research paper

American Journal of Medical Quality 2016, Vol. 31(4) 315 –322 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1062860615574327 ajmq.sagepub.com

Article

Hospitalized patients in the United States are increasingly being cared for by physicians other than their primary care physicians (PCPs).1 In 2010, more than 80% of US hospitals with ≥200 beds had hospitalist programs.2 As a result, the importance of communication between hospi- tal providers and PCPs to prevent medical errors and improve quality of care has come to the forefront.3-6 Hospital systems are, for the most part, not optimized to provide efficient transfer of this vital information, and communication between physicians caring for hospital- ized patients and PCPs is often suboptimal.7-9

For patients with complex medical problems, the hospi- tal discharge period is particularly prone to errors.5 Medications may have been discontinued or added or may have had dosing changes during a hospitalization, fre- quently leading to errors. Medical errors are common in the early postdischarge period,10 and adverse events occur in about 20% of patients post discharge, most often because of medications.11,12 Medication errors and adverse drug events (ADEs) are frequently caused by hospital system factors,13 such as ineffective communication between caregivers.11

Almost half of discharged patients have unexplained medi- cation discrepancies, heightening ADE risk.14 Medication reconciliation is a Joint Commission National Patient Safety Goal and a core measure of Stage 2 meaningful use.15 However, hospitals and electronic medical record (EMR) vendors have struggled to meet this mandate.16,17

Prior research has studied interventions to decrease medication errors at hospital discharge and to improve patient outcomes.18 Some interventions used medication reconciliation performed by pharmacists, with medication errors being variably affected by these interventions.19,20 Computerized medication reconciliation tools have been developed21 and have shown promise as a means to decrease medication errors, but effects on patient outcomes

574327AJMXXX10.1177/1062860615574327American Journal of Medical QualitySmith et al research-article2015

1University of Pittsburgh, Pittsburgh, PA 2Weill Cornell Medical College, New York, NY

Corresponding Author: Kenneth J. Smith, MD, MS, Department of Medicine, University of Pittsburgh, 200 Meyran Ave, Suite 200, Pittsburgh, PA 15232. Email: [email protected]

Automated Communication Tools and Computer-Based Medication Reconciliation to Decrease Hospital Discharge Medication Errors

Kenneth J. Smith, MD, MS1, Steven M. Handler, MD, PhD1, Wishwa N. Kapoor, MD, MPH1, G. Daniel Martich, MD1, Vivek K. Reddy, MD1, and Sunday Clark, ScD, MPH2

Abstract This study sought to determine the effects of automated primary care physician (PCP) communication and patient safety tools, including computerized discharge medication reconciliation, on discharge medication errors and posthospitalization patient outcomes, using a pre-post quasi-experimental study design, in hospitalized medical patients with ≥2 comorbidities and ≥5 chronic medications, at a single center. The primary outcome was discharge medication errors, compared before and after rollout of these tools. Secondary outcomes were 30-day rehospitalization, emergency department visit, and PCP follow-up visit rates. This study found that discharge medication errors were lower post intervention (odds ratio = 0.57; 95% confidence interval = 0.44-0.74; P < .001). Clinically important errors, with the potential for serious or life-threatening harm, and 30-day patient outcomes were not significantly different between study periods. Thus, automated health system–based communication and patient safety tools, including computerized discharge medication reconciliation, decreased hospital discharge medication errors in medically complex patients.

Keywords medication error, medication reconciliation, hospital discharge, communication tools

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are unclear.22,23 This study examines a health care system’s implementation of a broader set of automated PCP com- munication tools, including computerized medication rec- onciliation, and its impact on discharge medication errors.

Methods

A pre-post quasi-experimental study of a series of sys- tem-wide automated communication and patient safety tools was performed within the University of Pittsburgh Medical Center (UPMC) system, which in 2010 operated 20 hospitals throughout Western Pennsylvania. Data were collected for patients hospitalized at UPMC Presbyterian, UPMC’s major academic hospital.

The University of Pittsburgh Institutional Review Board approved a waiver of informed consent/HIPAA (Health Insurance Portability and Accountability Act) authorization to access, record, and use protected patient health information/patient medical record information. This study is registered at ClinicalTrials.gov, Identifier: NCT01397253.

The preintervention period for this study was April 1, 2009, through October 7, 2010. The end date was chosen based on the first of the new automated PCP communica- tion initiatives, rolled out on October 8, 2010. Assisted by an expert PCP panel, using the modified Delphi technique to seek consensus on information items PCPs want to receive,24 other initiatives were sequentially rolled out to improve notifications about admission, critical illness occurrence, test results, and discharge communication (see Figure 1). The UPMC Office of Physician Relations sent notifications by secure e-mail or fax, using the PCPs’ preferred method. The Office of Physician Relations

maintained addresses and phone numbers to ensure timely delivery notification while managing and correct- ing any process failures. These efforts culminated in a mandatory EMR-based discharge medication reconcilia- tion procedure, with reports given to patients and sent to PCPs. This procedure, implemented in Cerner PowerChart (Cerner, Kansas City, Missouri), UPMC’s inpatient EMR, was launched on August 22, 2011; this began the postint- ervention period, which ended on December 31, 2012. At hospital discharge, physicians used this tool to reconcile discharge medications against medication histories obtained on hospital admission by hospital personnel; use was required to order discharge medications and to dis- charge patients. In the preintervention period, a paper- based nonmandatory discharge medication reconciliation process was in place, similarly reconciling against medi- cation histories obtained by hospital personnel; its effec- tiveness was unclear.

Patients were included if they were admitted to gen- eral medicine, geriatrics, or cardiology inpatient services; were ≥18 years of age; were discharged home; were med- ically complex (≥2 comorbid conditions present, defined using the Elixhauser comorbidity system25); were pre- scribed ≥5 preadmission medications (a measure of poly- pharmacy); and had outpatient care provided by PCPs who (1) use the UPMC Epic ambulatory care EMR (Epic Systems, Madison, Wisconsin) and (2) admitted ≥5 patients to UPMC Presbyterian in the year preceding the study. The Epic ambulatory EMR is used by approxi- mately 90% of UPMC outpatient providers. Patients were excluded if they were admitted to critical care units, admitted from skilled nursing facilities, diagnosed with dementia, or were organ transplant recipients; exclusions

Figure 1. Intervention elements.

Hospital admission notifications to primary care physicians (PCPs) with contacts for communication

PCP notification of patient transfer to critical care units

Mandatory computer-assisted discharge medication reconciliation

PCP notifications at a patient’s hospital discharge Current problem list Advance directive information Vaccination history Reconciled medication list Major tests and procedures Test results pending Planned follow-up Patient discharge instructions Patient information material/education received Hospital contacts for communication Discharge summary

Smith et al 317

were based on the expectation that study patients would be admitted from and discharged to a community setting in which they would resume care with their PCP. All medically complex patients identified and meeting inclu- sion/exclusion criteria were included in analyses.

Medication errors were identified using a 2-stage pro- cess.26,27 For the purposes of the study, this process was performed retrospectively after a patient’s hospital dis- charge and, thus, was entirely separate from procedures performed during the hospitalization by hospital person- nel during all phases of this study. In the first stage of the study-based process, trained research personnel created a case summary of each patient’s medications, which included preadmission medications, medications prior to discharge, and discharge medications. This case medica- tion summary was created by examining ambulatory EMR data on a patient’s current medications at the last PCP encounter before hospitalization. This retrospec- tively constructed list, intended to be a gold standard rep- resentation of prehospital medication use, was not connected to the medication history obtained by hospital personnel at the time of admission. Hospital medications and discharge medications were included in the study- based medication case summary using hospital EMR data post discharge. Discharge medications were those listed, after medication reconciliation, in discharge medication instructions given to the patient and sent to the PCP. Discrepancies in medication regimens were identified by comparing the preadmission medication list, hospital medications, and discharge medications. Any differences between the study-based preadmission medication case summary and discharge medications were considered medication variances. Hospital personnel, when obtain- ing the medication history, had access to the outpatient EMR throughout all study periods.

During the second stage of the study-based medication error identification process, 2 hospital-based clinical phar- macists independently reviewed those study-based medi- cation variance summaries, using methods described previously.27 Both pharmacists had previous experience and concurrent activity in clinical medication review and received refresher training in error classification. They reviewed the EMR to identify the need for changes from the patient’s preadmission medication case record. Medication variances deemed medically necessary were not considered medication errors. Variances not consid- ered changes required by the patient’s clinical status were classified as medication errors. The pharmacists then independently classified medication errors, via the schema of Pippins et al,27 as clinically important if there was the potential to cause death, permanent or temporary disabil- ity, prolonged hospital stay, readmission, or additional treatment or monitoring to protect the patient from harm; by this schema,27 these were serious or life-threatening

potential ADEs. All disagreements between pharmacists were resolved by consensus during periodic face-to-face meetings, supplemented by telephone and electronic com- munication. The pharmacists could not be blinded because of their use of the entire EMR in their reviews and the time-based nature of the intervention. Data for secondary outcomes (30-day readmission, emergency department visits, and follow-up PCP visits) were obtained through EMR review. Patients with >1 hospitalization during a study period were eligible for inclusion only during their first hospitalization but could be included once each dur- ing the preintervention and postintervention periods.

All comparisons were performed using Kruskal- Wallis and χ2 tests. To control for potential confounders, multivariable logistic regression was performed. Factors were included in the multivariable mixed-effects model if they were significantly associated with the outcome variable (unintended medication variances) at P < .20 or considered potentially clinically significant. A P < .20 was chosen because more traditional levels (eg, P < .05) can, in multivariable models, fail to identify the follow- ing: (1) variables known to be important or (2) collec- tions of variables that, considered together, are significant predictors when they are not significant individually.28 Because they could contribute to both study periods and because of multiple medications per individual, patients were included in the mixed-effects model as a random effect, and individual patient characteristics were included as fixed effects. Pre hoc power and sample size calculations showed that detection of a 10% absolute reduction in discharge medication errors (primary out- come) from an estimated baseline of 41% at α = .05 and 90% power required enrollment of 381 participants dur- ing each period (n = 762 over the entire study). This study planned enrollment of 500 patients in each period to increase power to detect differences in 30-day rehos- pitalization, emergency department visits, and PCP fol- low-up visits (secondary outcomes), with 80% power to detect 6% absolute reductions.

Changes in clinical responsibilities prevented all cases from being reviewed by both pharmacists. As a result, the primary analysis includes only cases reviewed by both pharmacists to ensure consensus regarding medication variances. A sensitivity analysis including all cases also was performed, whether reviewed by one or both phar- macists. In addition, a post hoc secondary analysis was performed that examined possible associations of sex, race, and hospital length of stay with medication errors.

Results

Data on 835 patient hospitalizations were obtained, 443 pre intervention and 392 post intervention. Of these, 560 (67%) had discharge medication variances reviewed by

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both pharmacists (317 pre intervention, 243 post interven- tion); these patients are included in the primary analysis, the remainder are included in a sensitivity analysis. It was

found that 28 patients were in both pre and post cohorts. Age, sex, and race did not differ between study periods (Table 1). Postintervention patients were significantly

Table 1. Characteristics and Outcomes of Participants, by Study Period.

Pre intervention, n = 317 Post intervention, n = 243 P Value

Demographic characteristics Age (years), median (IQR) 63 (53-76) 63 (54-73) .43 Sex (%) .20 Male 139 (44) 93 (38) Female 178 (56) 150 (62) Race (%) .44 White 216 (68) 151 (62) Black 96 (30) 86 (35) Native American/Alaskan Native 1 (0.3) 1 (0.4) Asian 3 (1) 4 (2) Hispanic 1 (0.3) 0 (0) Missing 0 (0) 1 (0.4) Insurance (%) <.001 Private 96 (30) 193 (79) Public 215 (68) 50 (21) Uninsured 4 (1) 0 (0) No documentation 2 (1) 0 (0)

Clinical characteristics Number of comorbidities (%) <.001 0 9 (3) 4 (2) 1 62 (20) 75 (31) 2 118 (37) 106 (44) 3 83 (26) 47 (19) 4 32 (10) 10 (4) 5 12 (4) 1 (0.4) 6 1 (0.3) 0 (0) Modified Elixhauser comorbidity index, median (IQR) 5 (3-11) 3 (0-5) <.001 Hospital length of stay (days), median (IQR) 3 (2-4) 2 (2-4) .54 Number of medications, median (IQR) 11 (8-15) 8 (6-10) <.001 Number of medications (%) <.001 5-9 107 (34) 165 (68) 10-14 126 (40) 61 (25) 15-19 62 (20) 14 (6) 20-24 15 (5) 3 (1) 25-29 6 (2) 0 (0) 30 1 (0.3) 0 (0)

Medication variance Medication variance (%) <.001 None 1836 (53) 1650 (58) Medically indicated variance 1009 (29) 814 (29) Medication error 645 (18) 359 (13) Clinically important medication error 9 (1.4) 11 (3.1) .10

30-Day follow-up Readmission (%) 58 (18) 41 (17) .74 Emergency department visit (%) 81 (26) 49 (20) .16 Attended PCP follow-up appointment (%) 148 (47) 109 (45) .04 Died (%) 0 (0) 0 (0) —

Abbreviations: IQR, interquartile range; PCP, primary care provider.

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more likely to have employer/commercial insurance. Modified Elixhauser comorbidity index scores29 and med- ications per patient were slightly lower post intervention.

Fewer medication errors occurred during the postin- tervention period. Clinically important medication errors did not differ between study periods. Although there was a small but statistically significant decrease in PCP fol- low-up visits post intervention, no differences were observed in hospital readmissions or emergency depart- ment visits.

Differences in medication errors remained statistically significant on multivariable analysis adjusting for age, sex, insurance, comorbidity, and number of medications (Table 2).

A sensitivity analysis, including cases only reviewed by a single pharmacist (totaling 835 hospitalizations; 443 pre intervention, and 392 post intervention), showed results not materially different from the primary analysis, with the fully adjusted multivariable mixed-effects model showing a reduction in medication errors post interven- tion (odds ratio [OR] = 0.52; 95% confidence interval [CI] = 0.42-0.66; P < .001). After adjustment, no signifi- cant differences were seen in clinically significant medi- cation errors or in 30-day patient outcomes.

In post hoc secondary analyses to assess associations between medication errors and sex, race, and hospital length of stay, race was not associated with medication errors (data not shown). However, women were more likely to have medication errors (OR = 1.40; 95% CI = 1.11-1.75) after adjustment for age, insurance, comor- bidity, and number of medications, and longer hospital stays were associated with fewer discharge medication errors (first quartile: reference; second quartile: OR = 0.91, 95% CI = 0.68-1.21; third quartile: OR = 0.56, 95% CI = 0.41-0.76; fourth quartile: OR = 0.60, 95% CI = 0.45-0.82) in the fully adjusted model. Stratifying by study period did not materially change results (data not shown).

Discussion

This study examined the impact of automated health sys- tem–based interventions on patient care quality and safety, in the context of a PCP’s patient being admitted to the

hospital, cared for by another physician, and discharged back to the PCP’s care. Statistically significant decreases in medication errors were seen when comparing preinterven- tion and postintervention periods. Clinically significant medication errors with potential for serious or life-threaten- ing consequences were rare and no different between study periods. After adjustment, 30-day patient care outcomes for rehospitalization and emergency department visits were not significantly different between study periods.

The intervention included automated communications to notify PCPs of their patients’ admission, discharge, and critical care transfers during a hospitalization and to pro- vide PCPs with important information on follow-up care at discharge. This information includes studies whose results were pending and reports from a mandatory computerized medication reconciliation process. Unfortunately, individ- ual intervention component effectiveness cannot be mea- sured. Because this study did not measure the effects of automated hospital communications on hospital/PCP inter- actions, it could be argued that the EMR-based mandatory discharge medication reconciliation was the key compo- nent in decreasing medication errors, with PCP communi- cation unlikely to affect this outcome. If so, demonstration that software-based medication reconciliation successfully reduced medication errors is still a valuable finding and consistent with prior studies.22,23 A conference convened to discuss challenges facing medication reconciliation, including myriad tracking systems, unclear responsibili- ties, and systems development needs, has made recom- mendations to help resolve them.17 On the other hand, communication between hospitalists and PCPs is a recent focus of research and guidelines, with hopes that electronic communication tools will improve patient care quality and outcomes4-6,30 and lead to information exchange between both parties, rather than passive information transfer from hospital to PCP.31 In theory, highly developed 2-way elec- tronic communication systems between hospitals and PCPs, with access to EMR data and direct communication links to hospital caregivers, could allow PCPs the option of participating more directly in their patients’ hospital care at a distance, providing virtual continuity of care through electronic means and, through this interaction, avoiding transition of care miscommunications that could lead to medical errors.

Table 2. Multivariable Mixed-Effects Model of Intervention Effects on Unintended Medication Variances (Medication Errors).

Odds Ratio 95% Confidence Interval P Value

Unadjusted 0.63 0.51-0.77 <.001 Adjusted for age, sex, and insurance 0.54 0.43-0.69 <.001 Adjusted for age, sex, insurance, and comorbidity score 0.52 0.41-0.67 <.001 Adjusted for age, sex, insurance, comorbidity score, and number of

medications 0.57 0.44-0.74 <.001

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In this study, comparisons were made between pread- mission medication lists that were created retrospectively by research personnel based on ambulatory EMR data and discharge medications. Thus, the effectiveness of the entire hospital medication transition reconciliation and prescribing process was tested en bloc, noting uncor- rected medication errors occurring from preadmission medications onward through the hospitalization, based on discrepancies between lists. Ambulatory EMR use to construct prehospitalization medication lists could be criticized if long intervals between PCP visits and hospi- talizations were seen, with new medications possibly added by non-PCP physicians in the interim but not noted in the EMR. However, the medication summaries were identically obtained throughout all study periods; thus, differences attributable to this effect should cancel out between preintervention and postintervention periods. Finally, the study-based reviewing pharmacists were not blinded, a potential limitation, because they needed access to the entire EMR for their determinations.

No differences were found in clinically important medication errors or in patient outcomes. Interestingly, clinically important medication error rates in this study were lower than those typically reported.27 It is not clear why. A common definition was used for errors,27 as was a well-described format for finding them.26,27 The study- based medication case record was obtained independently from the clinical medication history. Two trained clinical pharmacists examined each case record and, for the pri- mary analysis, reached consensus on medication error classification. In the study institution, a paper-based med- ication reconciliation process had been in place before this intervention, possibly diluting its effect. More recent studies found serious potential ADE rates at hospital dis- charge, from 0.01 to 0.21 per patient32; the present study found rates of 0.03 and 0.05 per patient in preintervention and postintervention, respectively. In addition, 30-day outcomes could have been underestimated if visits occurred at non-UPMC facilities because outcomes were ascertained using UPMC EMR data, a study limitation. However, study participants were patients of PCPs who use the UPMC EMR, likely mitigating this effect.

Post hoc secondary analyses found associations of errors with female sex and hospital length of stay. Greater medication error risk in women has been reported previ- ously33; its mechanism is unclear. Medication error risk decreased with longer hospital length of stay, a finding not described elsewhere. Although requiring confirma- tion, it raises several possibilities. Medication errors are commonly made at hospital admission32; longer hospital- izations may provide more opportunities for error correc- tion. Patients with shorter stays may be perceived as less sick, and less vigilance could result. Finally, patients with in-hospital ADEs have longer lengths of stay.34 ADEs

could trigger greater attention to medications and fewer errors at discharge.

There are limitations in quasi-experimental study designs.35 A nonrandomized study could insufficiently control for important confounding variables. This study controlled for variables where significant differences were found between study groups, but unmeasured con- founders could still affect results. Secular trends toward decreasing discharge medication errors also could explain the study results. However, a gap of less than 11 months between study periods makes this less likely. Introduction of the intervention represented a historical event that could have changed physician attitudes and affected results. On the other hand, randomized trials of medical informatics interventions are often difficult to perform within a single facility because of barriers to selective rollout of interventions.35 Contamination effects, wherein personnel learning a new intervention could apply it to all patients regardless of randomized group, also could occur.

Thus, a multicenter randomized trial of the study insti- tution’s automated tools would need to be performed to definitively demonstrate benefit. A multicenter random- ized trial of best practices to improve medication recon- ciliation at 6 US hospitals is ongoing. This effort, the Multicenter Medication Reconciliation Quality Improvement Study (MARQUIS), will assess multiple interventions, including medication reconciliation soft- ware, to specifically address obtaining a “best medication history” from hospitalized patients and using multiple processes to ensure that all necessary medications are taken post discharge.32

In conclusion, implementation of automated health system–based tools, including computerized discharge medication reconciliation, decreased hospital discharge medication errors in medically complex patients. Definitive assessment of these tools will await future multicenter trials.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: All authors are or have been employees of UPMC and/or the University of Pittsburgh. There are no other conflicts of interest.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Agency for Healthcare Research and Quality (R18HS18151, R01HS018721, K12HS019461), which had no role in the study design, collec- tion, analysis, interpretation, or drafting of the manuscript or in the decision to submit the manuscript for publication. The

Smith et al 321

content is solely the responsibility of the authors and does not represent the official views of the Agency for Healthcare Research and Quality.

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