneered elsewhere, deserve to be repeated.

If done correctly, value-based healthcare can enable patients to gain access to and support in using needed medications, while simultaneously lowering cost growth and achieving the quality benchmarks that the marketplace will soon demand. An integrated view of medications and overall care is necessary for the full realization of the potential offsets and benefits that medications can offer. This view can apply to any value-based environment—ACOs, consumer-directed health plans, or patient-centered medical homes. We hope this framework will be of use to provider organizations and their stakeholders as they navigate this new world.

The Working Group on Optimizing Medication Therapy in Value-Based Healthcare: Amanda Brummel, PharmD (Fairview Health Services); F. Douglas Carr, MD (Billings Clinic); Julie Day, MD (University of Utah Health Care); Robert W. Dubois, MD, PhD (National Pharmaceutical Council); Michael Evans, RPh (Geisinger Health System); Marv Feldman, RPh, MS (Premier); Gary Kerr, PharmD (Baystate Health); John Martin, MPH (Premier); Gary Plank, PharmD (Marshfield Clinic); Albert Rizos, PharmD (Sharp HealthCare); Julie Sanderson-Austin, RN (American Medical Group Association); and Kimberly D. Westrich, MA (National Pharmaceutical Council).

From National Pharmaceutical Council (RWD, KDW), Washington DC; Premier Inc (MF, JM), Boston, MA; American Medical Group Association (JS-A), Alexandria, VA.

Dr Dubois and Ms Westrich report employment with the National Pharmaceutical Council, the funder of the study. The authors (MF, JM, JS-A) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (RWD, MF, JM, JS-A, KDW); acquisition of data (RWD); analysis and interpretation of data (RWD, MF); drafting of the manuscript (RWD, JM, JS-A, KDW); critical revision of the manuscript for important intellectual content (RWD, MF, JM, JS-A, KDW); obtaining funding (RWD); and supervision (RWD).

Robert W. Dubois, MD, PhD, 1501 M St, NW, Ste 650, Washington, DC 20005. E-mail: rdubois@npcnow.org.1. Physician Group Practice Demonstration succeeds in improving quality and reducing costs [press release]. Baltimore, MD: Centers for Medicare & Medicaid Services; August 8, 2011. https://www.cms.gov/apps/media/press/release.asp?Counter=4047. Accessed January 30, 2012.

2. Wilensky, GR. Lessons from the Physician Group Practice Demonstration—a sobering reflection. N Engl J Med. 2011;365(18):1659-1661.

3. Song Z, Safran DG, Landon BE, et al. Health care spending and quality in year 1 of the alternative quality contract. N Engl J Med. 2011;365(10): 909-918.

4. Berwick DM. Making good on ACOs’ promise—the final rule for the Medicare shared savings program. N Engl J Med. 2011;365(10): 1753-1756.

5. Sokol MC, McGuigan KA, Vergrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005;43(6):521-530.

6. Joynt KE, Orav JE, Jha AK. The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med. 2011;154(2):94-102.

A framework for the role of pharmaceuticals in value-based healthcare, practical examples from organizations that have made the transition, and implementation considerations.

Role of Pharmaceuticals in Value-Based Healthcare: A Framework for Success

 To examine the association between patient loyalty, as measured by member reten-tion in the health plan, and access to My Health

Manager (MHM), Kaiser Permanente’s PHR, which is linked to its electronic health record, KP HealthConnect.

We conducted a retrospective cohort observational quality improvement project from the third quarter of 2005 to the fourth quarter of 2008 for approximately 394,000 Kaiser Permanente Northwest members.

To control for self-selection bias, we used propensity scores to perform exact 1-to-1 matching without replacement between MHM users and nonusers. We estimated retention rates of the matched data and assessed the association between MHM use and retention versus voluntary termination. We also estimated odds ratios of significant variables impacting member retention.

The probability of remaining a member or being involuntarily terminated versus voluntary termination was 96.7% for users (95% confidence interval [CI], 96.6%-96.7%) and 92.2% for nonusers (95% CI, 92.1%-92.4%; 

<.001). In the logistic model, MHM use was a significant predictor; only tenure and illness burden were stronger predictors. Users were 2.578 (95% CI, 2.487%-2.671%) times more likely to choose to remain members than were nonusers. The impact was more substantial among newer members.

 MHM use was significantly associated with voluntary membership retention. An indicator of patient loyalty, retention is critical to healthcare organizations.

Among 394,000 eligible members in the northwest region, use of My Health Manager (MHM)—Kaiser Permanente’s personal health record linked to its comprehensive electronic health record system&mdash;was associated with the greater likelihood of voluntary retention of membership.

		We conducted a retrospective cohort observational analysis, controlling for self-selection bias using propensity score estimates and exact matching methods.

		 Member retention was assessed using the matched sample; MHM users were 2.578 times more likely to remain members than were nonusers.

		 Patient access to MHM may have contributed to patient loyalty to an integrated health plan and delivery system.

Among American healthcare consumers, considerable interest exists in integrated personal health records (PHRs). Across several consumer-focused surveys, 76% to 86% of respondents expressed interest in having Internet access to their health information, yet far fewer—7%&mdash;had experience doing so.

In a national survey, the majority of respondents reported the belief that online access to health information would have personal benefits that would improve the quality of healthcare.

 People pay more attention to and become more engaged in their health and medical care when they have easy

online access to their health information.

Most investigations report on adoption and the characteristics and preferences of users of online access websites or patient portals integrated with electronic health records (EHRs).

 Far fewer reports document the impact on outcomes of patient portals.

 Documented impacts include increased efficiency, increased patient contacts of all types, and improved Healthcare Effectiveness Data and Information Set (HEDIS) scores.

 Operational cost savings related to reduced mailed and telephone communications have also been documented.

An important potential outcome of patient access to an integrated PHR, in addition to clinical benefits, is increased loyalty to the delivery system as reflected in membership retention. Beyond the obvious negative impact to a healthcare organization’s bottom line of member terminations, terminations are associated with significant costs for acquiring new members, transferring health records, and to some extent, liability related to transferring patients to other providers.

 Indirect costs may also occur related to the organization’s reputation and physician dissatisfaction with the voluntary departure of patients from their care.

Our objective was to examine the association between the use of My Health Manager (MHM), Kaiser Permanente’s integrated PHR, and health plan member retention.

Kaiser Permanente is one of the nation’s leading healthcare providers and not-for-profit health plans, with 9 million members in 9 states and the District of

Columbia. We conducted a retrospective cohort analysis of 394,215 eligible Kaiser Permanente Northwest members between the fourth quarter of 2005 and the third quarter of 2008. Eligible members included those in a mixture of zero- to high-deductible health maintenance organization (HMO) and point-of-service plans who were continuously enrolled or whose membership was terminated. Nearly 94% of the eligible members subscribed to HMO plans. We excluded members under the age of 13 years as they are not eligible to register for MHM access. Data on termination and member characteristics were obtained from KP HealthConnect, Kaiser Permanente’s integrated electronic health record, and other administrative sources.

By registering to use MHM on website kp.org, members gain access to a robust set of online functionalities. Registered users can view portions of their medical record—eg, a health summary including allergies, immunizations, health conditions, and clinical laboratory results&mdash;schedule or change office visit appointments, securely e-mail physicians and other healthcare providers, order prescription refills, view summaries from recent appointments and reminders for needed services, request an update to their medical record, and perform other healthcare-related activities. We defined MHM use as registration, activation, and at least 1 login event during the observation period; in our exploratory analyses, this definition was highly correlated with the number of login events and online functionalities used. During the study period, 162,022 members became MHM users. By March of 2012, 53.3% (218,456) of eligible Kaiser Permanente members in the Northwest region were registered users.

The outcome measure was retention of Kaiser Permanente membership. Health plan members participate in an annual “open period” during which they elect to retain an existing health plan or replace it; every member retained is a membership termination avoided. We identified retention as occurring in the same month in which the member’s employer group renewed its contract with Kaiser Permanente; if the group renewed the contract and the individual did not terminate membership, we categorized the membership as retained. We considered involuntary termination as equivalent to retention; ie, members did not choose to discontinue membership. In contrast, we also identified voluntary termination as occurring in the same month as group contract renewal; if the individual did not renew membership, we categorized the termination as voluntary.

We performed exploratory data analyses on the raw data and calculated retention rates for MHM users and nonusers. To control for self-selection bias in our observational design, we used matching methods to minimize any systematic differences of the known factors between the MHM users and nonusers.

 This method required a design phase to select a subset of data matched on significant covariates of MHM use; we used SAS 9.1 software to estimate propensity scoreson each of the observations using logistic regression between the covariates and MHM use or nonuse, without including the outcome measure of membership retention.

 The covariates included age, gender, tenure (length of KP membership in years), membership type (subscriber, spouse/partner, or dependent), illness burden (measured by average per year, concurrent Diagnostic Cost Group scores), and the presence of diabetes and hypertension. Explanatory variables were treated as categorical; modeling them as continuous variables did not improve model fit.

Patients with online access to key components of their personal health records were 2.6 times more likely than nonusers to remain health plan members.

July 2012

Incidence and Cost of CAP in a Large Working-Age Population

Screening Electronic Veterans' Health Records for Medication Discontinuation

Patient Safety-Focused Medication Therapy Management: Challenges Affecting Future Implementation

Role of Pharmaceuticals in Value-Based Healthcare: A Framework for Success

Association Between Personal Health Record Enrollment and Patient Loyalty

Exploring Health Plan Perspectives in Collecting and Using Data on Race, Ethnicity, and Language

Standardizing Primary Care Physician Panels: Is Age and Sex Good Enough?

Automated Phone and Mail Population Outreach to Promote Colorectal Cancer Screening

Gender Differences in Healthcare Utilization of Patients With Diabetes