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Logo of jamiaJAMIA - The Journal of the American Medical Informatics AssociationInstructions for authorsCurrent TOC
J Am Med Inform Assoc. 2012 Sep-Oct; 19(5): 728–734.
Published online May 3, 2012. doi:  10.1136/amiajnl-2011-000723
PMCID: PMC3422826
Effects of an online personal health record on medication accuracy and safety: a cluster-randomized trial
Jeffrey L Schnipper,corresponding author1,2 Tejal K Gandhi,1,2,3 Jonathan S Wald,1,2,4 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 and Blackford Middleton1,2,8
1Division 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
5General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
6Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
7Clinical and Quality Analysis, Partners HealthCare System, Boston, MA
8Clinical Informatics Research and Development, Partners HealthCare System, Boston, Massachusetts, USA
corresponding authorCorresponding author.
Correspondence to Dr Jeffrey Schnipper, Division of General Medicine, Brigham and Women's Hospital, 1620 Tremont Street, Boston, MA 02120-1613, USA; jschnipper/at/
Received November 23, 2011; Accepted April 10, 2012.
To determine the effects of a personal health record (PHR)-linked medications module on medication accuracy and safety.
From September 2005 to March 2007, we conducted an on-treatment sub-study within a cluster-randomized 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 ‘eJournals’ that enabled rapid updating of medication lists during subsequent clinical visits.
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 patient-reported medication regimens. The primary outcome was proportion of medications per patient with unexplained discrepancies.
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, 95% CI 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% CI 0.10 to 0.92, p=0.04).
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 (NCT00251875).
Keywords: Heath records, personal; medication errors; ambulatory care; quality improvement; patient safety; clinical decision support; hospital medicine; personal health records; diabetes; medical informatics; decision support; healthcare information technology; knowledge management; electronic health records; clinical informatics
Medication-related morbidity and mortality is estimated to result in $76 billion dollars in total costs annually.1 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.
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, PG) to address medication issues.10 11 The design of the PG Medications Module, preliminary usage data, and patient and provider impressions of the application have been described previously.12 This manuscript reports how the module affected medication safety outcomes and patient–provider communication. We hypothesized that the module would reduce discrepancies between patient-reported and EHR-documented medication regimens and reduce potential and preventable/ameliorable ADEs.
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 Partners ambulatory clinics is the Longitudinal Medical Record (LMR), an internally developed, certified EHR.13 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 (NCT00251875).
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 PG, 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.
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 PCP visit following a predefined protocol. The survey was developed based on previous studies regarding medication discrepancies and ADEs 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 ADEs. 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 six-point confidence scale, using the Naranjo algorithm as a guide.16 In the event of a likely ADE, adjudicators decided on its severity (significant, serious, life-threatening, or fatal), whether the ADE could have been prevented, and if not, whether the ADE 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.
The primary outcome was discordance between documented and reported medication regimens, that is, the proportion of each 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 multinomial model since the outcome was an eight-level ordinal response.
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 α of 0.05, estimated cluster size of three patients per PCP, and intra-class correlation coefficient of 0.10. Two-sided p values <0.05 were considered significant. All analyses were conducted using SAS V.9.2.
During the study period, among 121 046 patients in primary care practices using PG, 21 533 (18%) had active accounts and were invited to participate (figure 1). Of these, 3979 (18%) consented to be in the study. Among the 1761 patients in the intervention arm, 1053 (60%) had an eligible primary care visit and were invited to submit a medications eJournal, of whom 743 (71%) submitted one. Of those, 267 were selected to be in the medications sub-study. These patients were matched to 274 patients in the control arm, of whom 170 had been invited to complete a health maintenance eJournal during the study period and 135 (79%) submitted one. Eligible patients who did not participate in the sub-study for various reasons (see figure 1) were on fewer medications and had fewer prior visits compared with those who did participate; these differences were similar in both arms of the study (see table A1 in the supplementary online appendix).
Figure 1
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. †‘Opted out’ includes (more ...)
Table 1 compares patients in the two arms of the main study and in the medications sub-study. Among patients in the medications sub-study, those in the intervention arm were older and less often white, with higher median income by zip code and on more medications than control patients. The difference in number of medications was exaggerated when intervention patients were compared with control patients who submitted a health maintenance eJournal (5.6 vs 4.7) as opposed to all control patients (5.6 vs 4.9). Among patients in the intervention arm of the main study, those who submitted medications eJournals were prescribed more medications than non-submitters (5.2 vs 4.8, p=0.046). Among patients in the active control arm, there were no differences in the number of documented medications between those who submitted health maintenance eJournals and those who did not (4.6 vs 4.4, p=0.41; data not shown).
Table 1
Table 1
Comparison of study participants in the active control and intervention arms
In the intervention arm, 78% of patients who were invited to complete a medications eJournal opened it, and 72% completed and submitted it. The primary care practices opened 77% of submitted medications eJournals.
Three weeks after the index primary care visit, the average medication regimen was 51% discordant among control patients. In other words, on average, slightly less than half the medications reported by patients and documented in the LMR completely agreed with each other. Unexplained discrepancies between documented and reported medication regimens included missing medications (ie, reported to be taken by the patient but not in the LMR, in 36% of patients), differences in dose and frequency (46%), and additional medications (ie, documented in the LMR but not being taken by patients (68%)). In the intervention arm, the discordance of the medication list 3 weeks after the visit was significantly lower at 42% (adjusted OR 0.71, 95% CI 0.54 to 0.94, p=0.01; table 2). This difference was mainly driven by fewer additional medications (57% vs 68% of patients) and less so by fewer missing medications (29% vs 36% of patients).
Table 2
Table 2
Results of medication safety evaluation
There was a borderline but non-significant difference in the number of unexplained discrepancies per patient with potential for harm in the two study groups (0.24 per patient in the intervention arm vs 0.34 in the control arm, adjusted p=0.059). However, there was a significant difference in the number of discrepancies per patient with potential for severe harm (0.03 vs 0.08 per patient, adjusted RR 0.31, 95% CI 0.10 to 0.92, p=0.04). There was no significant difference in the number of preventable or ameliorable ADEs per patient between the two study arms or in the duration of ameliorable ADEs (table 2).
In secondary analyses restricted to patients who submitted eJournals, the direction and magnitude of effect sizes for all study outcomes were similar (see table A2 in the supplementary online appendix). Differences between study and control arms in the discordance of medication regimens was virtually identical to the primary analysis (42% vs 50%) and remained statistically significant in unadjusted and adjusted analyses, but the difference in the number of discrepancies with potential for severe harm was no longer statistically significant. In another analysis in which clustering was performed at the practice level instead of the provider level, the results were again similar except that the effect of the intervention on discrepancies with potential for any (not just severe) harm was statistically significant (see table A3 in the supplementary online appendix).
Of 1423 patients who submitted an eJournal in either arm of the main study, 1278 (90%) completed a follow-up survey. Compared with patients in the active control group, patients in the intervention arm more often reported that they always told their PCP about all prescribed medications they took, even those prescribed by other doctors (table 3). In addition, more patients in the intervention arm reported that they told their doctor about any medication-related problems or new symptoms.
Table 3
Table 3
Survey results among patients who submitted eJournals
In this study, patients using an electronic application focused on medications within a PHR had greater concordance between documented and patient-reported medication regimens and fewer unexplained discrepancies with potential for severe harm. There was no difference in the total number of preventable or ameliorable ADEs or in the duration of ameliorable ADEs.
We believe that the PG Medications Module encouraged patients to review their medication regimen as displayed in the ambulatory EHR and note discrepancies, and it activated PCPs to update the medication list. The results of the survey suggest that it may also have led to discussions about medication adherence and potential side effects. The intervention seemed particularly effective at decreasing unexplained discrepancies with potential for severe harm, perhaps because these caught the attention of PCPs and led to action.
On the other hand, some intervention patients still experienced potentially harmful medication discrepancies (0.24 per patient in the intervention arm). That 71% of eligible patients submitted a medications eJournal suggests that it was generally well accepted, but further improvements in usability might have increased that rate. We know that PCPs and practice staff did not open medications eJournals in 23% of cases, and they may not have accurately documented all they discussed with patients. Practices were expected to use the module when they signed up for the Prepare for Care study, but this decision was made by practice leaders with variable buy-in from individual providers. PCPs were trained in the use of the module and were encouraged to use it, but in the end, use was left to provider discretion. The module was designed to integrate with provider workflow (eg, automatically appearing instead of the usual medications screen when an eJournal had been submitted), but apparently some PCPs never opened a medications screen at all during some visits (eg, only opening the note-writing screen and importing the medication list into their note). Also, to our knowledge, the module was rarely used by other practice staff.
We were not surprised that the total number of preventable ADEs did not differ in the two study arms. Only a small number of ADEs were judged to be completely preventable (1.5% of all ADEs in this study), and causes are often multifactorial, requiring multi-faceted interventions. More feasible is decreasing the duration of ameliorable ADEs, but because only a proportion of ADEs in this study were ameliorable (58%), the study was likely under-powered to detect a difference.
Most EHR-connected PHRs facilitate patient–provider communication, completion of administrative tasks, and sharing of portions of the EHR with patients. Studies of such PHRs have shown effects on patient–physician collaboration18 and patient access to care.19 Studies of other interventions of ambulatory medication safety have focused on pharmacist interventions and mostly provider-centric health information technology (HIT) tools such as e-prescribing.20 21 To our knowledge, this study is one of the few examples of a patient-centric HIT tool focused on improving medication safety and one of the only successful ones.
This study has several limitations. First, it should be emphasized that this was an on-treatment analysis that evaluated a small proportion of patients compared with the over 120 000 patients theoretically eligible to participate in this study. To estimate the effect in all patients, the 9% absolute difference in the discordance rate should at least be multiplied by the 71% adherence rate (those who actually submitted a medications eJournal), that is, 6.4%. Because adherence might be less among the 82% of patients who had a PG account but chose not to participate in the study, the actual effect size would likely be smaller than that. Finally, this intervention was only effective among active PG users. At the time of the study, the rate of PG use was 18%. As of March 2011, with marketing and time, the rate was 35% and continues to increase.
This study has several other limitations. As suggested above, the low enrollment rate in PG in general and with this study in particular limits the generalizability of the findings, perhaps to particularly motivated early adopters of technology. The large number of people who did not participate in the medications sub-study also limits generalizability, although at least differences between participants and non-participants were similar in the two study arms. Second, this study was conducted in one medical system using internally developed software. However, the 11 primary care practices in this study had considerable heterogeneity, and such technology could be adopted by other EHRs that have associated PHRs. Third, as noted above, the sample size of the medications sub-study was small and had limited power to detect clinically important differences in some secondary outcomes. Fourth, we did not compare patient-reported medication regimens or documented regimens to a gold standard, that is, what a patient's providers collectively think the patient should be taking. This regimen does not usually exist anywhere and would require interviews with all providers (ideally, simultaneously) at the time of data collection.
Finally, there is no ideal comparison group to the patients in the intervention arm. On the one hand, a comparison of patients who submitted eJournals in both arms adjusts for potential confounding due to factors that lead to eJournal submission in general, such as comfort with technology and willingness to communicate asynchronously with providers. However, the reasons why a patient may submit a medications eJournal are likely different than the reasons why a patient may submit an eJournal about family history or health maintenance. Specifically, patients who submit a medications eJournal are prescribed more medications than those who do not, and they may be more likely to have potentially harmful medication discrepancies, an effect not seen among those offered a health maintenance eJournal. This is why we instead chose to match patients based on visit date, visit type, and comparable practice. In the secondary analysis of eJournal submitters only, the effects of the intervention on medication discordance were virtually identical to the main effects; the loss of statistical significance in discrepancies with potential for severe harm may reflect the difference described above between eJournal submitters and non-submitters in the different study arms, a loss of statistical power, unmeasured confounding, or a combination of these factors.
Future work is needed to increase patient enrollment in PHRs and to close the well-documented ‘digital divide’ between users and non-users of HIT.22 Improvements to the design of the patient side of the application might further increase submission of eJournals. Further work is also needed to better integrate the Medications Module with provider workflow, perhaps with further improvements to the PCP side of the application (eg, prompting review of the module from the EMR notes screen) and through changes in practice design (eg, team-based care in which other clinical staff members review medications eJournal information with patients prior to final vetting of the information by PCPs). Issues of provider expectations, training, and buy-in would also need to be addressed to maximize the effectiveness of the intervention. From a research standpoint, larger studies conducted in various healthcare systems and using multiple EHR/PHR platforms would establish the role of this kind of technology with greater generalizability.
In conclusion, when used, an interactive tool within a PHR focused on medications was able to decrease the discordance of documented and patient-reported medication regimens and reduce discrepancies with potential for severe harm. This technology shows promise, and further studies should be conducted integrating this kind of tool into other EHR platforms and evaluating its effects on medication safety.
Supplementary Material
Supporting Statement
Supplementary Data
We would like to thank Anuj K Dalal, MD, for his assistance with adjudication, Hannah Pham, Alexis Tumolo, Andrea Melnikas, Helen Lo, Irina Litvak, and Madeline McCarthy for their assistance in conducting patient phone surveys, and Erin Hartman, MS, for her editorial review of the manuscript.
Funding: This work was supported by AHRQ Grant R01-HS013660-02: Shared Online Health Records for Patient Safety and Quality. Dr Schnipper was also supported by a mentored career development award from the National Heart, Lung, and Blood Institute (1 K08 HL072806-01). AHRQ and NHLBI were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
Competing interests: Drs Schnipper, Gandhi, and Poon are consultants to QuantiaMD, for whom they have created on-line educational materials for both providers and patients regarding patient safety, including medication safety. The findings of this study are not a part of those materials and the work with QuantiaMD in no way influenced the content of this manuscript. Dr Schnipper has received grant funding from Sanofi Aventis for an investigator-initiated study to design and evaluate an intensive discharge and follow-up intervention in patients with diabetes. The funder has no role in the design of the study, and the content is not relevant to the current manuscript. Dr Poon is a consultant to Becton, Dickinson, and Company, for whom he has presented at an internal educational forum. The findings of this study are not a part of those materials and the work with Becton, Dickinson, and Company in no way influenced the content of this manuscript. No other authors have any financial interests in the subject matter or materials discussed in the manuscript.
Ethics approval: This study was approved by the Partners HealthCare institutional review board.
Provenance and peer review: Not commissioned; externally peer reviewed.
1. Johnson JA, Bootman JL. Drug-related morbidity and mortality. A cost-of-illness model. Arch Intern Med 1995;155:1949–56. [PubMed]
2. Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA 1995;274:29–34. [PubMed]
3. Gandhi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care. N Engl J Med 2003;348:1556–64. [PubMed]
4. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med 2006;166:565–71. [PubMed]
5. Insull W. The problem of compliance to cholesterol altering therapy. J Intern Med 1997;241:317–25. [PubMed]
6. McDermott MM, Schmitt B, Wallner E. Impact of medication nonadherence on coronary heart disease outcomes. A critical review. Arch Intern Med 1997;157:1921–9. [PubMed]
7. Vermeire E, Hearnshaw H, Van Royen P, et al. Patient adherence to treatment: three decades of research. A comprehensive review. J Clin Pharm Ther 2001;26:331–42. [PubMed]
8. World Health Organization (WHO) Adherence to Long-Term Therapies: Evidence for Action. Geneva: WHO, 2003.
9. Tang PC, Ash JS, Bates DW, et al. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc 2006;13:121–6. [PMC free article] [PubMed]
10. Wald JS, Middleton B, Bloom A, et al. A patient-controlled journal for an electronic medical record: issues and challenges. Stud Health Technol Inform 2004;107:1166–72. [PubMed]
11. Wald JS, Businger A, Gandhi TK, et al. Implementing practice-linked pre-visit electronic journals in primary care: patient and physician use and satisfaction. J Am Med Inform Assoc 2010;17:502–6. [PMC free article] [PubMed]
12. Schnipper JL, Gandhi TK, Wald JS, et al. Design and implementation of a web-based patient portal linked to an electronic health record designed to improve medication safety: the Patient Gateway medications module. Inform Prim Care 2008;16:147–55. [PubMed]
13. Certification Commission for Healthcare Information Technology Partners HealthCare System Longitudinal Medical Record (LMR) 5.1.1. 2006. (accessed 8 Dec 2009).
14. Schnipper JL, Hamann C, Ndumele CD, et al. Effect of an electronic medication reconciliation application and process redesign on potential adverse drug events: a cluster-randomized trial. Arch Intern Med 2009;169:771–80. [PubMed]
15. Schnipper JL, Roumie CL, Cawthon C, et al. Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study. Circ Cardiovasc Qual Outcomes 2010;3:212–19. [PMC free article] [PubMed]
16. Leape LL, Brennan TA, Laird N, et al. The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N Engl J Med 1991;324:377–84. [PubMed]
17. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med 2004;23:2937–60. [PubMed]
18. Grant RW, Wald JS, Schnipper JL, et al. Practice-linked online personal health records for type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med 2008;168:1776–82. [PubMed]
19. Ralston JD, Martin DP, Anderson ML, et al. Group health cooperative's transformation toward patient-centered access. Med Care Res Rev 2009;66:703–24. [PubMed]
20. Devine EB, Hansen RN, Wilson-Norton JL, et al. The impact of computerized provider order entry on medication errors in a multispecialty group practice. J Am Med Inform Assoc 2010;17:78–84. [PMC free article] [PubMed]
21. Kaushal R, Kern LM, Barron Y, et al. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med 2010;25:530–6. [PMC free article] [PubMed]
22. Yamin CK, Emani S, Williams DH, et al. The digital divide in adoption and use of a personal health record. Arch Intern Med 2011;171:568–74. [PubMed]
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