NEU External Validity Analysis

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NEU External Validity Analysis

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EDITOR’S CHOICE Effects of an online personal health record on medication accuracy and safety: a cluster-randomized trial 2,3 2,1 Jeffrey L Schnipper,1 ‘2 Tejal K Gandhi,’ Jonathan S Wald,’ Richard W Grant,2 ‘5’6 Eric G Poon,1’2 Lynn A Volk,7 Alexandra Businger,1 Deborah H Williams,7 Elizabeth Siteman,7 Lauren Buckel,7 Blackford Middleton1’2’8 Additional materials are published online only. To view these files please visit the journal online )http://dx.doi.orgl 10.1 136/arniajnl-2011-000723). ‘Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts, USA 2Harvard Medical School, Boston, Massachusetts, USA 3Patient Safety, Partners HealthCare System, Boston, MA 4RTI International, Waltham, Massachusetts, USA ‘General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA ‘Division of Research, Kaiser Perrnanente Northern California, Oakland, California, USA 7 Clinical and Quality Analysis, Partners HealthCare System, Boston, MA ‘Clinil Informatics Research and Development, Partners HealthCare System, Boston, Massachusetts, USA Correspondence to Or Jeffrey Schnipper, Division of General Medicine, Brigham and Women’s Hospital, 162D Tremont Street, Boston, MA 02120-1613, USA; jschnipper@partners.org Received 23 November 2011 Accepted 10 April 2012 Published Online First 3 May 2012 ABSTRACT Objective To determine the effects of a personal health record )PHR)-linked medications module on medication accuracy and safety, Design From September 2005 to March 2007, we conducted an on-treatment sub-study within a clusterrandomized trial involving 11 primary care practices that used the same PHR. Intervention practices received access to a medications module prompting patients to review their documented medications and identify discrepancies, generating ‘eJoumals’ that enabled rapid updating of medication lists during subsequent clinical visits, Measurements A sample of 267 patients who submitted medications eJournals was contacted by phone 3 weeks after an eligible visit and compared with a matched sample of 274 patients in control practices that received a different PHR-linked intervention. Two blinded physician adjudicators determined unexplained discrepancies between documented and patientreported medication regimens. The primary outcome was proportion of medications per patient with unexplained discrepancies. Results Among 121 046 patients in eligible practices, 3979 participated in the main trial and 541 participated in the sub-study. The proportion of medications per patient with unexplained discrepancies was 42% in the intervention arm and 51% in the control arm (adjusted OR 0.71, 85% Cl 0,54 to 0.94, p=0.01). The number of unexplained discrepancies per patient with potential for severe harm was 0.03 in the intervention arm and 0.08 in the control arm (adjusted RR 0.31, 95% Cl 0.10 to 0.82, p=0.04). Conclusions When used, concordance between documented and patient-reported medication regimens and reduction in potentially harmful medication discrepancies can be improved with a PHR medication review tool linked to the provider’s medical record. Trial registration number This study was registered at ClinicalTrials.gov (NCT00251 875). BACKGROUND AND SIGNIFICANCE Medication-related morbidity and mortality is estimated to result in $76 billion dollars in total costs annually.’ One drug-related problem, adverse drug events (ADEs), broadly defined as injuries due to medications,2 is estimated to occur in 25% of ambulatory patients.-3 Of these, approximately 11% are considered preventable and an additional 28% ameliorable. 728 One important cause of ambulatory ADEs is medication discrepancies, including unexplained differences between medication regimens patients think they should be taking and regimens collectively prescribed by their physicians.4 Discrepancies can have serious consequences, including prolonged periods of over- or under-treatment .5-8 By definition, medication discrepancies are not reported by patients and can only be detected by active surveillance. Communication regarding medication-related problems may be completely absent between patient visits and is often inadequate even during visits because of competing demands, patient concerns about bothering their physicians, or limited patient involvement in their own care. Consequently, drug-related problems remain undetected3 and the opportunity to mitigate these problems is lost. By empowering patients to become active participants in their own care, a personal health record (PHR) linked to an ambulatory electronic health record (EHR) has the potential to address many medication safety and quality issues.9 A PHR module focused on medications could allow patients online access to update medication data from their EHR, identify discrepancies, and report medication concerns. This information could then be conveyed to the patient’s physician, who can discuss it with the patient, update the EHR, and take action as needed. At Partners HealthCare System, we developed and deployed such a tool within a PHR (Patient Gateway, PC) to address medication issues.’° 11 The design of the PG Medications Module, preliminary usage data, and patient and provider impressions of 12 the application have been described previously. This manuscript reports how the module affected medication safety outcomes and patient—provider communication. We hypothesized that the module would reduce discrepancies between patientreported and EHR-documented medication regimens and reduce potential and preventable/ameliorable ADEs. METHODS Setting Partners HealthCare System is an integrated regional healthcare delivery network in eastern Massachusetts with more than 20 affiliated primary care clinics. The main EHR used in JAm Med Inform Assoc 2012:19:728-734. doi;10.1 136/amiajnl-201 1-000723 Partners ambulatory clinics is the Longitudinal Medical Record (LMR), an internally developed, certified EHR’3 Informed consent was obtained from eligible patients prior to notification of practice randomization status. This study was approved by the Partners HealthCare institutional review board and registered at ClinicalTrials.gov (NCT00251 875). Patient Gateway PG is a secure online PHR developed by Partners to improve patient—provider communication. At the time of the study, PG allowed patients limited access to their LMR data (including read-only access to their medication lists) and gave them the ability to request appointments and referrals, communicate with their physician via secure email, request prescription renewals, and access a health information library. Prepare for Care study The Prepare for Care study was a cluster-randomized trial with active controls conducted from September 2005 to March 2007 in 11 Partners primary care practices that used PG and agreed to participate in the study. Practices were randomized to one of the two arms after matching for setting (urban vs suburban), services (women’s health vs general), and size (large vs small). Randomization of matched pairs of practices was carried out using random number generation in Excel (Microsoft, Redmond, Washington, USA) by the study statistician. To be eligible for the study, patients had to have an active PG account (ie, had logged in at least once) and at least one visit with their designated primary care provider (PCP) in a study practice in the prior year. As part of this study, the pre-visit ejournal, a new feature of PC, was developed.10 11 In the present study, patients in the intervention arm were invited to complete medications ejournals prior to an upcoming PCP visit that allowed them to review and indicate updates to their medication lists, allergies, and if applicable, diabetes management information. Patients in the active control arm were invited to complete ejournals that let them review and update family history and provided views of health maintenance reminders (hereafter referred to as health maintenance ejournals). Given the nature of the interventions, practices and providers could not be blinded, but outcome adjudicators remained blinded (see below). Data collectors were initially blinded to study arm, but some patients revealed information during follow-up phone calls that may have led to unblinding in some cases. Medications Module The design of the PG Medications Module has been described previously.12 Upon invocation of the module, patients saw the current active LMR medication list and were asked about any discrepancies between this list and what patients thought they should be taking (eg, differences in dose, missing medications). The module asked patients about any problems they might be having with adherence, any possible side effects, and if they needed a prescription refill. Once a medications ejournal had been submitted, the patient’s practice could view the information in a modified LMR medication screen that displayed automatically instead of the usual medication screen. A PCP could easily verify and move medications ejournal information (eg, changes to the medication list) into the LMR. Patients were informed that their PCPs would have access to their ejournals during the consent process, but they were not automatically notified of whether their providers had viewed the information. JAm Med Inform Assoc 2012;19:728-734. doi:10.113/amiajn-2011-000723 Medication sub-study outcomes We conducted a sub-study of 541 patients from the main Prepare for Care study to assess the effect of the intervention when used, that is, an on-treatment analysis. First, we selected 267 patients in the intervention arm who had an eligible visit and submitted a medications ejournal. We then matched those patients to 274 patients in a matched practice in the active control arm who had a similar visit (annual or follow-up) during the same month. Prepare for Care study subjects were invited to participate in the sub-study by email and were contacted if they did not opt out within 1 week; patients could also refuse to participate when contacted. Because only annual visits triggered health maintenance ejournal invitations (at the request of the practices) while both annual and follow-up visits triggered medications ejournal invitations, not all patients in the control arm of the sub-study were invited to or completed a health maintenance ejournal. All medication safety outcomes were assessed using a process similar to that employed in previous studies.4 14 15 Intervention and control patients were contacted by phone by a trained research assistant (RA) beginning 3 weeks after the PC? visit following a predefined protocol. The survey was developed based on previous studies regarding medication discrepancies and AIDEs after discharge,2-4 but the survey was not otherwise validated. RAs first asked patients to name all the medications they thought they were supposed to be taking, including dose, route, and frequency. If any discrepancies between that list and the LMR medication list were found, reasons for the discrepancy were explored. Patients were then asked about possible AIDEs. RAs conducted a thorough review of symptoms patients might have had within the previous 3 months. If symptoms were reported, RAs elicited further details and asked directed questions to determine the possible relationship of the symptom to medication use. Results of each survey were presented to two blinded physician adjudicators (adjudicators included JLS, TKG, RWG, and one non-author physician). Adjudicators first decided whether a discrepancy between documented and reported medication regimens was readily explained (eg, a change made by one of their physicians since the visit). For the remaining (unexplained) medication discrepancies, adjudicators decided whether the discrepancy had potential for patient harm. Severity of potential harm (significant, serious, or life-threatening) was also assessed.4 For patient-reported symptoms, based on the phone survey and medical records within a month of the patient visit, adjudicators decided whether the symptom was due to a medication using a slx-6point confidence scale, using the Naranjo algorithm as a guide.1 In the event of a likely AIDE, adjudicators decided on its severity (significant, serious, life-threatening, or fatal), whether the AIDE could have been prevented, and if not, whether the AIDE could have been ameliorated (ie, lessened in severity or duration).4 All differences between adjudicators were resolved by consensus. Survey outcomes As part of a follow-up survey administered in October through December 2006 to all patients in the main study who submitted an ejournal of any type, we assessed how frequently patients self-reported communicating with their physicians regarding medication issues. Analysis The primary outcome was discordance between documented and reported medication regimens, that is, the proportion of each 729 patient’s medication regimen with unexplained medication discrepancies. The denominator was calculated as all medications on either the LMR medication list or reported by the patient, while the numerator was the number of unexplained discrepancies assessed by outcome adjudicators. Secondary outcomes included the number of discrepancies with potential for harm per patient and number of discrepancies with potential for severe harm. We also assessed the number of preventable or ameliorable ADEs per patient, and the duration of ameliorable ADEs. Adjusted analyses were conducted using PROC GENMOD in SAS using a binomial logistic model in which the outcome was in the form of X/N, where X was the number of unexplained discrepancies and N was the total number of medications. To adjust for possible confounding (ie, due to imperfect randomization at the practice level or imbalance between ejournal submitters and non-submitters), we used propensity scores17: we first derived a model to predict being in each of the two study arms, adjusting for patient age, sex, race, number of medications, number of prior visits, and median income by zip code. The score derived from this model was then used in all subsequent models of study outcomes. We used general estimating equations to adjust for clustering by provider. For the number of discrepancies and ADEs per patient, we used propensity-score adjusted Poisson regression. For duration of ameliorable ADEs, we used a multinoniial model since the outcome was an eight-level ordinal response. Figure 1 Study flow diagram. *No match’ indicates patients in the active control arm who did not match any patient in the intervention arm of the sub-study by practice and by date and type of visit. t’Opted out’ includes those patients who opted out by email after receiving the invitation to participate in the medications sub-study and those who declined participation once contacted by phone. $’Never called’ indicates those patients in the active control arm who did not need to be called because a sufficient number of eligible patients had already been enrolled. PCP, primary care provider. To address issues of possible unadjusted confounding caused by differences between patients who submitted and did not submit ejournals, we conducted a secondary analysis of all outcomes limiting the study population to just those who submitted ejournals of any type. We also conducted an analysis in which we adjusted for clustering at the practice level instead of the provider level. Power and sample size With an anticipated sample size of 300 patients in each arm of the medications sub-study, we had 80% power to detect a decrease in unexplained medication discrepancies from 50% to 38% with a two-sided a of 0.05, estimated cluster size of three patients per PCP, and intra-class correlation coefficient of 0.10. Two-sided p values