Background
Transitions across care settings are a critical time to manage medications [
1‐
6]. Up to two-thirds of individuals have unintended medication discrepancies at admission, and medication changes made during the hospitalization are not always conveyed to primary care providers following discharge. Rehospitalization is particularly important today as Medicare policy now reduces payments to health systems for specified conditions where rates do not meet target goals. Thus, improving care during transitions is critical. With medications as one important aspect of care coordination, involving pharmacists in these processes may be helpful [
7‐
10].
Medication reconciliation has been advocated to reduce medication discrepancies [
6‐
8,
11], and is defined as the development of a medication list that is as accurate as possible, which is compared at admission, transfer or discharge, to help ensure correct medications at transitions [
11]. However, up-to-date discharge medication lists do not ensure the correct medications are obtained and used. In fact, two recent reviews examined the impact of medication reconciliation [
7,
8] and stated that it alone was not likely to improve post-discharge utilization, yet it reduced medication discrepancies, potential adverse drug events and adverse drug events [
10,
11]. Hesselink et al. noted that about half the studies showed improvements in adverse events or healthcare utilization post-discharge and noted the difficulty in comparing interventions across studies because of their complexity, lack of detail, varying outcomes and variability in study execution [
9]. Thus, numerous well-designed studies have been reported, but the evidence is not consistent, and further studies are needed to examine how discharge processes can be improved to achieve optimal outcomes.
We previously reported the methods for the Iowa Continuity of Care Study [
12]. The aim of the Iowa Continuity of Care Study was to determine if a pharmacist case manager (PCM) providing a faxed discharge medication care plan from a tertiary care institution to primary care could improve medication appropriateness and reduce adverse events, rehospitalization and emergency department visits. The major goal was to improve communication links between the tertiary hospital, the primary care physician and community pharmacists, and these providers typically received communication from the institution by mail (primary care physician) or not at all (community pharmacists). The hypotheses were: (1) Medication appropriateness will be improved in patients receiving care from PCM versus usual care; (2) Adverse drug events (ADE) will be lower post-discharge in patients receiving care from PCM versus usual care and (3) Number of readmission, emergency department visits or unscheduled office visits will be lower in patients receiving care from PCM versus usual care.
Results
We enrolled 945 participants into the study (Figure
2), and study groups were comparable at baseline (Table
1). The mean age (±SD) of participants in the study was 61.0 (±12.2) years, with 91% white and 66% married or living as married. The income and education distributions showed that 47% of study participants had an annual income less than $40,000 and 49% had a high school education, respectively. Most participants in the study had health insurance, with almost half having private insurance. Almost all, 96%, had prescription drug insurance.
Table 1
Participant demographic and clinical characteristics
Age
| | | | |
≤44 years | 34 (10.9) | 38 (12.2) | 29 (9.3) | 101 (10.8) |
45–54 years | 43 (13.8) | 52 (16.7) | 49 (15.7) | 144 (15.4) |
55–64 years | 101 (32.5) | 96 (30.8) | 110 (35.1) | 307 (32.8) |
65–74 years | 85 (27.3) | 85 (27.2) | 85 (27.2) | 255 (27.2) |
≥75 years | 48 (15.4) | 41 (13.1) | 40 (12.8) | 129 (13.8) |
Education
| | | | |
Less than high school (1–8) | 16 (5.1) | 12 (3.9) | 22 (7.0) | 50 (5.3) |
High school (9–12) | 151 (48.6) | 153 (49.0) | 151 (48.2) | 455 (48.6) |
Some college | 66 (21.2) | 73 (23.4) | 59 (18.9) | 198 (21.2) |
College degree | 45 (14.5) | 41 (13.1) | 54 (17.3) | 140 (15.0) |
Professional or advanced degree | 33 (10.6) | 33 (10.6) | 27 (8.6) | 93 (9.9) |
Race
| | | | |
White | 287 (92.3) | 283 (90.7) | 285 (91.1) | 855 (91.4) |
African American | 12 (3.9) | 16 (5.1) | 18 (5.8) | 46 (4.9) |
Hispanic | 7 (2.3) | 8 (2.6) | 6 (1.9) | 21 (2.2) |
Other | 5 (1.6) | 5 (1.6) | 4 (1.3) | 14 (1.5) |
Annual income
| | | | |
<$10,000 | 32 (10.4) | 34 (11.0) | 35 (11.2) | 101 (10.9) |
$10,000 - $24,999 | 55 (17.8) | 56 (18.1) | 64 (20.5) | 175 (18.8) |
$25,000 - $39,999 | 68 (22.0) | 53 (17.1) | 42 (13.5) | 163 (17.5) |
$40,000 - $54,999 | 56 (18.1) | 46 (14.8) | 42 (13.5) | 144 (15.5) |
$55,000 and greater | 98 (31.7) | 121 (39.0) | 129 (41.4) | 348 (37.4) |
Missing | 2 | 2 | 1 | 5 |
Marital status
| | | | |
Single/never married | 21 (6.8) | 18 (5.8) | 29 (9.3) | 68 (7.3) |
Married or living as married | 194 (62.6) | 213 (68.3) | 206 (65.8) | 613 (65.6) |
Divorced/separated | 56 (18.1) | 49 (15.7) | 51 (16.3) | 156 (16.7) |
Widowed | 39 (12.6) | 32 (10.3) | 27 (8.6) | 98 (10.5) |
Medical service
| | | | |
Internal Medicine/Family Medicine | 79 (25.4) | 84 (26.9) | 84 (26.8) | 247 (26.4) |
Cardiology | 111 (35.7) | 111 (35.6) | 112 (35.8) | 334 (35.7) |
Orthopedics | 121 (38.9) | 117 (37.5) | 117 (37.4) | 355 (37.9) |
Type of medical insurance
| | | | |
Private | 157 (50.5) | 145 (46.5) | 158 (50.5) | 460 (49.2) |
Medicare | 120 (38.6) | 123 (39.2) | 114 (36.4) | 357 (38.1) |
Medicaid | 27 (8.7) | 39 (12.5) | 38 (12.1) | 104 (11.1) |
Other insurer/none/self-pay | 7 (2.3) | 5 (1.6) | 3 (1.0) | 15 (1.6) |
Prescription drug insurance
| | | | |
Yes | 297 (95.5) | 294 (94.5) | 303 (97.1) | 894 (95.7) |
Average number of prescription medications*
| 11.0 (5.7) | 11.8 (6.0) | 10.4 (5.5) | 11.0 (5.8) |
Chronic conditions (% yes)
| | | | |
Hypertension | 249 (80.1) | 231 (74.0) | 239 (76.4) | 719 (76.8) |
Hyperlipidemia | 194 (62.4) | 195 (62.5) | 189 (60.3) | 578 (61.8) |
Heart failure | 85 (27.3) | 91 (29.2) | 76 (24.3) | 252 (26.9) |
Coronary artery disease | 114 (36.7) | 108 (34.6) | 94 (30.0) | 316 (33.8) |
Myocardial infarction | 69 (22.2) | 68 (21.8) | 62 (19.8) | 199 (21.2) |
Transient ischemic attacks | 26 (8.4) | 30 (9.6) | 26 (8.3) | 82 (8.8) |
Stroke | 12 (3.9) | 17 (5.5) | 14 (4.5) | 43 (4.6) |
Depression | 105 (33.8) | 105 (33.7) | 98 (31.3) | 308 (32.9) |
Anxiety | 91 (29.3) | 79 (25.3) | 73 (23.3) | 243 (26.0) |
Arthritis | 193 (62.1) | 197 (63.1) | 192 (61.3) | 582 (62.2) |
Diabetes | 113 (36.3) | 123 (39.4) | 114 (36.4) | 350 (37.4) |
Kidney disease* | 35 (11.3) | 58 (18.6) | 34 (10.9) | 127 (13.6) |
Liver disease | 15 (4.8) | 17 (5.5) | 14 (4.5) | 46 (4.9) |
Asthma or pulmonary disease | 79 (25.4) | 90 (28.9) | 86 (27.5) | 255 (27.2) |
Knee replacement | 82 (26.4) | 70 (22.4) | 78 (24.9) | 230 (24.6) |
Hip replacement | 49 (15.8) | 36 (11.5) | 32 (10.2) | 117 (12.5) |
Fracture | 135 (43.4) | 133 (42.6) | 131 (41.9) | 399 (42.6) |
Cancer | 56 (18.0) | 54 (17.3) | 49 (15.7) | 159 (17.0) |
Other | 172 (55.3) | 183 (58.7) | 171 (54.6) | 526 (56.2) |
Smoking status
| | | | |
Never | 132 (42.4) | 150 (48.1) | 126 (40.3) | 408 (43.6) |
Current | 33 (10.6) | 35 (11.2) | 27 (8.6) | 95 (10.2) |
Ex-smoker | 146 (47.0) | 127 (40.7) | 160 (51.1) | 433 (46.3) |
Alcohol intake
| | | | |
None | 200 (64.3) | 190 (60.9) | 183 (58.7) | 573 (61.3) |
<1 drink/day | 82 (26.4) | 95 (30.5) | 100 (32.1) | 277 (29.6) |
1–2 drinks/day | 22 (7.1) | 21 (6.7) | 24 (7.7) | 67 (7.2) |
≥3 drinks/day | 7 (2.3) | 6 (1.9) | 5 (1.6) | 18 (1.9) |
Self-rated health
| | | | |
Excellent | 15 (4.8) | 13 (4.2) | 19 (6.1) | 47 (5.0) |
Very good | 55 (17.7) | 44 (14.1) | 48 (15.3) | 147 (15.7) |
Good | 116 (37.4) | 122 (39.1) | 132 (42.2) | 370 (39.6) |
Fair | 88 (28.4) | 87 (27.9) | 79 (25.2) | 254 (27.2) |
Poor | 36 (11.6) | 46 (14.7) | 35 (11.2) | 117 (12.5) |
Self-reported medication adherence
|
Forget (% never or rarely)* | 233 (75.2) | 245 (79.0) | 267 (85.6) | 745 (79.9) |
Careless (% never or rarely) | 282 (91.0) | 289 (93.2) | 299 (95.8) | 870 (93.4) |
Stop if feel better (% never or rarely) | 292 (94.2) | 292 (94.2) | 302 (96.8) | 886 (95.1) |
Stop if feel worse (% never or rarely) | 278 (89.7) | 278 (89.7) | 286 (91.7) | 842 (90.3) |
Medication management ability
|
Able (% scoring 5) | 203 (66.3) | 218 (70.1) | 211 (68.5) | 632 (68.3) |
Some limitation (% 4 or less) | 103 (33.7) | 93 (29.9) | 97 (31.5) | 293 (68.3) |
Missing | 5 | 1 | 5 | 11 |
Medication self-efficacy scale
| | | | |
Average (standard deviation) | 123.3 (±12.5) | 124.4 (±12.3) | 125.0 (±10.1) | 124.2 (±11.7) |
Instrumental activities of daily living
|
No limitation | 215 (69.4) | 226 (72.4) | 239 (76.4) | 680 (72.7) |
Requires help or unable on 1 | 35 (11.3) | 42 (13.5) | 27 (8.6) | 104 (6.0) |
Requires help or unable on 2 | 22 (7.1) | 13 (4.2) | 21 (6.7) | 56 (6.0) |
Requires help or unable on 3 or more | 38 (12.2) | 31 (9.9) | 26 (8.3) | 95 (10.1) |
Participants were similar in terms of chronic conditions, smoking status and alcohol intake at baseline, though baseline medications were slightly higher in the minimal intervention group compared to controls (p = 0.0009) (Table
1). The prevalence of chronic kidney disease and reporting never or rarely forgetting to take medications did vary by study group. More participants in the control group (85.6%) rarely forgot their medications compared to minimal (79.0%) and enhanced (75.2%) (p < 0.0046). Self-rated health was comparable across the study groups, and 13% rated their health as poor, 27% as fair, 40% as good and 21% were very good or excellent.
Intervention fidelity was high for admission medication reconciliation and wallet card, but was variable for other parts of the intervention (Table
2). Discharge counseling was provided to 75% of enhanced and minimal intervention participants. Among the enhanced intervention group, 84% had their care plan faxed to their community physicians and 80% had it faxed to community pharmacists. Five pharmacists delivered the PCM activities over the study period, and their average time spent on study activities varied from 83 (±26) to 202 (±112) minutes per patient per pharmacist (p < 0.0001). As expected, pharmacists spent considerably more time with enhanced versus minimal participants (p < 0.0001).
Table 2
Fidelity of pharmacists’ interventions
Admission medication reconciliation | 311 (100%) | 312 (100%) | |
Community pharmacy contacted | 300 (96.5%) | 305(97.8%) | 0.34 |
Discharge counseling completed | 235 (75.6%) | 235 (75.3%) | 0.94 |
Wallet card completed | 309 (99.4%) | 308 (98.7%) | 0.41 |
Medication issues identified in hospital | 275 (88.4%) | 249 (79.8%) | 0.003 |
Post-discharge phone call completed | 301 (96.8%) | 4 (1.3%)* | |
Discharge care plan faxed to community physician | 267 (85.9%) | 1 (0.3%)* | |
Discharge care plan faxed to community pharmacist | 246 (79.1%) | 1 (0.3%)* | |
Discharge care plan included medication recommendations to community physician | 207 (66.6%) | NA | |
Discharge care plan medication issues identified by pharmacists† | To Hospital & Community Physicians | To Hospital Physicians | |
Mean (±SD) | 6.6 (±6.8) | 3.2 (±4.0) | |
Total number of issues identified | 2063 | 1012 | |
Dosing or administration | 260 | 131 | |
Indication | 754 | 363 | |
Efficacy | 319 | 101 | |
Cost | 103 | 38 | |
Risk to patient | 627 | 379 | |
Discharge care plan recommendations made to physicians† | To Hospital & Community Physicians | To Hospital Physicians | |
Mean (±SD) | 7.1 (±6.6) | 3.5 (±3.8) | |
Total number of recommendations | 2220 | 1077 | |
Discontinue medications | 377 | 195 | |
Add medications | 566 | 256 | |
Change medications | 361 | 151 | |
Disease monitoring | 280 | 56 | |
Follow-up patient | 262 | 134 | |
Patient education | 283 | 239 | |
Adherence education | 91 | 46 | |
Time pharmacist spent on each patient (minutes) | 210.0 (±93.0) | 118.5 (±58.6) | <.0001 |
The average MAI per medication as 0.53 at discharge and increased to 0.75 at 90 days, and this was true across all study groups (Table
3). There were no statistically significant differences in MAI supporting the intervention. The control group had a statistically significantly lower (improved) total MAI at discharge compared to minimal and enhanced groups, but the average MAI per medication was not different.
Table 3
Medication Appropriateness Index (MAI) by study group
Summed MAI per Participant
|
Discharge | 7.1 (±7.0) | 8.0 (±8.4) | 6.1 (±6.6) | E + M vs. C: 0.03 |
30 days post-discharge | 10.1 (±8.9) | 11.7 (±11.2) | 9.6 (±9.5) | E vs. C: 0.78 |
M vs. C: 0.07 |
90 days post-discharge | 11.6 (±10.5) | 13.6 (±12.3) | 11.1 (±11.3) | E vs. C: 0.94 |
M vs. C: 0.02 |
MAI per Medication
| | | | |
Discharge | 0.52 (±0.53) | 0.55 (±0.57) | 0.51 (±0.54) | E + M vs. C: 0.26 |
30 days post-discharge | 0.62 (±0.50) | 0.69 (±0.61) | 0.65 (±0.57) | E vs. C: 0.86 |
M vs. C: 0.70 |
90 days post-discharge | 0.72 (±0.68) | 0.80 (±0.65) | 0.73 (±0.63) | E vs. C: 0.84 |
| | | | M vs. C: 0.33 |
Post-discharge, about 16% of all participants experienced an AE, and this did not differ by study group (p > 0.05) (Table
4). The enhanced (6.1%) and minimal (6.1%) groups had more AE and ADEs during hospitalization identified than controls (4.5%), but the difference was not statistically significant.
Table 4
Adverse Events (AE) from non-adherence and under-treatment and Adverse Drug Events (ADE) by study group
Initial Hospitalization
| n = 311 | n = 312 | n = 313 | |
Number of possible adverse events | 40 | 43 | 40 | |
Number of AEs due to medication non-adherence† | 9 | 9 | 6 | |
Number of AEs due to medication under-treatment† | 1 | 3 | 3 | |
Number of ADEs† | 15 | 16 | 15 | |
Percent of participants with any AE or ADE† | 18 (5.8%) | 19 (6.1%) | 22 (7.0%) | E + M vs. C: 0.52 |
During Hospitalization
| | | | |
Number of possible adverse events | 55 | 52 | 57 | |
Number of AEs due to medication non-adherence † | 1 | 2 | 0 | |
Number of preventable medication non-adherence AEs‡ | 1 | 2 | 0 | |
Number of AEs due to medication under-treatment † | 2 | 0 | 1 | |
Number of preventable medication under-treatment AEs‡ | 2 | 0 | 1 | |
Number of ADEs† | 18 | 27 | 16 | |
Number of preventable ADEs‡ | 0 | 3 | 1 | |
Percent of participants with any AE or ADE† | 19 (6.1%) | 19 (6.1%) | 14 (4.5%) | E + M vs. C: 0.31 |
Post-Discharge
| n = 306 | n = 309 | n = 311 | |
Number of possible adverse events | 183 | 180 | 174 | |
Number of AEs due to medication non-adherence† | 15 | 8 | 10 | |
Number of preventable medication non-adherence AEs‡ | 15 | 7 | 9 | |
Number of AEs due to medication under-treatment† | 10 | 13 | 6 | |
Number of preventable medication under-treatment AEs‡ | 6 | 11 | 5 | |
Number of ADEs† | 47 | 49 | 60 | |
Number of preventable ADEs‡ | 8 | 7 | 9 | |
Percent of participants with any AE or ADE† | 48 (15.7%) | 50 (16.2%) | 53 (17.0%) | E vs. C: 0.72 |
| | | | M vs. C: 0.95 |
About 29% of all participants had any type of healthcare utilization within 30 days post-discharge, where 15% of all participants had a 30-day readmission. There were no statistically significant differences by study group for any utilization outcomes (Table
5). We examined the study participants with CHF and/or asthma/COPD separately, and the results were consistent with our overall findings (data not shown).
Table 5
Hospital readmission, emergency department visits or unscheduled office visits by study group
Number of patients with any post-discharge healthcare use
|
30 days | 81 (28.8%) | 88 (29.5%) | 87 (29.6%) | E vs. C: 0.82 |
M vs C: 0.92 |
90 days | 97 (34.5%) | 90 (30.5%) | 88 (29.9%) | E vs. C: 0.20 |
M vs C: 0.62 |
Number of patients with at least one specific post-discharge healthcare use
|
30 day readmission | 47(16.7%) | 40 (13.4%) | 43 (14.6%) | E vs. C: 0.29 |
M vs C: 0.38 |
30 day emergency dept visit | 38 (13.5%) | 49 (16.5%) | 52 (17.8%) | E vs. C: 0.18 |
M vs C: 0.71 |
30 day unscheduled visit | 31 (11.0%) | 30 (10.1%) | 32 (10.9%) | E vs. C: 0.81 |
M vs C: 0.69 |
90 day readmission | 49 (17.4%) | 51 (17.3%) | 47 (16.0%) | E vs. C: 0.77 |
M vs C: 0.83 |
90 day emergency dept visit | 41 (14.6%) | 40 (13.6%) | 46 (15.7%) | E vs. C: 0.99 |
M vs C: 0.54 |
90 day unscheduled visit | 42 (15.0%) | 36 (12.2%) | 33 (11.3%) | E vs. C: 0.18 |
| | | | M vs C: 0.74 |
Discussion
The pharmacist case manager did not affect medication appropriateness, number of clinically-relevant adverse events or adverse drug events or post-discharge healthcare utilization. These findings might be explained by the overall good medication appropriateness and generally low re-hospitalizations, ED visits and unscheduled office visits in all study groups when compared to previous studies. Other studies that have evaluated PCMs had mixed results, but our methodology and measures were anticipated to be sensitive to the intervention given previous findings about pharmacists’ impact post-discharge [
10]. Our finding is important given our strong study design, subjects with high medication use and a comprehensive assessment of all study outcomes.
Our primary outcome was medication appropriateness and we had good power to detect meaningful differences based on prior studies (17). Our study was powered to detect effect sizes of 0.22 in the MAI, but the largest effect size seen was 0.21 for minimal vs. control at 90 days, and this was in the wrong direction (minimal having a higher value). Moreover, the MAI per medication ranged from 0.51 to 0.8, indicating high medication appropriateness across the study. These finding suggest very good medication appropriateness with limited opportunity for improvement regardless of the intervention.
Our process to determine adverse events and adverse drug events was comprehensive using patient self-report of symptomatology along with patient medical records and expert opinion to establish attribution [
18‐
20]. Forster and colleagues evaluated ADEs after discharge [
21]. Of 400 patients, 45 (11%) had an ADE and over half could have been prevented or ameliorated. These ADEs occurred in spite of electronic discharge summaries being transmitted to primary care physicians. In our study, about 16% of all participants experienced an adverse event within the 90-day post-discharge period that was virtually certain or highly likely attributed to medications. While an interim analysis showed that medication discrepancies were reduced in the enhanced group, and the medication list in the primary care physician office was more likely to be up-to-date compared to the minimal or control groups [
22], this finding did not translate into other findings.
Between 2008 and 2010 the readmission at the study hospital were 18.2 and 19.1%, respectively, when our study was conducted [
23]. However, the 30-day readmission rate among study participants ranged from 13.4% to 16.7%, which is lower than anticipated when the study was designed. These observations would suggest that the rate of re-hospitalizations had dropped substantially after the study was designed which made it difficult for the intervention to improve these rates. There is conflicting evidence from other studies whether 30-day readmission rates were declining overall during our study period 2007–2012. Findings in the Veterans Affairs institutions suggest a decreasing trend over the past decade, but rates were steady for Medicare fee-for-service beneficiaries from 2007 through 2011, until 2012 when rates declined [
23‐
25]. Focusing this intervention exclusively on individuals with high risk for readmission post-discharge would have been a better strategy, yet our sub-analysis for heart failure showed no differences. Finally, a broader view of health and functional status besides medications is likely necessary to further reduce readmissions. For example, medications are one component of the Care Transitions Intervention, but it is not the solitary focus [
26].
The content of the PCM intervention was multi-faceted not merely medication reconciliation. There was some variability in the delivery of the intervention by the PCMs, but almost all participants were provided a medication reconciliation at admission and discharged medication lists. The PCM was expected to improve medication use at discharge via medication reconciliation and recommendations to the inpatient team. During study initiation, policy changed at the study hospital and many usual care patients received admission medication reconciliation. While the PCM contacted the community pharmacist for almost all participants at baseline in the minimal and enhanced groups, we cannot establish the effect of this call. As well, the discharge care plan was faxed to 86% of participants’ community physicians, and only 66% contained specific medication recommendations. These two facets of the intervention may have reduced its effectiveness.
Numerous studies have shown a positive impact of pharmacists’ recommendations when they work directly with teams [
27‐
34]. In considering the lack of effect at discharge, the PCMs were study pharmacists and not part of the inpatient medical teams. Our interim analysis showed that about half of the pharmacists’ recommendations were accepted by hospitalists or physicians in the in-patient setting [
35] and the recommendations did not change the prescribing of cardiovascular medications [
36]. Anecdotally, inpatient physicians often were reluctant to accept recommendations for chronic conditions, e.g. hyperlipidemia or diabetes, when these were not the reason for the hospitalization. These findings were disappointing, and the low rate of accepted recommendations is counter to pharmacists’ contribution to improved outcomes in both inpatient and primary care settings [
27‐
34].
The medication care plan and the 3–5 day post-discharge telephone call were expected to have an impact post-discharge. About 86% of participants had the discharge care plan faxed to physicians and almost all participants in the enhanced intervention group received the post-discharge telephone follow-up. While over 2000 recommendations were in the care plans, about one-half of these were provided to community physicians. Many of the recommendations were related to adding, changing or discontinuing medications. Physicians in the community may have not been aware of the hospitalization until a follow-up visit was done post-discharge. At that time, a discharge summary and/or the medication care plan could have been reviewed, but we have no conclusive way to know whether that occurred. There was no verbal hand-off back to the community providers. The development of a discharge medication care plan and sharing the care plan with physicians via fax were insufficient to improve medication use or patient outcomes. A verbal hand-off to physicians was effective in two previous studies, suggesting this contact may be critical [
37,
38]. However, the best time to actually complete the verbal hand-off to the primary care physician is unclear, as the timing would likely be best when the provider is scheduled to see the discharged patient. Further, the telephone follow-up, although provided to almost all intended participants at 3–5 days post-discharge, was not sufficient to improve outcomes for this population.
Finally, the study population was younger than many other studies where care transition programs have been effective [
9,
26]. Many of the individuals may not have had a high enough acuity to require such support during their transition, as about 70% had no instrumental activity of daily living limitation, only 40% rated their health as fair or poor, few reported intentional medication non-adherence, 25% reported forgetting medications and most participants had high medication self-efficacy. In retrospect, we should have focused this care transition intervention towards individuals with transition issues rather than those where pharmacists had been successful impacting outcomes in previous studies. In consideration of other studies and our current findings, we suggest that medication-focused care transition activities be targeted to individuals with greater health acuity who are known to exhibit medication management difficulties. In addition, implementation of medication-related recommendations will require more intense interventions than fax from the tertiary hospital with the primary care physicians after discharge.
This study has implications for policies related to reducing hospital readmissions. Hospitals will need to evaluate the most efficient and effective methods to reduce re-admissions and reduce adverse events. Improved information technology may be useful in transmitting discharge care plan information more efficiently, but it appears likely that a verbal or electronically verified hand-off may be necessary [
37,
38]. Such an approach would help ensure that important issues requiring follow-up were specifically identified for community providers. However, the most effective strategy may be stronger relationships and communications with care navigators or primary care physicians to optimize medications since it is hospitals that are at risk for re-admissions.
This study has limitations. At baseline, forgetting medications was not well randomized. Yet, it is unlikely that this single aspect of medication management would change the impact of the intervention on medication appropriateness or adverse events to a great degree across the three study groups. The intervention fidelity was good but not without some issues. We cannot separate the effect of any specific component of the intervention such as patient counseling on the outcomes of the study. We failed to determine whether community physicians actually used the discharge medication care plan information that was sent, and this is an important missing link in the process of care and in this study. As well, factors such as pharmacist personalities and health-system relationships with non-health system primary care providers form an important context in which such studies are completed and these cannot be ignored when considering generalizability. We did not separate self-reported versus medically document post-discharge events, which may contribute to measurement error making it more difficult to detect an effect.
Acknowledgements
Contributors. NA.
Funders. This study was supported by the National Heart, Lung, and Blood Institute (1RO1 HL082711). Drs. Carter, Kaboli and Christensen are also supported by the Comprehensive Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (HFP 04–149). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.
Prior presentations. NA.
Drs. Farris and Carter had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Competing interests
All of the authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Karen Farris
• Consultant for £1000 to London School of Economics and Political Sciences about the role of pharmacists in the United States
• Honoraria and travel expenses from Eli Lilly and Company in 2010 for presentation and article about pharmacists in care transitions
Barry Carter
• Consultant for Pro-Vice Chancellor for Research, Queens University, Centre on Health Improvement, Belfast, Northern Ireland, May 2010 about pharmacists’ roles.
The other authors no had conflicts to declare.
Authors’ contributions
KF, BC, DW, PK, PJ, AC and JB were involved in the design and execution of the study. KF and CS were responsible for the day-to-day activities. YX was responsible for data management and YX and JD conducted the statistical analyses. BC, DW, PK and PJ assessed the adverse drug events. All authors reviewed the drafts of the manuscripts and approved the final version.