The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Straight to the point: evaluation of a Point of Care Information (POCI) resource in answering disease-related questions.

Objective: To evaluate the ability of DynaMedex, an evidence-based drug and disease Point of Care Information (POCI) resource, in answering clinical queries using keyword searches. Methods: Real-world disease-related questions compiled from clinicians at an academic medical center, DynaMedex search query data, and medical board review resources were categorized into five clinical categories (complications & prognosis, diagnosis & clinical presentation, epidemiology, prevention & screening/monitoring, and treatment) and six specialties (cardiology, endocrinology, hematology-oncology, infectious disease, internal medicine, and neurology). A total of 265 disease-related questions were evaluated by pharmacist reviewers based on if an answer was found (yes, no), whether the answer was relevant (yes, no), difficulty in finding the answer (easy, not easy), cited best evidence available (yes, no), clinical practice guidelines included (yes, no), and level of detail provided (detailed, limited details). Results: An answer was found for 259/265 questions (98%). Both reviewers found an answer for 241 questions (91%), neither found the answer for 6 questions (2%), and only one reviewer found an answer for 18 questions (7%). Both reviewers found a relevant answer 97% of the time when an answer was found. Of all relevant answers found, 68% were easy to find, 97% cited best quality of evidence available, 72% included clinical guidelines, and 95% were detailed. Recommendations for areas of resource improvement were identified. Conclusions: The resource enabled reviewers to answer most questions easily with the best quality of evidence available, providing detailed answers and clinical guidelines, with a high level of replication of results across users.

Care coordination gaps due to lack of interoperability in the United States: a qualitative study and literature review.

BACKGROUND: Health information technology (HIT) could improve care coordination by providing clinicians remote access to information, improving legibility, and allowing asynchronous communication, among other mechanisms. We sought to determine, from a clinician perspective, how care is coordinated and to what extent HIT is involved when transitioning patients between emergency departments, acute care hospitals, skilled nursing facilities, and home health agencies in settings across the United States. METHODS: We performed a qualitative study with clinicians and information technology professionals from six regions of the U.S. which were chosen as national leaders in HIT. We analyzed data through a two person consensus approach, assigning responses to each of nine care coordination activities. We also conducted a literature review of MEDLINE®, CINAHL®, and Embase, analyzing results of studies that examined interventions to improve information transfer during transitions of care. RESULTS: We enrolled 29 respondents from 17 organizations and conducted six focus groups. Respondents reported how HIT is currently used for care coordination activities. HIT is currently used to monitor patients and to align systems-level resources with population needs. However, we identified multiple areas where the lack of interoperability leads to inefficient processes and missing data. Additionally, the literature review identified ten intervention studies that address information transfer, seven of which employed HIT and three of which utilized other communication methods such as telephone calls, faxed records, and nurse case management. CONCLUSIONS: Significant care coordination gaps exist due to the lack of interoperability across the United States. We must design, evaluate, and incentivize the use of HIT for care coordination. We should focus on the domains where we found the largest gaps: information transfer, systems to monitor patients, tools to support patients' self-management goals, and tools to link patients and their caregivers with community resources.

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients. STUDY DESIGN: Retrospective cohort study. METHODS: The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients. RESULTS: Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct. CONCLUSIONS: The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.

Using a Patient Portal to Screen Patients for Symptoms After Starting New Medications.

OBJECTIVE: Conduct systematic proactive pharmacovigilance screening for symptoms patients experienced after starting new medications using an electronic patient portal. We aimed to design and test the feasibility of the system, measure patient response rates, provide any needed support for patients experiencing potentially drug-related problems, and describe types of symptoms and problems patients report. METHODS: We created an automated daily report of all new prescriptions, excluding likely non-new and various OTC meds, and sent invitations via patient portal inviting patients to inquire if they had started the medication, and if "yes," inquire if they had they experienced any new symptoms that could be potential adverse drug effects. Reported symptoms were classified by clinical pharmacists using SOC MeDra taxonomy, and patients were offered follow-up and support as desired and needed. RESULTS: Of 11,724 included prescriptions for 9360 unique patients, 2758 (29.4%) patients responded. Of 2616 unique medication starts, patients reported at least 1 new symptom that represented a potential adverse drug reaction (ADR) in 678/2616 (25.9%). Nearly one-third of those experiencing new symptoms (30.3%) reported 2 or more new symptoms after initiating the drug. GI disorders accounted for 30% of the total reported ADRs. CONCLUSIONS: Systematic portal-based surveillance for potential adverse drug reactions was feasible, had higher response rates than other methods (such as automated interactive phone calling), and uncovered rates of potential ADRs (roughly 1 in 4 patients) consistent with other methods/studies.

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

A Multiyear Survey Evaluating Clinician Electronic Health Record Satisfaction.

OBJECTIVES: We assessed how clinician satisfaction with a vendor electronic health record (EHR) changed over time in the 4 years following the transition from a homegrown EHR system to identify areas for improvement. METHODS: We conducted a multiyear survey of clinicians across a large health care system after transitioning to a vendor EHR. Eligible clinicians from the first institution to transition received a survey invitation by email in fall 2016 and then eligible clinicians systemwide received surveys in spring 2018 and spring 2019. The survey included items assessing ease/difficulty of completing tasks and items assessing perceptions of the EHR's value, usability, and impact. One item assessing overall satisfaction and one open-ended question were included. Frequencies and means were calculated, and comparison of means was performed between 2018 and 2019 on all clinicians. A multivariable generalized linear model was performed to predict the outcome of overall satisfaction. RESULTS: Response rates for the surveys ranged from 14 to 19%. The mean response from 3 years of surveys for one institution, Brigham and Women's Hospital, increased for overall satisfaction between 2016 (2.85), 2018 (3.01), and 2019 (3.21, p < 0.001). We found no significant differences in mean response for overall satisfaction between all responders of the 2018 survey (3.14) and those of the 2019 survey (3.19). Systemwide, tasks rated the most difficult included "Monitoring patient medication adherence," "Identifying when a referral has not been completed," and "Making a list of patients based on clinical information (e.g., problem, medication)." Clinicians disagreed the most with "The EHR helps me focus on patient care rather than the computer" and "The EHR allows me to complete tasks efficiently." CONCLUSION: Survey results indicate room for improvement in clinician satisfaction with the EHR. Usability of EHRs should continue to be an area of focus to ease clinician burden and improve clinician experience.

Pharmacists' role in detection and evaluation of adverse drug reactions: Developing proactive systems for pharmacosurveillance.

PURPOSE: To identify current challenges in detection of medication-related symptoms, and review technology-based opportunities to increase the patient-centeredness of postmarketing pharmacosurveillance to promote more accountable, safer, patient-friendly, and equitable medication prescribing. SUMMARY: Pharmacists have an important role to play in detection and evaluation of adverse drug reactions (ADRs). The pharmacist's role in medication management should extend beyond simply dispensing drugs, and this article delineates the rationale and proactive approaches for pharmacist detection and assessment of ADRs. We describe a stepwise approach for assessment, best practices, and lessons learned from a pharmacist-led randomized trial, the CEDAR (Calling for Detection of Adverse Drug Reactions) project. CONCLUSION: Health systems need to be redesigned to more fully utilize health information technologies and pharmacists in detecting and responding to ADRs.

Correction: Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

[This corrects the article DOI: 10.2196/43960.].

Performance of a Web-Based Reference Database With Natural Language Searching Capabilities: Usability Evaluation of DynaMed and Micromedex With Watson.

BACKGROUND: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue. OBJECTIVE: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development. METHODS: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague. RESULTS: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds). CONCLUSIONS: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users.

Randomized controlled trial of health maintenance reminders provided directly to patients through an electronic PHR.

BACKGROUND: Provider and patient reminders can be effective in increasing rates of preventive screenings and vaccinations. However, the effect of patient-directed electronic reminders is understudied. OBJECTIVE: To determine whether providing reminders directly to patients via an electronic Personal Health Record (PHR) improved adherence to care recommendations. DESIGN: We conducted a cluster randomized trial without blinding from 2005 to 2007 at 11 primary care practices in the Partners HealthCare system. PARTICIPANTS: A total of 21,533 patients with access to a PHR were invited to the study, and 3,979 (18.5%) consented to enroll. INTERVENTIONS: Patients in the intervention arm received health maintenance (HM) reminders via a secure PHR "eJournal," which allowed them to review and update HM and family history information. Patients in the active control arm received access to an eJournal that allowed them to input and review information related to medications, allergies and diabetes management. MAIN MEASURES: The primary outcome measure was adherence to guideline-based care recommendations. KEY RESULTS: Intention-to-treat analysis showed that patients in the intervention arm were significantly more likely to receive mammography (48.6% vs 29.5%, p = 0.006) and influenza vaccinations (22.0% vs 14.0%, p = 0.018). No significant improvement was observed in rates of other screenings. Although Pap smear completion rates were higher in the intervention arm (41.0% vs 10.4%, p < 0.001), this finding was no longer significant after excluding women's health clinics. Additional on-treatment analysis showed significant increases in mammography (p = 0.019) and influenza vaccination (p = 0.015) for intervention arm patients who opened an eJournal compared to control arm patients, but no differences for any measure among patients who did not open an eJournal. CONCLUSIONS: Providing patients with HM reminders via a PHR may be effective in improving some elements of preventive care.

Usability of a novel clinician interface for genetic results.

The complexity and rapid growth of genetic data demand investment in information technology to support effective use of this information. Creating infrastructure to communicate genetic information to healthcare providers and enable them to manage that data can positively affect a patient's care in many ways. However, genetic data are complex and present many challenges. We report on the usability of a novel application designed to assist providers in receiving and managing a patient's genetic profile, including ongoing updated interpretations of the genetic variants in those patients. Because these interpretations are constantly evolving, managing them represents a challenge. We conducted usability tests with potential users of this application and reported findings to the application development team, many of which were addressed in subsequent versions. Clinicians were excited about the value this tool provides in pushing out variant updates to providers and overall gave the application high usability ratings, but had some difficulty interpreting elements of the interface. Many issues identified required relatively little development effort to fix suggesting that consistently incorporating this type of analysis in the development process can be highly beneficial. For genetic decision support applications, our findings suggest the importance of designing a system that can deliver the most current knowledge and highlight the significance of new genetic information for clinical care. Our results demonstrate that using a development and design process that is user focused helped optimize the value of this application for personalized medicine.

Expert Consensus on Currently Accepted Measures of Harm.

BACKGROUND: Twenty-five years after the seminal work of the Harvard Medical Practice Study, the numbers and specific types of health care measures of harm have evolved and expanded. Using the World Café method to derive expert consensus, we sought to generate a contemporary list of triggers and adverse event measures that could be used for chart review to determine the current incidence of inpatient and outpatient adverse events. METHODS: We held a modified World Café event in March 2018, during which content experts were divided into 10 tables by clinical domain. After a focused discussion of a prepopulated list of literature-based triggers and measures relevant to that domain, they were asked to rate each measure on clinical importance and suitability for chart review and electronic extraction (very low, low, medium, high, very high). RESULTS: Seventy-one experts from 9 diverse institutions attended (primary acceptance rate, 72%). Of 525 total triggers and measures, 67% of 391 measures and 46% of 134 triggers were deemed to have high or very high clinical importance. For those triggers and measures with high or very high clinical importance, 218 overall were deemed to be highly amenable to chart review and 198 overall were deemed to be suitable for electronic surveillance. CONCLUSIONS: The World Café method effectively prioritized measures/triggers of high clinical importance including those that can be used in chart review, which is considered the gold standard. A future goal is to validate these measures using electronic surveillance mechanisms to decrease the need for chart review.

Personal Formularies of Primary Care Physicians Across 4 Health Care Systems.

Importance: More conservative prescribing has the potential to reduce adverse drug events and patient harm and cost; however, no method exists defining the extent to which individual clinicians prescribe conservatively. One potential domain is prescribing a more limited number of drugs. Personal formularies-defined as the number and mix of unique, newly initiated drugs prescribed by a physician-may enable comparisons among clinicians, practices, and institutions. Objectives: To develop a method of defining primary care physicians' personal formularies and examine how they differ among primary care physicians at 4 institutions; evaluate associations between personal formularies and patient, physician, and practice site characteristics; and empirically derive and examine the variability of the top 200 core drugs prescribed at the 4 sites. Design, Setting, and Participants: This retrospective cohort study was conducted at 4 US health care systems among 4655 internal and family medicine physicians and 4 930 707 patients who had at least 1 visit to these physicians between January 1, 2017, and December 31, 2018. Exposures: Personal formulary size was defined as the number of unique, newly initiated drugs. Main Outcomes and Measures: Personal formulary size and drugs used, physician and patient characteristics, core drugs, and analysis of selected drug classes. Results: The study population included 4655 primary care physicians (2274 women [48.9%]; mean [SD] age, 48.5 [4.4] years) and 4 930 707 patients (16.5% women; mean [SD] age, 51.9 [8.3] years). There were 41 378 903 outpatient prescriptions written, of which 9 496 766 (23.0%) were new starts. Institution median personal formulary size ranged from 150 (interquartile range, 82.0-212.0) to 296 (interquartile range, 230.0-347.0) drugs. In multivariable modeling, personal formulary size was significantly associated with panel size (total number of unique patients with face-to-face encounters during the study period; 1.2 medications per 100 patients), physician's total number of encounters (5.7 drugs per 10% increase), and physician's sex (-6.2 drugs per 100 patients for female physicians). There were 1527 unique, newly prescribed drugs across the 4 sites. Fewer than half the drugs (626 [41.0%]) were used at every site. Physicians' prescribing of drugs from a pooled core list varied from 0% to 100% of their prescriptions. Conclusions and Relevance: Personal formularies, measured at the level of individual physicians and institutions, reveal variability in size and mix of drugs. Similarly, defining a list of commonly prescribed core drugs in primary care revealed interphysician and interinstitutional differences. Personal formularies and core medication lists enable comparisons and may identify outliers and opportunities for safer and more appropriate prescribing.

Relationship between use of electronic health record features and health care quality: results of a statewide survey.

BACKGROUND: Electronic health records (EHRs) are widely viewed as useful tools for supporting the provision of high quality healthcare. However, evidence regarding their effectiveness for this purpose is mixed, and existing studies have generally considered EHR usage a binary factor and have not considered the availability and use of specific EHR features. OBJECTIVE: To assess the relationship between the use of an EHR and the use of specific EHR features with quality of care. RESEARCH DESIGN: A statewide mail survey of physicians in Massachusetts conducted in 2005. The results of the survey were linked with Healthcare Effectiveness Data and Information Set (HEDIS) quality measures, and generalized linear regression models were estimated to examine the associations between the use of EHRs and specific EHR features with quality measures, adjusting for physician practice characteristics. SUBJECTS: A stratified random sample of 1884 licensed physicians in Massachusetts, 1345 of whom responded. Of these, 507 had HEDIS measures available and were included in the analysis (measures are only available for primary care providers). MEASURE: Performance on HEDIS quality measures. RESULTS: The survey had a response rate of 71%. There was no statistically significant association between use of an EHR as a binary factor and performance on any of the HEDIS measure groups. However, there were statistically significant associations between the use of many, but not all, specific EHR features and HEDIS measure group scores. The associations were strongest for the problem list, visit note and radiology test result EHR features and for quality measures relating to women's health, colon cancer screening, and cancer prevention. For example, users of problem list functionality performed better on women's health, depression, colon cancer screening, and cancer prevention measures, with problem list users outperforming nonusers by 3.3% to 9.6% points on HEDIS measure group scores (all significant at the P < 0.05 level). However, these associations were not universal. CONCLUSIONS: Consistent with past studies, there was no significant relationship between use of EHR as a binary factor and performance on quality measures. However, availability and use of specific EHR features by primary care physicians was associated with higher performance on certain quality measures. These results suggest that, to maximize health care quality, developers, implementers and certifiers of EHRs should focus on increasing the adoption of robust EHR systems and increasing the use of specific features rather than simply aiming to deploy an EHR regardless of functionality.

Self-reported familiarity with acute respiratory infection guidelines and antibiotic prescribing in primary care.

OBJECTIVE: Familiarity with guidelines is generally thought to be associated with guideline implementation, adherence and improved quality of care. We sought to determine if self-reported familiarity with acute respiratory infection (ARI) antibiotic treatment guidelines was associated with reduced or more appropriate antibiotic prescribing for ARIs in primary care. PARTICIPANTS: and MAIN OUTCOME MEASURES: We surveyed primary care clinicians about their familiarity with ARI antibiotic treatment guidelines and linked responses to administrative diagnostic and prescribing data for non-pneumonia ARI visits. RESULTS: Sixty-five percent of clinicians responded to the survey question about guideline familiarity. There were 208 survey respondents who had ARI patient visits during the study period. Respondents reported being 'not at all' (7%), 'somewhat' (30%), 'moderately' (45%) or 'extremely' (18%) familiar with the guidelines. After dichotomizing responses, compared with clinicians who reported being less familiar with the guidelines, clinicians who reported being more familiar with the guidelines had higher rates of antibiotic prescribing for all ARIs combined (46% versus 38%; n = 11 164; P < 0.0001), for antibiotic-appropriate diagnoses (69% versus 59%; n = 3213; P < 0.0001) and for non-antibiotic appropriate diagnoses (38% versus 28%; n = 7951; P < 0.0001). After adjusting for potential confounders, self-reported guideline familiarity was an independent predictor of increased antibiotic prescribing (odds ratio, 1.36; 95% confidence interval, 1.25-1.48). CONCLUSIONS: Self-reported familiarity with an ARI antibiotic treatment guideline was, seemingly paradoxically, associated with increased antibiotic prescribing. Self-reported familiarity with guidelines should not be assumed to be associated with consistent guideline adherence or higher quality of care.

Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation.

BACKGROUND: Clinical decision support (CDS) alerting tools can identify and reduce medication errors. However, they are typically rule-based and can identify only the errors previously programmed into their alerting logic. Machine learning holds promise for improving medication error detection and reducing costs associated with adverse events. This study evaluates the ability of a machine learning system (MedAware) to generate clinically valid alerts and estimates the cost savings associated with potentially prevented adverse events. METHODS: Alerts were generated retrospectively by the MedAware system on outpatient data from two academic medical centers between 2009 and 2013. MedAware alerts were compared to alerts in an existing CDS system. A random sample of 300 alerts was selected for medical record review. Frequency and severity of potential outcomes of alerted medication errors of medium and high clinical value were estimated, along with associated health care costs of these potentially prevented adverse events. RESULTS: A total of 10,668 alerts were generated. Overall, 68.2% of MedAware alerts would not have been generated by the existing CDS system. Ninety-two percent of a random sample of the chart-reviewed alerts were accurate based on structured data available in the record, and 79.7% were clinically valid. Estimated cost of adverse events potentially prevented in an outpatient setting was more than $60 per drug alert and $1.3 million when extrapolating study findings to the full patient population. CONCLUSION: A machine learning system identified clinically valid medication error alerts that might otherwise be missed with existing CDS systems. Estimates show potential for cost savings associated with potentially prevented adverse events.