Predicting the impact of Medicare Part D implementation on the pharmacy workforce

Research in Social and Administrative Pharmacy

2 (2006) 315e328

Original research

Predicting the impact of Medicare Part Dimplementation on the pharmacy workforce

Brian Meissner, Ph.D.a, Donald Harrison, Ph.D.b,Jean Carter, Ph.D.a,*, Matthew Borrego, Ph.D.c

aPharmacy Administration, Department of Pharmacy Practice, University of Montana,

32 Campus Drive, Missoula, MT 59812-1522, USAbAdministrative Sciences, University of Oklahoma, Oklahoma City, Oklahoma, OK, USA

cPharmacy Administration, University of New Mexico, Albuquerque, NM, USA

Abstract

Background: There is currently a shortage of pharmacist manpower, and it isexpected to continue into the near future. It is also likely that the implementationof Medicare Part D will further aggravate the shortage by increasing demand, but

it is not clear how much impact it will have.Objective: The purpose of this study was to estimate the impact that the new Medi-care drug benefit program will have on pharmacy workforce demand.Methods: Analysis was conducted using forecasting techniques, which combines

traditional statistical theory with both quantitative and qualitative methods. TheAggregated Demand Index (ADI) was designated as the dependent variable. A num-ber of independent variables were selected for their potential to affect the workforce,

demand for prescriptions or clinical services, and patient population. Data for theidentified variables were collected from a variety of sources. Supply and demanddata were analyzed at a national level.

Results: Both historical and univariate forecasts indicated that the demand for phar-macists will continue to exceed the supply of pharmacists. The ADI ratio of pharma-cist demand-to-supply has recently leveled off which means that demand and supplyare in an equilibrium that falls to the demand side. Consequently, the Medicare

* Corresponding author. Tel.: þ1 406 243 5780; fax: þ1 406 243 4353.

E-mail address: [email protected] (J. Carter).

1551-7411/$ - see front matter � 2006 Elsevier Inc. All rights reserved.

doi:10.1016/j.sapharm.2006.07.007

mailto:[email protected]

316 B. Meissner et al. / Research in

Social and Administrative Pharmacy 2 (2006) 315e328

Modernization Act (MMA) is not predicted to produce a dramatic increase in pre-

scription volume, which would change the current demand for pharmacists. Multi-variate forecasting models were not robust primarily due to the lack of precisepredictor variables.Conclusions: Despite the reliance on preliminary univariate forecasts and imprecise

predictor variables, it appears that the increased use of prescriptions due to theMMA Part D will have minimal impact on pharmacist demand.� 2006 Elsevier Inc. All rights reserved.

Keywords: Medicare Part D; Pharmacists; Manpower; Workforce; Forecasting

1. Introduction

Recent studies of the health care workforce indicate that the currentshortage of pharmacists will persist into the future even though there couldbe as many as 225,000 pharmacists by 2010.1 Predictions of pharmacistshortages at the national level have been as high as 157,000 by 2020.2

Researchers and planners alike have been working to characterize, under-stand, and predict future demand for pharmacy services and supply of phar-macists.1,2 Additionally, data related to current manpower supply are beingcollected by the Pharmacy Manpower Project. These data are used to deriveregional and state-level estimates of the Aggregate Demand Index (ADI),which calculates the demand for pharmacists on a monthly basis usingsurvey data submitted by panelists in various pharmacy practice sites.3,4

Whether the current shortage is a continuation of the shortage that beganin the late 1980s or a more recent, separate event is not clear, because thefactors contributing to both shortages appear to be the same.1,4,5 These fac-tors include the number of prescriptions written or filled, number of phar-macy graduates, diversity of pharmacy practice, and number of part-timeworkers.1-4

There is now another factor to consider. Medicare’s new Part D program,implemented by congress as part of the Medicare Modernization Act(MMA) will undoubtedly increase access to prescription medications andclinical pharmacy services for America’s increasingly aging population.The geriatric patient population eligible for this new program is more likelyto use multiple medications on an ongoing basis and requires more intensemedication therapy management, both of which will further amplify theeffects of Medicare Part D implementation on demand for pharmacists. Inthe United States, the average number of prescriptions for a patient agedbetween 65 and 90 years has increased from approximately 19 in 1996 toover 25 by 2002 according to the Medical Expenditure Panel Survey. How-ever, the real extent to which this new program will aggravate pharmacistshortages is not known. Therefore, the purpose of this study is to examine

317B. Meissner et al. / Research in


the effect of Medicare Part D implementation on the predicted pharmacistworkforce shortage using a forecasting method.

2. Methods

2.1. Study design

This study attempted to develop a forecasting model to estimate the de-mand for community pharmacists from January 1, 2006 through December31, 2009 as a result of the MMA Part D using retrospective data fromvarious public sources. Because no human subjects were used, InstitutionalReview Board approval was not required.

2.2. Forecasting

This study used standard economic forecasting to estimate futuremonthly ADI values from September 2005 through August 2009. Forecast-ing is a method that uses historical and presents data to infer what to expectin the future. Prior to making specific forecasts, it is necessary to developa specific model to estimate the dynamic relationship between various pre-dictors.6 This study used ordinary least squares to determine the relation-ships between potential predictor variables and ADI estimates. The nextstep involves developing the forecast model to predict future values basedon historical data and predictor variables.

Although forecasting models use traditional statistical (objective)methods, assessment of each model forecast relies on both subjective andobjective techniques. The objective measures used to determine the robust-ness of our models included the R2 value, Durbin-Watson statistic, AkaikeCriteria (AIC), Schwarz Criteria (SIC), and partial autocorrelation function(PACF).6 The subjective criteria used to examine the validity of the fore-casted model included how closely the predicted estimates matched histori-cal data and how reasonably the forecasted estimates corresponded to theexpected demand.

2.3. Univariate forecasting model

Both univariate and multivariate models were developed, and all of themodels were examined for the presence of a stochastic or deterministictrend, seasonality, and cycles. The univariate model used 1 variable,monthly ADI estimates, and forecasted future estimates (January 1, 2006through December 31, 2009) solely on the basis of its own past values.Thus, univariate forecasts do not directly consider the influence of theMMA Part D, but provide an understanding of the overall trend in thedemand for pharmacists.



2.4. Multivariate forecasting models

The multivariate models were developed and forecasted on the basis oftheir own historical data as well as other predictor variables. The multivariatemodels used both a conditional and an unconditional approach. Essentially,an unconditional model allows the researcher to generate forecasts based onthe past performance of each of the predictors using a vector autoregression(VAR). The unconditional forecasts for each of the predictors were subjec-tively compared to forecasted estimates found within the literature to deter-mine the appropriateness. Historical monthly prescription volume wasobtained from National Association of Chain Drug Stores (NACDS) andwas used to capture the influence of the MMA Part D on the demand forpharmacists. The historical data for both monthly prescription volume andother predictor variables were used in a VAR to create forecasts of futurevalues. Monthly prescription volume estimates obtained from the literatureas a consequence of theMMAPart D were then compared with those derivedthrough VAR. It was not expected that those estimates derived from VARwould closely reflect the prescription volume as a result of the MMA PartD. Thus, VAR predictions were modified as needed to more closely reflectthose values predicted in the literature as a result of the MMA Part D.

In contrast, a conditional model requires the researcher to subjectivelyinput future predictor values, which is then used to forecast future ADIestimates. This method allows monthly prescription volume estimatesobtained in the literature to be imputed over the forecasted time frame.The usefulness of the predictors in the models was examined using a GrangerCausality test.6 The historical time frame used to develop the multivariateforecasts was March 2001 through December 2005. This time frame is short-er than that of the univariate forecasts due to the limited availability ofmonthly prescription volume data.

2.5. Dependent variable

Of the several measures or estimates of pharmacist workforce reviewed aspotential dependent variables, the ADI, which is a ratio of pharmacistdemand-to-supply, was selected.3 The ADI appeared to have the most ro-bust mechanism for deriving the severity level of the pharmacist shortage.

As mentioned earlier, the ADI estimate was developed as part of the Phar-macyManpower project and predicts the nationwide and regional demand forpharmacists in a community, institutional, and combined settings. Morespecifically, through monthly surveys to a panel of pharmacist employersnationwide the ADI quantifies the difficulty in filling pharmacist vacancies.The ADI estimate is a ratio of demand-to-supply that ranges from 1 to 5with the lower numbers indicating that the demand is much less than the sup-ply and the higher numbers indicating a high demand with difficulty in fillingopen positions. A mid-range value of 3 indicates supply is meeting demand.



The current month’s ADI estimates of workforce demand are reported bysite, state, and region. Because this study needed only those estimates relatedto community retail dispensing, which would be affected by the MMA PartD implementation, only the national community site estimates were used.The national estimates of manpower demand are based on aggregated indi-vidual state-level ADI values and the percentage of the total U.S. popula-tion that resides in each of the states, resulting in a weighted ADIaverage. Thus, the national ADI estimate for community sites is a compositemeasure addressing the overall demand for pharmacists in the United Statesthat month. The data used to estimate ADI have been captured sinceAugust of 1999. This study used monthly ADI estimates from August of1999 through August of 2005 to develop a forecast model. The actualmonthly estimates were obtained directly from Dr Katherine Knapp, oneof the individuals instrumental in the development of the ADI.3

2.6. Independent variables

Several independent variables were identified based on prior research thatpredicted the severity of the pharmacist shortage and were used in the devel-opment of the multivariate models.

Table 1 provides a description of the operationalization and source foreach of the independent variables considered in various models.

According to the 2000 Health Resources and Services Administration(HRSA) report, the demand for pharmacists is the result of several factorsincluding increased use of prescription medications; market growth of retailpharmacies; expansion of pharmacists’ roles; changing demographic profileof pharmacists; and burden placed on pharmacists by third-party payers.5

These factors were used to operationalize a number of the predictor vari-ables. The 2000 HRSA report was descriptive and did not quantitativelylink the shortage of pharmacists with the variables. Other sources wereused to derive quantitative information.

One of the main drivers influencing pharmacists demand is prescriptionvolume and its growth rate as documented by Knapp et al.4 Monthly pre-scription volume data were obtained from NACDS. It is also importantto consider, direct-to-consumer (DTC) advertising not only because it influ-ences the total prescription volume, but it also engages the pharmacist-patient relationship. Thus, the annual amount spent on DTC was capturedfrom Intercontinental Marketing Services. Additionally, use of mail-orderpharmacies tend to decrease retail prescription volume; thus, mail-orderuse was included as a predictor variable.

A number of other factors indirectly influence the demand for phar-macists. Clearly, the number of registered pharmacies influences the overalldemand. As a result, the annual number of registered pharmacies was consid-ered in the multivariate models. The expanded professional opportunitiesboth within and outside the retail setting are expected to further influence the



demand for pharmacists. As a consequence, the percent of all pharmacistswho are working within a community setting was considered. Additionally,as the demand for pharmacists and their roles expand, states have increasedthemaximumnumber of technicians that pharmacists can supervise. As a con-sequence, the number of technicians, the ratio of pharmacists to technicians,and mean technician salary were considered in the multivariate analysis.

Table 1

Variables used in forecasting models

Variable name Description Sources of data

Prescription

volume

Monthly prescription volume from March

2001 to March 2005

NACDS7

Third party Annual estimate of the percentage of costs

paid by third-party payer from 1999 to

2004

NACDS Foundation Chain

Pharmacy Industry Profile,

20057

Total number

of technicians

Annual estimate of the number of

technicians within the United States from

1999 to 2004

BLS8

Total number of

pharmacists

Annual estimates of the number of active

pharmacists within the United States

from 1999 to 2005

Estimates from Gershon et al1

Technician

wages

Annual average wage for a pharmacy

technician from 1999 to 2004

BLS9

Pharmacist

wages

Annual average wage for a pharmacist from

1999 to 2004

BLS9

Pharmacist-to-

technician

ratio

Ratio of the number of pharmacists to

technicians according to BLS estimates

from 1999 to 2004

BLS9

New graduates Annual estimates of entry-level pharmacy

graduates from 1999 to 2005; predicted

schools/graduates 2006-2010

AACP Institutional Research

Reports10,11 ACPE12

Direct to

consumer

Annual estimate of dollars spent on

pharmaceutical advertising from

television, magazines, newspapers, radio,

and outdoor advertising from 1999 to 2004

IMS13

Mail order Annual percentage of prescriptions that

account for mail order from 1999 to 2004

NACDS Foundation Chain

Pharmacy Industry Profile,

20057

Number of

pharmacies

Estimates of the total number of

pharmacies in the United States from

1999 to 2004

NABP, Survey of Law, 2000,

2001, 2002, 2003, 2004, and

200514

Percent of

community

pharmacists

Percent of pharmacists who work in an

ambulatory/community setting from

1999 through 2004

NABP, Survey of Law, 2000,

2001, 2002, 2003, 2004, and

200514

Retiring from

pharmacy

Estimated number of annual pharmacists

who retire from the profession from 1999

to 2004

Estimates from Gershon et al1

AACP, American Association of Colleges of Pharmacy; ACPE, Accreditation Council for

Pharmacy Education; BLS, Bureau of Labor Statistics; IMS, Intercontinental Marketing Ser-

vices; NABP, National Association of Boards of Pharmacy; NACDS, National Association

of Chain Drug Stores.



Third-party prescriptions have been found to have a negative affect onpharmacists’ productivity. Research has indicated that between 9.5% and20% of a pharmacist’s day is spent addressing third-party prescriptionissues.5 Thus, the percent of prescription dispensed through a third-partyadministrator was considered.

The multivariate analysis also examined the number of active pharma-cists, new graduates, and retiring pharmacists.1 Pharmacists’ salaries werealso included in assessment of supply side factors because their upwardtrend may influence the overall demand for pharmacists.5

Overall, there was limited quantitative research assessing the predictorsof pharmacists demand. Walton et al,15 quantitatively validated the correla-tion between the number of filled pharmacy positions and the number ofnew graduates, prescription volume, hospital days, number of licensed com-munity pharmacies, and state residents aged older than 65 years. Regressionmodels found that the state population and community pharmacy prescrip-tion volume were the only significant variables in predicting the number offilled pharmacist positions.15 Clearly, some of the proposed predictor vari-ables have stronger rationale supporting their use in multivariate modelsthan others as it relates to predicting the demand for pharmacists, but itwas thought that being more comprehensive may enhance further research.Due to potential overlap of influence among some of the variables, the pres-ence of multicollinearity was also examined.

Unlike the ADI and prescription volume, which are monthly estimates,all of the predictor variables selected for this study are assessed annually.Annual estimates were converted to monthly estimates to maximize the sen-sitivity of forecasting models. Where possible, additional information wasused to account for seasonal variations. When none was available, totalamounts were divided into 12 equal parts (eg, pharmacists’ wages) andratios or means were maintained at the same value each month (eg, percent-ages of mail-order prescriptions or community pharmacists).

Unlike the monthly ADI estimates, which were obtained through August2005, most of the predictor estimates were only available up to December2004. As a result, univariate forecasting models were developed to estimatethe values for the ‘‘missing’’ months (January 2005 through August 2005)using the criteria explained above to identify the most appropriate forecasts.The annual first professional degree graduate estimates were divided intoDecember graduates (20%) and May graduates (80%) to better indicatewhen entry-level pharmacists were entering the workforce.

3. Results

Overall, there was an unmet demand for pharmacists available to fillopen positions (Figure 1) within a community setting throughout the histor-ical data (August 1999 to August 2005). The magnitude of the unmet



demand has been slowly trending downward from an ADI of 4.0 to the mid-3over a 5-year period until January 2005 when the ADI leveled off at around3.5 implying demand, and supply factors were in equilibrium at a point wheredemand was still greater than supply but the situation was stable.

3.1. Univariate models

The univariate model was developed based on the following model (seeEquation 1):

ADI¼ boþ btimeþARð1Þ þARð2Þ þARð3Þ þ seasonalityþ et ð1Þ

bo ¼ y Intercept; btime ¼ time period; AR (1-3) ¼ first through third orderautoregressive processes; seasonality ¼ seasonal variation; et ¼ unexplainederror term.

This model predicted approximately 55% (adjusted R2) of the variance inthe ADI. The Durbin-Watson statistic approached 2, which indicated no au-tocorrelation, and the univariate model was developed to minimize both theAIC and SIC values. The resulting univariate forecast indicated that the de-mand for pharmacist will continue to decrease. More specifically, the fore-casted demand-to-supply ratio (ADI) will approach 3.0, which indicatesa sufficient supply of pharmacists given the demand. However, the confi-dence intervals for the model were large, indicating the potential for signif-icant variation in the forecasted estimates and a need for caution wheninterpreting the results (Figure 1).

The demand-to-supply ratio for pharmacists was decreasing at the sametime the volume of prescriptions (Figure 2) was increasing. The number of

2.00

2.50

3.00

3.50

4.00

4.50

1999

:08

2000

:04

2000

:12

2001

:08

2002

:04

2002

:12

2003

:08

2004

:04

2004

:12

2005

:08

2006

:04

2006

:12

2007

:08

2008

:04

2008

:12

2009

:08

Months

AD

I

ADIForecasted ADI

Upper Bound 95% CILower Bound 95% CI

Adjusted r2 = 0.547; Durbin-Watson = 1.90; AIK = –0.94; SIC = –0.43; PACF = white noise

Fig. 1. Historical and univariate forecasted Aggregated Demand Index estimates from 1999

through 2009.



yearly prescriptions dispensed increased by 6% between 2002 and 2005. Fu-ture increases in prescription volume due to the implementation of MMAPart D were estimated by Heffler et al to grow by 8.9% for 2005-2006and 6.5% for 2006-2011, which indicates continued increases in prescriptionvolume.16 Tests of individual predictor variables produced mixed results.

3.2. Predictors supporting observation

Table 2 presents the estimates for each of the predictor values consideredin this study. One predictor that may account for the decreasing pharmacistdemand yet increasing prescription volume (Figure 2) was the ratio of phar-macists to technicians. These ratios, which are a function of state laws, de-creased by 24% (from 1.15 pharmacists for each technician in 1999 to 0.87in 2004). This decrease appears to be driven by a 29% increase in number oftechnicians relative to a smaller increase (9%) in active pharmacists.

The number of new graduates relative to the estimated number of phar-macists switching careers/retiring increased from 2,094 additional graduatesin 1999 to 3,058 in 2005. Similarly, pharmacists’ median salary increased ap-proximately by 33%, whereas technicians’ salary increased by only 23%.Another contributor to the decreasing demand for pharmacists could bethe increase in use of mail-order pharmacies, which increased from 4.9%in 1999 to 6.1% in 2004.

3.3. Predictors opposing

In contrast, other predictors of pharmacist demand indicated that phar-macist demand should be increasing. For example, third-party payers forprescription drug coverage have been estimated to increase the amount oftime by 20% for each prescription filled.17 During the study time period,the use of third-party payers increased approximately by 10%.

200210220230240250260270280290

2001

-03

2001

-07

2001

-11

2002

-03

2002

-07

2002

-11

2003

-03

2003

-07

2003

-11

2004

-03

2004

-07

2004

-11

2005

-03

2005

-07

2005

-11

Year

Pre

sc

rip

tio

n V

olu

me

(in

m

illio

ns

)

Fig. 2. Prescription volume (in millions) from March 2001 through March 2005.



Another predictor, the number of registered pharmacies continued to in-crease between 1999 and 2004. In addition, historical data have indicatedthat the percentage of pharmacists working within a retail setting hasslightly decreased from 60% of all registered pharmacists in 1999 to 55%in 2004. Finally, the amount of money spent on DTC advertising of phar-maceutical advertising increased to 144% between 1999 and 2004.

3.4. Multivariate model result

The development of the multivariate model failed to result in any of theexpected predictor variables being statistically significant after controllingfor trend, seasonality, and cycles. Therefore, the results of the multivariatemodel produced results that were very similar to those developed from theunivariate model. As a result, no multivariate forecast models were found tosignificantly add to the prediction of future pharmacy workforce demand.The lack of robust multivariate models is likely a function of the lack ofsensitivity of the predictor variables relative to the ADI estimates.

Table 2

Longitudinal (1999-2005) estimates of pharmacy demand predictors

Year 1999 2000 2001 2002 2003 2004 2005a

Third-party volume 79.0% 81.5% 84.0% 85.6% 86.4% 87.3% d

Number of technicians 196,430 190,940 207,130 207,380 211,270 255,290 dNumber of active

pharmacists

192,793 196,011 198,718 201,359 204,194 207,243 210,321

Pharmacist-to-

technician ratio

1.152064 1.113753 1.079564 1.057913 1.017797 0.87336 d

Median annual

pharmacist salary

$63,030 $69,440 $72,830 $75,140 $78,620 $84,370 d

Median annual

technician salary

$20,050 $21,600 $22,510 $23,200 $23,860 $24,700 d

Number of graduates 7,141 7,000 7,573 7,488 8,158 8,288 9,364

Direct-to-consumer

dollars spent

$1,848 $2,467 $2,679 $2,638 $3,235 $4,510 d

Percent of mail-order

pharmacy

4.9% 5.0% 5.3% 5.5% 5.8% 6.1% d

Number of licensed

pharmacies

72,068 73,782 74,564 74,951 78,505 80,890 d

Percent of pharmacists

who practice in

a retail setting

60.0% 59.0% 59.1% 58.1% 58.0% 55.7% d

Estimated number

of retiring

pharmacists

5,047 5,040 5,101 5,157 5,195 5,230 5,289

a At the time this manuscript was developed only a limited number of 2005 estimates were

available.



4. Discussion

Throughout the historical and forecasted time period, the demand forpharmacists always exceeded supply. However, the demand-to-supply ratio(ADI) appears to be stable after trending downwards for several years;a trend that was validated by predictor variables including the pharma-cist-to-technician ratio, number of new pharmacists, new graduates relativeto those retiring or switching careers, and the increased use of mail-orderpharmacies.

The effect of the implementation of dispensing services through MMAPart D on the trend is not clear, but it appears it will have little or no impactin the long-term. The large confidence interval makes an accurate predictionimpossible. Currently, with a little more than 3 months’ experience with thenew Medicare drug benefit and based on mainly anecdotal evidence, it ap-pears that there has been little impact on the demand for prescriptions.However, the new drug benefit has had a short-term impact on the demandfor pharmacists’ time; mainly due to a multitude of technical difficulties ex-perienced during the implementation phase of the new benefit. With time,these technical difficulties are being solved and the demand for pharmacists’time should return to previous levels.

When just prescription volume is considered, it does not appear that theimplementation of a Medicare prescription drug benefit program will havemuch impact on future pharmacist demand. However, it must be empha-sized that pharmacists are involved in more than just dispensing, and othervariables related to those services must be considered. Without consideringother services provided by pharmacists and the confounding effects of fac-tors such as pharmacist-to-technician ratios, and the expanding roles andworkplace environments where pharmacists are now used; these estimatescould potentially lack the robustness necessary to make sound policydecisions related to the pharmacist workforce.

The studies published as part of the Pharmacy Manpower Project havedescribed historical trends using longitudinal methods. Knapp and Livesey3

reported that between 1999 and 2001 the ADI indicated that pharmacist po-sitions were ‘‘somewhat difficult’’ to ‘‘difficult to fill,’’ with regional varia-tion. A more recent publication by Knapp et al4 indicated that thedemand continues to outpace the supply of pharmacists, but the demandhas been moving toward an adequate supply. Finally, because of the poten-tial implications for the profession of pharmacy, it is imperative that the var-ious professional organizations continuously monitor the supply anddemand for pharmacists and undertake efforts needed to improve futureforecasting efforts.

The implementation of the MMA Part D program is just 1 example of thekind of event that impacts the pharmacy demand-to-supply balance. Be-cause there will continue to be new demands for pharmacists and changesin the supply in the future, new ways to look at workforce demand and



supply will be needed. The usefulness of forecasting methods for looking atfuture pharmacist demand and supply was shown by this study; however,the usefulness of forecasting will be limited until monthly data for keyvariables are collected.

4.1. Limitations

Due to limitations of this study, which were beyond the researchers’influence, readers should exercise caution when interpreting the results. Aprimary limitation is the crudeness of the data available for the predictorvariables; these rough measures did not allow for the appropriate develop-ment and precision of multivariate forecasting models. The data used in thisstudy were from a multitude of sources and collected by a variety ofmethods.

All of the quantitative variables thought to influence pharmacist demandobtained for the purposes of this study were measured on a yearly basis. Toleverage the sensitivity of the historical ADI estimates, these predictor vari-ables were modified to reflect monthly estimates. This resulted in more lim-ited multivariate models. The additional variables thought to influence thedemand for pharmacists were not directly considered in the models becausethese data were not readily accessible at an appropriate national level or ona longitudinal basis.

Not all potential variables were included in the models and this may haveaffected results. Such variables were access to pharmacy related technology;complexity of patient’s medication therapy; expanded pharmacy storehours; ratio of male to female registered pharmacists; number of part-time pharmacists; expanded role of pharmacy technicians; the expandedrole of pharmacists; and expanded employment opportunities for pharma-cists in the health care industry, outside of pharmacists’ traditional roles.

Finally, as Knapp et al have already stated, difficulties encountered inforecasting are due in part to the use of historical data trends that are basedon health care and academic environments that no longer exist.4

4.2. Better data are needed

The lack of available data to better estimate future pharmacy workforcedemand is troubling. Data must be developed if the pharmacy profession isexpected to confront the uncertainties of a future that could be significantlyaltered by the implementation of the MMA. The potential increase in de-mand for prescriptions by Medicare beneficiaries is only one aspect ofchanges brought about by the MMA. The potential increased demand fordrug therapy management provided by pharmacists could also have a signif-icant impact on demand for pharmacists in the retail setting. Therefore,great care is needed in the initial time period after the implementation ofMedicare Part D to measure its effect on demand for prescriptions and



pharmacists’ services so that the potential effect on future demand forpharmacists may be estimated in as timely manner as possible.

The pharmacy profession must not be slow in reacting to future changesin demand. Therefore, professional pharmacy organizations must be proac-tive in supporting the initiatives necessary to gather and analyze the data re-quired. If the shortage of pharmacists becomes too acute, the health caremarketplace will find ways to resolve the shortage that may not be palatableto the profession (eg, increased automation, reliance on technicians, and ex-panded roles of allied health professionals to include dispensing prescrip-tions). Equally disconcerting is the potential for a surplus of pharmacists.New pharmacy programs are being developed throughout the country basedon the assumption that the shortage will continue.

5. Conclusions

Based on the results of this study, it appears that the implementation ofMMA Part D should have minimal influence on the overall demand forpharmacists. The forecast models indicate that future demand pharmacistswill continue to decrease relative to the supply, but caution is recommendedwhen interpreting these results due to the crudeness of the data. A more for-mal system for capturing monthly data on variables that may more accu-rately predict the impact on the demand for pharmacists is necessary toallow for more precise forecasting estimates.

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