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Improving admission medication reconciliation with pharmacists or pharmacy technicians in the emergency department: a randomised controlled trial
  1. Joshua M Pevnick1,2,
  2. Caroline Nguyen3,
  3. Cynthia A Jackevicius4,5,6,7,8,
  4. Katherine A Palmer3,
  5. Rita Shane3,
  6. Galen Cook-Wiens9,
  7. Andre Rogatko9,
  8. Mackenzie Bear3,
  9. Olga Rosen3,
  10. David Seki3,
  11. Brian Doyle10,
  12. Anish Desai1,
  13. Douglas S Bell10
  1. 1 Department of Medicine, Division of General Internal Medicine, Cedars-Sinai Health System, Los Angeles, California, USA
  2. 2 Department of Biomedical Sciences, Division of Informatics, Cedars-Sinai Medical Center, Los Angeles, California, USA
  3. 3 Department of Pharmacy Services, Cedars-Sinai Medical Center, Los Angeles, California, USA
  4. 4 Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, California, USA
  5. 5 Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
  6. 6 Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
  7. 7 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
  8. 8 University Health Network, Toronto, Ontario, Canada
  9. 9 Department of Biomedical Sciences, Biostatistics and Bioinformatics Research Center, Cedars-Sinai Health System, Los Angeles, California, USA
  10. 10 General Internal Medicine and Health Services Research, UCLA, Los Angeles, California, USA
  1. Correspondence to Dr Joshua M Pevnick, Department of Medicine, Division of General Internal Medicine, Cedars-Sinai Health System, 8700 Beverly Blvd, B113, Los Angeles, CA 90048, USA; Joshua.Pevnick{at}cshs.org

Abstract

Background Admission medication history (AMH) errors frequently cause medication order errors and patient harm.

Objective To quantify AMH error reduction achieved when pharmacy staff obtain AMHs before admission medication orders (AMO) are placed.

Methods This was a three-arm randomised controlled trial of 306 inpatients. In one intervention arm, pharmacists, and in the second intervention arm, pharmacy technicians, obtained initial AMHs prior to admission. They obtained and reconciled medication information from multiple sources. All arms, including the control arm, received usual AMH care, which included variation in several common processes. The primary outcome was severity-weighted mean AMH error score. To detect AMH errors, all patients received reference standard AMHs, which were compared with intervention and control group AMHs. AMH errors and resultant AMO errors were independently identified and rated by ≥2 investigators as significant, serious or life threatening. Each error was assigned 1, 4 or 9 points, respectively, to calculate severity-weighted AMH and AMO error scores for each patient.

Results Patient characteristics were similar across arms (mean±SD age 72±16 years, number of medications 15±7). Analysis was limited to 278 patients (91%) with reference standard AMHs. Mean±SD AMH errors per patient in the usual care, pharmacist and technician arms were 8.0±5.6, 1.4±1.9 and 1.5±2.1, respectively (p<0.0001). Mean±SD severity-weighted AMH error scores were 23.0±16.1, 4.1±6.8 and 4.1±7.0 per patient, respectively (p<0.0001). These AMH errors led to a mean±SD of 3.2±2.9, 0.6±1.1 and 0.6±1.1 AMO errors per patient, and mean severity-weighted AMO error scores of 6.9±7.2, 1.5±2.9 and 1.2±2.5 per patient, respectively (both p<0.0001).

Conclusions Pharmacists and technicians reduced AMH errors and resultant AMO errors by over 80%. Future research should examine other sites and patient-centred outcomes.

Trial registration number NCT02026453.

  • healthcare quality improvement
  • health services research
  • human error
  • medication reconciliation
  • pharmacists

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Introduction

Bates et al defined an adverse drug event (ADE) as an ‘injury resulting from medical intervention related to a drug’.1 The Institute of Medicine estimates that hospitalised US patients suffer from 400 000 preventable ADEs annually.2 Among the most frequent causes of preventable ADEs are errors in admission medication histories (AMH).3–5

Using pharmacists to check AMHs reduces preventable ADEs.6 Nonetheless, many organisations have encountered difficulties in disseminating pharmacist-led medication reconciliation interventions. We have previously attributed poor uptake of such interventions to the complexity of implementing medication reconciliation interventions, which affect multiple interacting workflows, and to the cost of employing pharmacists.7

To address both implementation complexity and cost, we modified this intervention by stationing pharmacists in the emergency department (ED) to obtain AMHs before admitting physicians place admission medication orders (AMO). This allows admitting physicians to work from an accurate AMH, which is especially important in an era when electronic health records (EHR) allow physicians to convert AMHs into AMOs with just a few mouse clicks, and when patients are often admitted by hospitalists unfamiliar with patients’ home medication regimens.

To quantify the reduction in AMH errors achieved by pharmacists and pharmacist-supervised pharmacy technicians (PSPT) obtaining AMHs in the ED, we conducted a three-arm randomised controlled trial comparing these providers with usual care processes in a population of medically complex patients. To better understand the effect on more downstream outcomes, including preventable ADEs occurring in the hospital and after discharge, we also compared rates of AMO errors resulting from AMH errors.

Methods

Trial design overview

We conducted a three-arm randomised controlled trial. Intervention arms used pharmacists or PSPTs to obtain AMHs before AMOs were placed. The Cedars-Sinai Medical Center (CSMC) Institutional Review Board agreed that informed consent of patients should be waived in this randomised allocation of services that had heretofore been allocated via operational convenience.

Setting and study population

CSMC is a large university-affiliated hospital. Providers placing orders for trial patients included community, hospitalist, and resident physicians, as well as nurse practitioners and physician assistants. Pharmacists included licensed resident pharmacists.

Eligible participants were medically complex patients admitted to CSMC through the ED. Enrolment screening occurred Mondays through Thursdays from approximately 11:00 to 20:00 beginning 7 January 2014 through 14 February 2014. Enrolment ceased at the end of the first day on which the intended sample size was exceeded. Screening was occasionally paused when pharmacy staff were otherwise occupied with clinical or research duties. Inclusion criteria were: ≥10 active chronic prescription medications in the EHR, history of acute myocardial infarction or congestive heart failure in the EHR problem list, admission from a skilled nursing facility (SNF), history of transplant, or active anticoagulant, insulin or narrow therapeutic index medications (online supplementary appendix). Patients were excluded if they had previously been enrolled in the study, or if admitted to paediatric or trauma services or transplant services with pharmacists.

Supplementary file 2

Randomisation

Investigators reviewed the EHR to identify ED patients for whom providers had already placed an admission order. Upon identifying trial candidates, investigators reviewed inclusion/exclusion criteria. After enrolling patients meeting criteria, investigators used RANDI2 randomisation software to randomise each patient.8 Each block of six consecutively enrolled patients was allocated in a 2:2:2 distribution across the three study arms (figure 1). Patients who left the ED before an AMH could be obtained and patients not ultimately admitted (despite an initial decision to admit) were considered lost to follow-up. Because the number of patients assessed for eligibility on 30 January 2014 was lost, we substituted the mean assessed patient count using all other enrolment days.

Figure 1

Workflow diagram of admission medication history (AMH) processes occurring during usual care and study randomisation. Common usual care process variations italicised and circumscribed by dotted lines.

Interventions

Patients were randomly allocated to usual care or to one of two intervention arms in which either a pharmacist or a PSPT had primary responsibility for obtaining the AMH. Obtaining the initial AMH usually began with reviewing the medication regimen present in the EHR if one was available from a prior encounter. Next, patients, families and caregivers present in the ED were interviewed. Pill bottles, medication lists and SNF medication administration records were also reviewed. In cases where sources matched convincingly, no further efforts were undertaken. However, in most cases, other sources including family, pharmacies and/or providers were contacted until questions were resolved. This is consistent with a published protocol for obtaining a ‘best possible medication history’.4 Pharmacists and PSPTs attempted to complete all intervention-arm AMHs soon after the ED decision to admit was made and before any AMOs were placed, such that the workflow of admitting physicians would not be affected, and that there would be no need to contact and convince admitting physicians to fix AMHs or AMOs retroactively.

PSPTs presented their AMHs to a supervising pharmacist to allow the pharmacist to decide whether data sources needed further review, or whether the AMH was ready to be entered into the EHR. Requiring pharmacists to enter PSPTs’ AMHs into the EHR ensured that pharmacists reviewed all medications in the AMH, and constituted the pharmacist supervision of PSPTs.

Didactic and experiential training of pharmacists and pharmacy technicians

All pharmacists and pharmacy technicians underwent standardised training in obtaining AMHs. Didactic training generally took 8–16 hours and included: review of background publications; review of locally created general and ED-specific medication reconciliation manuals with detailed guides of AMH workflows, the patient interview and EHR utilisation; and a didactic training evaluation. Experiential training included observing >5 AMHs obtained by an expert pharmacist, followed by the trainee obtaining >5 AMHs under the proctoring of an expert pharmacist. Training continued until proctors deemed trainees competent.

Usual care

All arms received usual care for patients admitted from the ED, which commonly involves multiple process variations. EHR-derived medication regimen accuracy is subject to variation in the knowledge and efforts of prior providers, which are often driven by patient acuity and patient care priorities. Patients and caregivers’ recall of medication regimens varies over time and across patients. Nurse and physician contributions likely vary in accordance with their pharmacological training and with competing obligations, including patients’ requests for home medications. Finally, physicians may place AMOs before or after patients have had their AMH obtained by an inpatient nurse (dotted lines and italicised text highlight common process variations in figure 1). To minimise unnecessary overlap, inpatient pharmacists and nurses were advised not to initiate new efforts to improve upon pharmacist-approved AMHs. However, they were able to address any concerning AMH or AMO data that arose during clinical care.

Outcome measurement

Reference standard AMHs

As per prior studies, we attempted to obtain reference standard AMHs from patients in all arms on the day following admission.4 When a reference standard AMH was not obtained, patients were considered lost to follow-up. Reference standard AMHs were more comprehensive than initial AMHs in several ways. First, pharmacists obtaining reference standard AMHs started with initial AMH data. As such, study arm could not be masked. Second, reference standard AMHs were only obtained by pharmacists considered to be ‘expert’ in this clinical skill based on their previous experience in obtaining medication histories. These pharmacists were advised to take additional time and to consider additional information (eg, previous hospital discharge orders) as necessary. Third, these pharmacists often had new information available to them (eg, medication lists brought in after admission, improved patient mental status). Finally, these pharmacists identified errors that arose during clinical care prior to the reference standard AMH. Some of these pharmacists were study authors. To maximise patient benefit from the study, reference standard AMH findings, including any impact on AMOs, were communicated to the appropriate clinician.

Primary outcome: mean severity-weighted AMH error score

In obtaining reference standard AMHs, expert pharmacists identified AMH errors in the initial AMHs and classified each error according to a previously developed taxonomy as significant, serious or life threatening.1 Error severity weights of 12=1, 22=4 and 32=9, respectively, were chosen to reflect the relative capacity of each error type to cause patient harm. A second pharmacist reviewed classifications, and a physician adjudicated disagreements. Because the reference standard pharmacist obtained their AMH while the patients were still hospitalised and used contemporaneous information (eg, conversations with patients and family members), study arm could not be masked. Because of the vast amount of complex information that might be consulted in determining error severity, we also chose not to mask study arm with case summaries for other reviewers.

For each patient, we calculated a severity-weighted AMH error score. We used this novel error score because it provides a single, severity-weighted measure of error for each AMH. This allowed our power analysis to account for the different potential clinical consequences of different error severities. For each trial arm, we calculated a mean severity-weighted AMH error score.

Secondary outcome: mean severity-weighted AMO error score

For each AMH error identified, two physicians independently reviewed the relevant medications ordered at hospital admission in the context of the clinical chart. They classified each AMH error as either resulting in no AMO error, or an AMO error of significant, serious or life-threatening severity. In cases where the admitting physician’s knowledge of an AMH error was unclear and where the resultant orders were clinically reasonable (eg, the AMH erroneously omitted hydrocodone and it was not ordered at admission, but where it may have been intentionally held for altered mental status, rather than unintentionally omitted), we determined that the AMH error did not clearly lead to any AMO error. A third physician adjudicated disagreements. All adjudicating physicians were study authors. Because all AMO determinations began with a previously identified AMH error, we did not address AMO errors unrelated to AMH errors.

Tertiary outcomes

Kruskal-Wallis and Fisher’s exact tests were used to compare the three arms in terms of patients’ mean length of stay and the per cent of patients readmitted to Cedars-Sinai Medical Center within 30 days, respectively. The study was not powered to detect differences in these tertiary outcomes.

Statistical analysis

Using single-factor analysis of variance (ANOVA), we determined that a sample of 300 patients would achieve 80% power to detect absolute error score differences of at least 11.2 using the Tukey-Kramer (pairwise) multiple comparison test with an alpha of 0.05.9 10 Based on pilot data, we expected patients in the usual care group to have a mean severity-weighted error score of 20.7, with an SD of 16.2. A difference of 11.2 units is clinically significant, representing 1 life threatening, almost 3 severe, or 11 significant AMH errors.

Clinical and demographic variables were summarised using mean or count. Error counts per patient and error scores per patient were summarised by study arm using mean. In accordance with the a priori analysis plan for this randomised trial, we used linear regression models to compare primary outcome and secondary measures across study arms (ANOVA). Because baseline characteristics were balanced across study arms, the linear regression models were not adjusted for any other variables. Post hoc pairwise comparisons between study arms used a Tukey-Kramer adjustment for multiple testing. The outcomes were transformed for the models due to outliers in the distributions. To test whether results were robust to the unknown outcomes of patients admitted but lost to follow-up, we conducted a sensitivity analysis where all such intervention patients were assumed to have the worst AMH error score measured for any patient, and where all such usual care patients were assumed not to have any AMH errors.

To minimise the effect of outliers in the distributions of error counts and scores, a rank transformation was applied to the outcomes in the regression models. The results of hypothesis testing for transformed and non-transformed outcomes were similar, but the residuals in the rank-transformed data better fit the model assumptions as the variance of the outcomes in the usual care group was larger than the other two groups. The following variables were compared across study arms with Kruskal-Wallis tests: number of medications, zip code median income, weighted Charlson comorbidity score and length of stay. Insurance type, race, ethnicity and readmission rate were analysed across study arms using Fisher’s exact test. Analyses used SAS V.9.3.

Results

Enrolment and baseline characteristics

We enrolled 306 patients. Patient characteristics, including age, sex, race, ethnicity, insurance, number of medications, income and comorbidities, were similar across study arms (table 1). The mean±SD patient age was 72±12 and number of medications present in the EHR prior to obtaining an AMH was 15±7.

Table 1

Baseline characteristics of patients

Of 103 and 102 patients randomised to the pharmacist and PSPT arms, only 5 (5%) and 9 (9%) did not receive the intervention, respectively. These patients and 14 others for whom a reference standard AMH was not obtained were classified as dropouts (figure 2). The primary outcome was not measurable for these 28 (9.2%) patients lacking a reference standard AMH. Therefore, except for the sensitivity analyses, further results are based on the 278 remaining patients.

Figure 2

Consort flow diagram.

Identification and adjudication of AMH errors and resultant AMO errors

Pharmacist raters found that 192 (69%) of 278 patients had 1016 AMH errors. They determined that 399 (39%) AMH errors were significant, 605 (60%) were serious and 12 (1%) were life-threatening errors. These errors occurred in the AMHs of 138, 164 and 11 patients, respectively.

Physician raters agreed that 419 (41%) of these AMH errors clearly led to an AMO error. The 419 AMO errors occurred among 142 (74%) of the 192 patients who had an AMH error. Raters found that 261 (62%) AMO errors occurring among 117 patients were significant, 155 (37%) among 84 patients were serious and 3 (1%) among 3 patients were life-threatening errors. Examples of AMH and AMO errors identified are detailed in online supplementary table 1.

Supplementary file 1

Outcome comparisons across arms

There was a mean±SD of 8.0±5.6 AMH errors per patient in the usual care arm versus 1.4±1.9 and 1.5±2.1 AMH errors per patient in the pharmacist and PSPT arms, respectively (pairwise t-tests, p<0.0001) (table 2). When we accounted for error severity via the primary outcome of severity-weighted AMH error score, patients in the usual care arm had a mean±SD severity-weighted AMH error score of 23.0±16.1 versus scores of 4.1±6.8 and 4.1±7.0 in the pharmacist and PSPT arms, respectively (p<0.0001).

Table 2

Outcomes of 278 patients with reference standard AMH

Our sensitivity analysis, which assumed that all intervention patients lost to follow-up had the worst measured AMH severity score (100), but that usual care patients lost to follow-up had no AMH errors, resulted in the usual care arm having a mean±SD severity-weighted AMH error score of 22.0±16.4 versus scores of 9.0±22.1 and 13.8±29.8 in the pharmacist and PSPT arms, respectively (p<0.0001).

Patients in the usual care arm had a mean±SD of 3.2±2.9 AMO errors per patient versus 0.6±1.1 and 0.6±1.1 AMO errors per patient in the pharmacist and PSPT arms, respectively (p<0.0001). Accounting for error severity showed that patients in the usual care arm had a mean±SD severity-weighted AMO error score of 6.9±7.2 vs 1.5±2.9 and 1.2±2.5 in the pharmacist and PSPT arms, respectively (p<0.0001).

Using Cohen’s d to standardise the magnitude of the measured effect revealed that for the primary outcome of AHM error score, the effect size for each intervention was 1.5 (table 3). For the more downstream outcome of severe or life-threatening AMO errors, the effect size for each intervention was approximately 0.8. These measurements are accepted to represent very large and large effect sizes, respectively.11 Although this trial was not designed to test for non-inferiority, we found no differences in any outcomes between pharmacists and PSPTs.

Table 3

Comparing AMH error and AMO error rates across study arms

Of 183 patients randomised to either intervention, 29 (16%) had a serious or life-threatening AMO. Compared with 56 (59%) of 95 control patients with such errors, this represents a number needed to treat of 3 (point estimate 2.3, 95% CI 1.8 to 3.2). This number underestimates the intervention’s impact because many patients had multiple serious AMO errors. Although there were no statistically significant differences in utilisation outcomes across arms, point estimates for length of stay were approximately 1 day longer in the intervention arms (p=0.13), and point estimates for 30-day readmission rates were approximately 10% lower in the intervention arms (p=0.16).

Discussion

In this three-arm randomised controlled trial, adding AMH interviews by pharmacists or PSPTs to usual care processes reduced AMH errors by over 80%. The most downstream and clinically meaningful result was reducing the severe and life-threatening AMO error rate from 1.2 per patient in the usual care arm to 0.2 per patient in the intervention arms. Preventing AMOs should allow patients to avoid ADEs, which are known to increase length of stay, cost, morbidity and mortality.2 12

We found a much larger benefit than prior research. Many prior studies checked AMHs after AMOs were placed, thus resembling our usual care arm. For example, one systematic review found that the median study only identified (and in some cases addressed) 0.35 clinically significant unintentional medication discrepancies per patient.13 In contrast, our usual care arm reference standard AMHs identified a mean of 1.2 severe or life-threatening AMO errors per patient, which translated to a much greater opportunity for reductions.

We attribute the high baseline error rate to the medically complex patient population we studied, which resulted from our inclusion criteria. Two prior systematic reviews had conflicting findings regarding targeting interventions at high-risk patients. One review found such targeting in 13 of 26 studies, and deemed it to be a ‘key aspect of successful interventions’.14 The other review found such targeting in 7 of 20 interventions, and determined that ‘commonly used criteria for selecting high-risk patients do not consistently improve the effect of medication reconciliation.’13 Our study patients had a mean of 15 medications present at enrolment versus prior study population means ranging from 7 to 11 medications.15 The strong effect of our intervention suggests that targeting may be helpful if it is used to identify these patients at extremely high risk for ADEs. Such patients are already prevalent at CSMC, and this cohort is growing quickly throughout the developed world due to population ageing and increasing prescription drug use.16

The second factor likely contributing to the strong effect, and likely related to the high-risk patient population, is the substantial time spent by the pharmacist and PSPTs who conducted the intervention. In a time and motion study reported elsewhere, we found that they spent 58.5 and 79.4 min per patient, respectively (p=0.14).17 Although one other study reported similar results,18 this represents substantially more time than the 20–40 min reported in several prior studies conducted on younger, healthier patients.19 20 Beyond these substantial time requirements, these interventions also require pharmacy personnel to be stationed in the ED and able to attend to AMHs as soon as a determination to admit a patient has been made—before AMOs are placed. As such, these interventions may be best suited to large hospitals with sufficient ED patient volume to justify stationing pharmacy personnel in the ED.

To better understand the potential impact of the studied interventions, we consulted previous literature showing that 0.9% of AMO errors result in an ADE during hospitalisation.21 Critically, the studied interventions have potential advantages that we did not evaluate. The intervention workflows should be more efficient than using pharmacists to retrospectively check usual care processes and to contact and convince ordering physicians to request changes before errors cause harm. Furthermore, it seems likely that the interventions streamlined physicians’ workflows and saved them time by allowing them to order from accurate AMHs, to minimise downstream pharmacist contacts and to reduce the need for corrections. Finally, and most importantly, prior research has shown the greatest benefit of reducing AMH errors to be a reduction in postdischarge prescription errors and resultant ADEs.4 Future research should endeavour to evaluate these hypothesised benefits.

Because one sought-after benefit of using PSPTs is to reduce costs, it is notable that we found no difference in the benefit provided by PSPTs versus pharmacists. This is consistent with other similar studies.22 23 However, our aforementioned time and motion analysis also did not find intervention costs to be lower in the PSPT arm, as compared with the pharmacist arm, once the costs of pharmacist supervision were included.17 Nonetheless, the current study may allay concerns of effectiveness that have hindered PSPT adoption. With effectiveness established, these results point to an opportunity to improve PSPT efficiency, through altered work processes and the use of electronic pharmacy claims data (EPCD), which could make PSPT both a better and less expensive intervention.

Generalisability is a known gap in medication reconciliation intervention research.7 Beyond embracing an intervention that we thought would improve efficiency and reduce implementation complexity, we also designed our trial to be pragmatic. In contrast to prior work,15 we included many patients admitted by community physicians. Because the interventions did not require physician workflow changes, many physicians were unaware of the trial entirely. We included resident pharmacists to ensure that experience was unnecessary. We minimised biases associated with requiring patients to opt-in. All of these factors should contribute to strong external validity.

The findings must be interpreted in the context of limitations. First, the study was powered on intermediate endpoints, rather than on patient-centred outcomes (PCO). Although there is an established linkage between AMH errors and PCO,1 it would be useful to study PCO directly, especially because systematic reviews have drawn conflicting conclusions about whether previously studied medication reconciliation interventions affect PCO.6 13 15 24 Second, we only used one site. Third, not all aspects of randomisation were masked from study personnel. Because block size was not masked, selection bias could have occurred. Furthermore, we could not practicably mask arm allocation. Fortunately, we were able to increase objectivity by leveraging accepted methodology, which used agreement of independent raters to identify and rate the severity of AMH and AMO errors.4 Finally, study providers could not access EPCD. Because EPCD is likely now available in most US hospitals, and because it has good potential to reduce AMH errors and to reduce the time needed to obtain AMHs, it will be important to retest these interventions with EPCD.25

Conclusions

Among medically complex older adults, pharmacists and pharmacist-supervised pharmacy technicians reduced admission medication history errors and resultant admission medication order errors by over 80% by obtaining admission medication histories in the ED. This effect was robust to severity weighting, and thus shows promise for reducing patient harm. We attribute the strong effect to a high-risk patient population and an intensive intervention. Future research should test whether these results generalise to other settings and affect patient-centred outcomes, and whether hypothesised efficacy and efficiency benefits are indeed demonstrable.

Acknowledgments

The authors acknowledge Margaret Kelley for her role in coordinating manuscript submission. The authors acknowledge Drs Joel Geiderman, Sam Torbati and the rest of the Cedars-Sinai Emergency Department staff for allowing pharmacy personnel to use space in the Emergency Department during the study.

References

View Abstract

Footnotes

  • Contributors JP had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: JP, CN, CJ, KP, RS, AR, MB, DB. Acquisition, analysis or interpretation of data: JP, CN, CJ, KP, RS, GCW, AR, MB, OR, DS, BD, AD, DB. Drafting of the manuscript: JP, CN, GCW. Critical revision of the manuscript for important intellectual content: JP, CN, CJ, KP, RS, GCW, AR, OR, BD, DB. Statistical analysis: JP, CJ, GCW, AR. Administrative, technical or material support: JP, CN, KP, RS, MB, OR, DS, DB. Study supervision: JP, CN, KP, RS, DB.

  • Funding Joshua Pevnick was supported by the National Institute On Aging and the National Center for Advancing Translational Science of the National Institutes of Health under awards K23AG049181 and UCLA CTSI KL2TR000122. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

  • Competing interests JP currently receives funding from the American Society for Health-System Pharmacists Research and Education Foundation to design a toolkit for pharmacists to use in postdischarge medication management.

  • Ethics approval Cedars-Sinai Medical Center Institutional Review Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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