• Community pharmacy;
  • Outcomes assessment;
  • Musculoskeletal disorders


  1. Top of page
  2. Abstract
  7. Acknowledgements


To evaluate the feasibility and benefit of capturing outcomes data in community pharmacy settings, and to characterize the health status, resource use, and medication use of patients with musculoskeletal disorders.


Patients (n = 460) with musculoskeletal disorders including osteoarthritis (OA), rheumatoid arthritis (RA), and low back pain from 12 community pharmacy sites responded to disease-specific questions, the Medical Outcomes Study Short Form-36 (SF-36) health survey, demographics, and resource use using touch screen computer technology. Patients provided information and met with a community pharmacist for scheduled visits at baseline, 3, 6, 9, and 12 months. Pharmacists, with the aid of the patient-reported information, documented medication use and identified and addressed drug therapy problems of the patients at each visit. Baseline results, based on descriptive statistics are reported.


OA was reported by 71% of the patients, 55% reported low back pain, and 19% reported RA. Despite receiving a variety of analgesic medications, a majority of the patients reported experiencing moderate to severe pain. SF-36 scores of the study population were significantly lower than age-adjusted population norms, with arthritis patients reporting worse physical health than patients with low back pain. Drug therapy problems were identified in 58% of the population, with need for additional drug therapy (31%) and adverse drug reactions (18%) being the most common problems identified.


Results indicate that routine capture of patient-reported health outcomes data is feasible in community pharmacy settings using touch screen technology.


  1. Top of page
  2. Abstract
  7. Acknowledgements

Can measures of health outcomes aid in targeting, planning, and monitoring individual patient outcomes of care in ambulatory practice settings? Although health outcomes assessment and its interpretation is understood in research and policy-making circles, the practical use of these measures in an ambulatory practice setting is limited (1). These limitations are the result of a lack of experience and understanding of the concepts and measures of health status by primary health care providers, and concerns about the feasibility of data collection and integrating data interpretation into day-to-day clinical activities (2–4).

At population levels, health outcome measures have been accepted as indicators for health care quality assessment. This is apparent from the efforts of various federal agencies and policy-making bodies. The use of the Medical Outcomes Study Short Form-36 health survey (SF-36) to track health status of Medicare beneficiaries in the Health of Seniors project by the Health Care Financing Administration, the inclusion of outcome measures in the HEDIS data set by the National Committee for Quality Assurance, and the proposal to use outcome measures in the accreditation process by the Foundation for Accountability are but a few examples of population-based endeavors.

Evidence of successful use of outcomes measures at the patient level to facilitate improved patient care by clinicians is limited (5). Although physician practices and tertiary care centers have been the focus of such point-of-service outcome measurement (5–11), pharmacies also provide a unique opportunity to capture and utilize health outcomes data (12–15). Pharmacists have frequent contact with patients affected by chronic medical conditions, and generally are in an ideal position to collect data on process and outcomes of care. Pharmacists could use health outcomes information to evaluate the impact of treatment on patients' health status and to focus their patient-counseling efforts in appropriate areas. With immediate access to the prescription and/or over-the-counter medications taken by the patients, pharmacists could initiate appropriate interventions to improve the health status of their patients.

However, a key factor limiting use of outcomes assessment at the point of service, such as a community pharmacy, is the means of gathering such information. With advances in technology, use of electronic devices to capture information is becoming common. Information is being gathered using devices such as touch-sensitive video monitors and hand-held computers (8, 16). These devices allow for more efficient data collection and the generation of usable outputs in a timely manner.

The purpose of this research project was to evaluate the feasibility and usefulness of collecting patient-reported health outcomes information coupled with a standardized presentation of process of care measures by community pharmacists. The study design and baseline characteristics of the study population are described in this article. Evaluation of whether or not the availability of outcomes information contributes to improved patient management by the pharmacist will be addressed in future studies.


  1. Top of page
  2. Abstract
  7. Acknowledgements


Twelve community pharmacies that are constituents of Outcomes Certified Pharmaceutical Care Network (CPCN) in eastern Iowa participated in this study. Information on these pharmacies and participating community pharmacists can be found in the acknowledgment section.


This 12-month prospective observational study began on February 1, 1999 with a 6-week enrollment period. A visit during this period served as the baseline visit with subsequent, quarterly visits scheduled for each patient at 3, 6, 9, and 12 months. A central institutional review board approved the protocol for this study prior to the study initiation.

Study population

In order to be eligible for this study, patients had to satisfy all of the following inclusion criteria:

  • 1
    Pharmacy patients could either be new or established.
  • 2
    Patients received a minimum of 3 months supply of any nonsteroidal antiinflammatory drug (NSAID), narcotic, or non-narcotic analgesic for the management of musculoskeletal disorders, specifically osteoarthritis (OA), rheumatoid arthritis (RA), or low back pain over the past 12 months. Pharmacist's records ascertained the NSAID or analgesic use among these patients. Patient reports of their disease condition were deemed appropriate for this study and were not validated by their primary care physician.
  • 3
    Patients had to be at least 18 years of age.
  • 4
    Patients had to be non-institutionalized and willing to fulfill the visit requirements.
  • 5
    Patients had to be able to read, write, and understand English.
  • 6
    Patients had to be willing to provide informed consent to participate in this study.

A patient was ineligible if he or she did not meet any of the inclusion criteria, was participating in other study protocols, or was, in the opinion of the participating pharmacist, unable to complete the study.

Each participating community pharmacist identified potential participants by reviewing their dispensing database to identify patients who had received a 3-month supply of a prescription analgesic or antiinflammatory drug in the previous year. The pharmacist then contacted identified participants to determine their eligibility and willingness to participate in the study.

Data collection

At baseline and at each followup visit ASSIST touch-screen computer technology (ASSIST Technologies, Scottsdale, Arizona) was used to capture and document all visit-specific information. Each pharmacy had one data collection terminal installed in an appropriate location within the pharmacy.

Patients and pharmacists provided data at each visit. Patients responded to a series of questions including the SF-36 health survey (17), health care resources used, disease-specific questions, comorbidities, gastrointestinal symptoms experienced, and demographics. The pharmacists documented prescription and over-the-counter medications (OTC) taken by the patient, any drug therapy problems identified, interventions made, and the outcomes of those interventions if they resolved the drug therapy problem. Drug therapy problems were categorized into: need for additional drug therapy; dosage too low; dosage too high; adverse drug reaction; unnecessary drug therapy; wrong drug; or non-adherence (18). Additionally, both patients and pharmacists answered questions regarding their opinions of the data capture process and its usefulness.

Study procedures

Participating pharmacists were trained on the specifics of the study protocol, which included patient recruitment, outcomes data collection, and data interpretation and documentation using the touch screen technology. Eligible patients, once identified, were informed about the purpose of the study by the pharmacists. If an eligible patient refused to participate in the study, the reason for refusal along with age and sex was recorded. Once the patient agreed to participate in the study, he or she signed the informed consent. At each visit, patients were asked to complete the questionnaire using the touch screen technology with instructions given to answer all questions completely and accurately without prompts from the pharmacists. After the patient completed the questionnaire, 2 reports were immediately printed. One report highlighted responses that were predetermined to be clinically important indicators of functional status and resources utilization. These predetermined responses were identified, based on the consensus of the participating pharmacists and the investigators, as critical markers that might indicate health-related problems among the patients. The other report included a graph of the patient's SF-36 health survey scores compared to age- and sex-adjusted population norms. Previous visit scores were available at each of the followup visits.

The pharmacists reviewed the reports with the patients to determine if there were any health or medication issues that should be addressed. The pharmacist documented information regarding medication use, any drug therapy problems identified, as well as any interventions, using the same touch screen technology. Any changes to the medications that the patients were taking was done in consultation with the patients' physician. Data that were stripped of any patient identifiers were electronically transmitted weekly by each pharmacy to a central location, combined from all the pharmacies, and collectively analyzed.

Data analyses

Aggregated data collected during the baseline visit were used for data analyses. Statistical analysis was performed with SAS Software, version 6.03 (SAS Institute, Cary, NC). Descriptive statistics were used to describe the demographics, health-related quality of life, health care resource utilization, medication use, and drug therapy problems identified by the pharmacists. The SF-36 health survey scores were compared between conditions using analysis of covariance, with age, sex, education, marital status, severity of pain, and comorbid conditions as covariates. Scheffe's test for multiple comparison was also performed.


  1. Top of page
  2. Abstract
  7. Acknowledgements

A total of 635 patients were contacted, of which 461 (72.5%) completed the baseline survey. Patients who refused to participate in the study (n = 174) gave “lack of interest” (11.0%), “no transportation” (4.6%), and “too busy” (4.3%) as the major reasons for not enrolling in the study. The average age of the patients who refused to participate was 58.6 years, and 61.5% were women.

Respondent characteristics

Table 1 lists the demographic characteristics of the participants. The mean age of patients enrolled in the study was 59.2 years, and women constituted 69% of the study population. The majority of the participants were married, had a minimum of a high school degree, and had never smoked. Nearly all participants had health insurance coverage, with 27% of the patients paying out-of-pocket for their prescription medications, and 65% having copayment arrangements for their prescription drug coverage.

Table 1. Study population demographics at baseline (n = 460)
VariablesValues n (%)
Age (years)
 Mean ± SD59.2 ± 13.5
 Female317 (69)
 Male144 (31)
Marital status
 Married298 (65)
 Divorced53 (12)
 Separated4 (1)
 Widowed72 (16)
 Never married33 (7)
Educational status
 8th grade or less21 (5)
 Some high but did not graduate34 (7)
 High school graduate196 (43)
 Some college or 2 year degree111 (24)
 4 year college graduate49 (11)
 More than 4 year college degree49 (11)
Smoking status
 Every day54 (12)
 Some days12 (3)
 Not at all394 (86)
Health Insurance status
 Yes436 (95)
 No24 (5)
Insurance type
 Medicare only16 (4)
 Medicare with supplemental148 (34)
 Medicaid19 (4)
 Private205 (47)
 Other48 (11)
Payment method for prescription medications
 Out of pocket113 (26)
 Co-pay282 (65)
 Major medical26 (6)
 Other15 (3)

A majority of the patients reported having OA (71%), either of the hip/knee, the hand/wrist, or both. Low back pain was the second most common disease condition (55%), followed by RA (19%) (Table 2). About 37% reported having one disease condition (arthritis of hand/wrist, arthritis of hip/knee, RA, or low back pain), 31% reported 2 conditions, 20% reported 3 conditions, and 4% reported all 4 conditions. Eight percent of the patients failed to report any condition and were excluded from any future analysis. Hypertension was the most commonly reported comorbid condition (45%), followed by sciatica (34%). Nearly 25% of the patients reported having sleep problems, depression, or stomach-related problems. Other comorbid conditions reported were osteoporosis (16%); diabetes (11%); respiratory disorders (10%); cancer (10%); angina (8%); myocardial infarction (7%); stroke (5%); other heart conditions (14%); Crohn's disease (4%); and congestive heart failure (3%). Nineteen percent reported no comorbidities.

Table 2. Description of disease characteristics for study population at baseline
Disease characteristicsArthritis of hip/knee n (%)Arthritis of hand/wrist n (%)Rheumatoid arthritis n (%)Low back pain n (%)
  • *

    Disease durations for low back pain were less than 1 month, 1–5 months, 5–10 months, 10–12 months, and >1 year.

  • Patient global assessment question was not administered to low back pain patients.

Disease duration*
 Less than 1 year6 (3)3 (2)2 (3)1 (3)
 1–5 years67 (28)61 (34)20 (25)2 (5)
 5–10 years64 (27)38 (21)16 (20)6 (3)
 10–15 years46 (19)31 (18)14 (18)3 (7)
 More than 15 years56 (23)44 (25)28 (35)206 (91)
Pain assessment
 None4 (2)2 (1)1 (1)10 (5)
 Very mild19 (8)24 (16)6 (8)19 (9)
 Mild34 (16)33 (20)22 (29)41 (19)
 Moderate126 (56)81 (50)37 (49)100 (47)
 Severe41 (18)21 (13)10 (13)44 (21)
Patient Global assessment of arthritis condition
 Very good13 (6)15 (10)7 (9)
 Good91 (43)70 (45)35 (47)
 Fair92 (43)59 (37)26 (35)
 Poor14 (6)8 (5)4 (5)
 Very poor3 (1)7 (4)3 (4)

When specifically asked about certain gastrointestinal symptoms experienced by the patients in the previous 3 months, up to 52% of the patients reported experiencing symptoms such as pain in the upper stomach, burping or belching, nausea, heartburn, bloating, sour taste, passing gas, and/or bad breath.

When asked about aids and devices used to perform their daily living activities, canes (14%), jar openers (15%); and bath tub bars (14%) were the most common devices used. About 29% of the study population needed help with gripping and opening things, 21% with errands and chores, and 16% with reaching (data not shown).

Disease severity

At baseline, patients were asked about the duration of their disease condition, and about the pain that they were experiencing. Not all patients were able to complete these questions at baseline due to software error. Results are presented in Table 2 for those patients who completed the questions.

Of the patients with arthritis of the hip/knee and hand/wrist, 56% and 50% reported moderate pain and 18% and 13% reported severe pain, respectively. These patients also reported the global assessment of their disease to be good or fair. For patients with RA, almost 50% reported experiencing moderate pain and long disease duration (>10 years). For patients with low back pain, over 75% reported experiencing moderate or severe pain.

Health-related quality of life of study population

Figure 1 compares the scores in each domain of the SF-36 health survey for the study population to age-adjusted general US population norms (17). The scores for every domain of the SF-36 health survey were lower for the study population than the corresponding age-adjusted norms.

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Figure 1. Comparison of baseline Medical Outcomes Short Form-36 health survey scores between study population and age-adjusted general US population norms. Higher scores represent better health status. PF = physical functioning; RP = role physical; BP = bodily pain; GH = general health; VT = vitality; RE = role emotional; SF = social functioning; MH = mental health. ◊ = Study population; □ = Age-adjusted norms.

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Table 3 presents data on the SF-36 health survey scores of patients who had either arthritis (OA or RA), or low back pain, or both. Patients with both arthritis and low back pain had significantly lower physical health (physical component summary score = 30.99, P < 0.01) compared to arthritis only or low back pain only patients. These patients scored consistently lower than patients with either arthritis or low back pain alone on all domains of SF-36 except for role emotional and mental health. Patients with only low back pain scored lower than the patients with only arthritis on 5 of the 8 domains of SF-36, however the differences were not statistically significantly different except for the general health domain, a domain in which arthritis patients scored lower than low back pain patients.

Table 3. Comparison of Medical Outcomes Short Form (SF)-36 Health Survey Scores at baseline between arthritis patients and those with low back pain*
SF-36 Health Survey*Arthritis patients (n = 167) LSMean ± SELow back pain patients (n = 74) LSMean ± SEBoth arthritis & low back pain (n = 181) LSMean ± SEP
  • *

    Type of musculoskeletal disease not reported by 38 patients; comparison after controlling for age, gender, education, marital status, severity of pain, and comorbid conditions. LS = least squares.

  • P-value based on ANCOVA with age, gender, education, marital status, severity of pain and comorbid condition as covariates with Scheffe's test for multiple comparison.

  • Significantly different from the group with both arthritis and low back pain conditions.

  • §

    Significantly different from the group with low back pain only.

Physical functioning45.39 ± 4.0947.51 ± 4.6840.08 ± 3.680.057
Role physical35.54 ± 6.1328.88 ± 7.0227.17 ± 5.530.128
Bodily pain43.45 ± 2.6242.06 ± 3.0136.98 ± 2.360.001
General health44.64 ± 3.06§50.12 ± 3.5041.68 ± 2.75§0.007
Vitality41.51 ± 3.2341.45 ± 3.7037.51 ± 2.900.146
Social functioning62.36 ± 4.0065.04 ± 4.5959.93 ± 3.600.322
Role emotional46.23 ± 6.9934.55 ± 8.0137.33 ± 6.290.085
Mental health65.33 ± 2.9962.64 ± 3.4263.20 ± 2.690.472
Physical component summary score33.31 ± 1.5134.59 ± 1.7330.99 ± 1.360.011
Mental component summary score45.17 ± 1.7343.12 ± 1.9943.60 ± 1.570.292

Health care resource utilization of study population

In the previous 3 months, 15% of the patients had 0 physician office visits, 28% of patients had 1 physician office visit, 22% had 2 visits, 17% had 3 visits, and 18% had 4 or more visits. Approximately 12% of the patients had at least 1 visit to the emergency department, and 7% of the patients spent at least 1 night in the hospital. About 21% of the patients visited a chiropractor, and about 13% visited a physical therapist at least once in the previous 3 months (data not shown).

Medication use patterns

At baseline, patients reported taking multiple prescription and OTC medications. Table 4 lists the baseline medication use by arthritis (OA and/or RA) patients, and low back pain patients. Significant differences in medication use were observed between patients with arthritis only, low back pain only, and both arthritis and low back pain patients. Patients with only low back pain tended to use more narcotic analgesics and anti-depressants than the other 2 groups. Patients with arthritis only were taking significantly more antirheumatic drugs and corticosteroids. Patients with both low back pain and arthritis were taking more gastroprotective agents (both prescription and OTC), and sleep-inducing drugs than patients with just one of the conditions. There were significant differences in medication use between the arthritis only and low back pain only patients for narcotic analgesics, antirheumatic drugs, corticosteroids, and anti-depressants.

Table 4. Medication use by categories across disease conditions at baseline n (%)*
Medication categoriesArthritis (n = 167)Low back pain (n = 74)Both arthritis & back pain (n = 181)P
  • *

    Type of musculoskeletal disease was not reported by 38 patients.

  • P-value based on Fisher's two tailed exact test.

  • NS = not significant (P > 0.05); NSAIDs = nonsteroidal antiinflammatory drugs; GPA = gastroprotective agents; OTC = over-the-counter.

 Prescription NSAIDs136 (81)59 (80)140 (77)NS
 Over-the-counter NSAIDs42 (25)18 (24)41 (23)NS
Analgesics52 (31)26 (35)68 (38)NS
Narcotic analgesics19 (11)25 (34)38 (21)<0.001
Antirheumatic drugs31 (19)1 (1)10 (6)<0.001
Corticosteroids25 (15)3 (4)14 (8)<0.05
 Prescription GPAs34 (20)10 (14)55 (30)<0.001
 OTC GPAs12 (7)5 (7)29 (16)<0.05
Antidepressants18 (11)17 (23)38 (21)<0.05
Sleep inducers17 (10)7 (9)38 (21)<0.001
Alternative therapies15 (9)6 (8)23 (13)NS

Drug therapy problems identified by the pharmacists

At baseline, a total of 452 individual drug therapy problems (DTPs) were identified among 265 patients (57.7%). Table 5 lists the most common DTPs identified, the diseases to which these problems were related, and the actions taken by the pharmacists to address these problems. The most common DTP identified was the need for additional drug therapy (31.4%), with adverse drug reactions (18.1%) being the second most common problem. The majority of drug problems were related to osteoarthritis (38.6%), followed by low back pain (10.6%). About 73% of DTPs were acted upon at baseline, with the most common action being patient education/instruction (42.7%). The outcome of these interventions by the pharmacists could not be ascertained using the baseline data.

Table 5. Drug therapy problems
DescriptionPatients (%)
Type of problem
 Additional drug therapy needed31.4
 Adverse drug reaction18.1
 Dose too low17.4
 Wrong drug9.8
 Unnecessary treatment5.7
 Dose too high3.8
Disease related to
 Low back pain10.6
 Rheumatoid arthritis8.9
 Stomach-related problems6.7
 Cardiovascular condition5.5
Action taken
 Patient education/instruction42.7
 Over-the-counter consultation21.5
 Patient monitoring10.6
 Consult prescriber10.3

Patient's views on completing the questionnaire using touch screen technology

The majority of the patients indicated they liked the touch screen technology “very much” or “somewhat” (68% and 16%), as a means to respond to the questions, 15% indicated that they were “neutral,” and 1% “did not like it too much.” Nearly all respondents indicated the number of questions asked was “just right,” with 3% each indicating that the number of questions were either “too many” or “too few.” When asked about the time it took to answer the questions, 40% responded they liked it very much, 22% liked it somewhat, 38% were neutral, and 1% did not like it too much.

Pharmacists views on touch screen technology

The majority of pharmacists “strongly agreed” (47%) and “agreed” (47%) that immediate access to patient's survey responses helped them provide patient care. Only about 5% were “not sure” or “disagreed.” The time spent by the pharmacist to identify and resolve the drug therapy problems is shown in Figure 2. About 60% of the pharmacists spent 15 to 25 minutes directly with the patient to identify and resolve drug therapy problems. Additionally, about 79% of the pharmacists spent up to 20 minutes after the patient left resolving the drug therapy problems.

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Figure 2. Time spent by the pharmacists to resolve drug therapy problems. Mins = minutes.

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  1. Top of page
  2. Abstract
  7. Acknowledgements

In this study, we evaluated patients with musculoskeletal disorders in eastern Iowa and assessed the feasibility and benefit of collecting health outcomes information in the community pharmacy setting using touch screen computer technology. Musculoskeletal disorders such as arthritis are reported by about 40 million people in the United States (19). Patients with these disorders often require multiple drug therapies and therefore provide a good opportunity for pharmacists to monitor drug therapy to help improve patient outcomes.

The population we evaluated in this study reported significantly lower health-related quality of life (HRQoL) as compared to age-adjusted population norms. This lower HRQoL is consistent with the fact that many of these patients, despite taking medications, were reporting a moderate amount of pain. This suggests that the medication regimens these patients were taking did not fully control their disease.

Previous studies have shown that patients over the age of 65 years generally tend to use more medications than younger patients because the older patients often have more diseases and symptoms to treat (20). Studies have also shown that patients over the age of 65 in ambulatory settings use anywhere from 3 to 8 prescription and OTC medications concurrently (21–26). Taking multiple medications can lead to an increased incidence of adverse drug reactions, drug-drug interactions, decreased adherence, and decreased quality of life (20). In fact, the overall incidence of adverse drug reactions is 2 to 3 times greater in the elderly as compared to young adults (26). In comparison, patients in this study population, although slightly younger than 65, had multiple comorbidities (Table 2), and were on multiple medications to treat their musculoskeletal disorders and other comorbidities (Table 4). Thus, they are at higher risk for adverse drug reactions and decreases in quality of life; and as seen from the results, patients in our study did experience adverse events and reported significantly lower health-related quality of life (Tables 3 and 5).

Studies have shown that pharmacist interventions can improve patient outcomes. In several studies, pharmacist interventions helped achieve target therapeutic goals and enhanced adherence and persistence with medication therapy (27–29), and in other studies, pharmacist interventions improved patients' HRQoL in diseases such as hypertension, hyperlipidemia, and asthma (27, 30, 31). With the baseline health outcomes information in our study, pharmacists were able to identify drug therapy problems in approximately 60% of the population and immediately address about 75% of the problems. These interventions would be expected to impact patients' HRQoL since approximately half of the interventions were due to the need for additional therapy or to manage adverse drug reactions.

The touch screen computer technology used to capture the health outcomes information in this study was easy to use and accepted by the patients. This favorable reaction is consistent with other studies that have used touch screen technology to collect health outcomes information (16). Of course, this is based on the initial experience with the touch screen. Further analyses of the longitudinal data will indicate whether this level of satisfaction was maintained. The touch screen did provide an easy way to capture information that was instantly available to the pharmacist at the point of service. Paper-based data collection would have required the pharmacist to spend more time to evaluate the responses of the patients.

From the baseline data analysis, it is apparent that the routine capture of patient-based clinical and humanistic outcomes is feasible in community pharmacies. Also, the availability of this information at the point of service contributes to the pharmacists' ability to identify drug therapy problems. To truly demonstrate the feasibility and benefit of this program, evaluation of patient outcomes over time is necessary. Further analyses of health outcomes data gathered over time, in addition to exploring the impact of resolution of identified drug therapy problems (32), will shed light on the benefit of an ongoing program like this and determine in more detail the nature and types of interactions and interventions performed by the pharmacist.


  1. Top of page
  2. Abstract
  7. Acknowledgements

We would like to thank the following pharmacists and pharmacies for their participation: Brad McClimon, Melanie Millard, Gary Albers: A Avenue Pharmacy, Cedar Rapids, IA; Betsy Pithan, Craig Clark: Clarks East Pharmacy, Cedar Rapids, IA; Robert Keane, Jim McEnany: Fifth Avenue Pharmacy, Cedar Rapids, IA; Kenton Brown: Keasling Drug, Keokuk, IA; Marla Tonn, Carrie Foust, Sara Ziegenfuss, Andrea Melton: Liberty Pharmacy, North Liberty, IA; Cindy Crawford, Patrick Barnes: Northwest Medical Clinic, Cedar Rapids, IA; Eric Strathman, Marilyn Osterhaus, Matthew Osterhaus, Michelle Prull, Jen James, Tammy Bullock: Osterhaus Pharmacy, Maquoketa, IA; Carl Chalstrom: Pharmacy Care Centre, Anamosa, IA; David Streng, John Mulert, Joshua Feldman, Kathy Macek, Melissa Jarding, Laura Fitzpatrick, Brenda Thies: Ruegnitz Drug #3, Dubuque, IA; Susan Phillips, Diane Marsh, Deniss Bousselot, Michelle Kasterke, Jolene Henning: Scott Drug, De Witt, IA; Alan Shepley, Becky Reinhart, Amy Koering, Ruth Clark: Shepley Pharmacy, Mount Vernon, IA; Christine Johnson, Bernie Cremers, William Haigh: Towncrest Pharmacy, Iowa City, IA.

We thank Patty Kumbera, RPh of Outcomes Pharmaceutical Care Network for her assistance in coordinating this research project.


  1. Top of page
  2. Abstract
  7. Acknowledgements
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