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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.
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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.
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Serviço Hospitalar de Emergência , Hospitalização , Aprendizado de Máquina , Multimorbidade , Humanos , Feminino , Idoso , Estudos Retrospectivos , Masculino , Hospitalização/estatística & dados numéricos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Idoso de 80 Anos ou mais , Medição de Risco , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Modelos LogísticosRESUMO
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.
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Sistemas Automatizados de Assistência Junto ao Leito , Humanos , Medicina Baseada em EvidênciasRESUMO
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.
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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.
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Assistência Ambulatorial , Registros Eletrônicos de Saúde , Segurança do Paciente , Humanos , Feminino , Pessoa de Meia-Idade , Masculino , Estudos Retrospectivos , Adulto , Idoso , Massachusetts , Adolescente , Adulto JovemRESUMO
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.
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Atenção à Saúde , Registros Eletrônicos de Saúde , Humanos , Feminino , Inquéritos e Questionários , Assistência ao Paciente , Satisfação PessoalRESUMO
[This corrects the article DOI: 10.2196/43960.].
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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.
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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.).
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Atenção à Saúde , Hospitalização , Erros Médicos , Dano ao Paciente , Segurança do Paciente , Humanos , Atenção à Saúde/normas , Atenção à Saúde/estatística & dados numéricos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Hospitalização/estatística & dados numéricos , Pacientes Internados , Erros Médicos/prevenção & controle , Erros Médicos/estatística & dados numéricos , Segurança do Paciente/normas , Estudos Retrospectivos , Dano ao Paciente/prevenção & controle , Dano ao Paciente/estatística & dados numéricosRESUMO
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.
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Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Informática Médica , Humanos , Farmacêuticos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/diagnóstico , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Prescrições de Medicamentos , Papel ProfissionalRESUMO
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 4â¯930â¯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 4â¯930â¯707 patients (16.5% women; mean [SD] age, 51.9 [8.3] years). There were 41â¯378â¯903 outpatient prescriptions written, of which 9â¯496â¯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.
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Atenção à Saúde/estatística & dados numéricos , Prescrições de Medicamentos/estatística & dados numéricos , Médicos de Atenção Primária/estatística & dados numéricos , Padrões de Prática Médica/estatística & dados numéricos , Adulto , Feminino , Formulários Farmacêuticos como Assunto , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Estados UnidosRESUMO
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.
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Pacientes Internados , Consenso , Humanos , IncidênciaAssuntos
Betacoronavirus , Doença Crônica/epidemiologia , Doença Crônica/tendências , Infecções por Coronavirus/epidemiologia , Gerenciamento Clínico , Epidemias , Pneumonia Viral/epidemiologia , COVID-19 , Doença Crônica/terapia , Infecções por Coronavirus/terapia , Testes Diagnósticos de Rotina/tendências , Prescrições de Medicamentos , Humanos , Pandemias , Pneumonia Viral/terapia , SARS-CoV-2RESUMO
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.
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Sistemas de Apoio a Decisões Clínicas , Sistemas de Registro de Ordens Médicas , Preparações Farmacêuticas , Redução de Custos , Humanos , Aprendizado de Máquina , Erros de Medicação/prevenção & controle , Estudos RetrospectivosRESUMO
PURPOSE: To examine the extent to which outpatient clinicians currently document drug indications in prescription instructions. METHODS: Free-text sigs were extracted from all outpatient prescriptions generated by the computerized prescriber order entry system of a major academic institution during a 5-year period. Natural language processing was used to identify drug indications. The data set was analyzed to determine the rates at which prescribers included indications. It was stratified by provider specialty, drug class, and specific medications, to determine how often these indications were in prescriptions for as-needed (PRN) versus non-PRN medications. RESULTS: During the study period, 4,356,086 prescriptions were ordered. Indications were included in 322,961 orders (7.41%). From these orders, 249,262 indications (77.18%) were written for PRN orders. Although internal medicine prescribers generated the highest number of medication orders, they included indications in only 6.26% of their prescriptions, whereas orthopedic surgery providers had the highest rate of documenting indications (33.41%). Pain was the most common indication, accounting for 30.35% of all documented indications. The drug class with the highest number of sigs-containing indications was narcotic analgesics. Non-PRN chronic medication prescriptions rarely included the indication. CONCLUSION: Prescribers rarely included drug indications in electronic free-text prescription instructions, and, when they did, it was mostly for PRN uses such as pain.
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Assistência Ambulatorial/estatística & dados numéricos , Prescrições de Medicamentos/estatística & dados numéricos , Sistemas de Registro de Ordens Médicas/estatística & dados numéricos , Assistência Ambulatorial/normas , Conjuntos de Dados como Assunto , Prescrições de Medicamentos/normas , Humanos , Sistemas de Registro de Ordens Médicas/normas , Erros de Medicação/prevenção & controle , Processamento de Linguagem NaturalRESUMO
Importance: The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective: To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants: This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems' outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures: Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results: Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1's system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2's system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P < .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P < .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance: Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction.
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Prescrição Eletrônica , Modelos Teóricos , Melhoria de Qualidade , Assistência Ambulatorial , Pessoal de Saúde , Humanos , Sistemas de Registro de Ordens Médicas , Erros de Medicação/estatística & dados numéricos , Inovação OrganizacionalAssuntos
Técnicas e Procedimentos Diagnósticos , Assistência Centrada no Paciente , Comunicação , Continuidade da Assistência ao Paciente , Erros de Diagnóstico , Serviço Hospitalar de Emergência , Política de Saúde , Humanos , Neoplasias/diagnóstico , Neoplasias/psicologia , Relações Médico-Paciente , Encaminhamento e ConsultaRESUMO
Objective: To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs. Methods: We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drug's association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications. Results: Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR). Conclusions: Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.
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Bases de Conhecimento , Erros de Medicação/prevenção & controle , Preparações Farmacêuticas , Terminologia como Assunto , Prescrições de Medicamentos , HumanosRESUMO
BACKGROUND: Computerised prescriber order entry (CPOE) systems users often discontinue medications because the initial order was erroneous. OBJECTIVE: To elucidate error types by querying prescribers about their reasons for discontinuing outpatient medication orders that they had self-identified as erroneous. METHODS: During a nearly 3 year retrospective data collection period, we identified 57 972 drugs discontinued with the reason 'Error (erroneous entry)." Because chart reviews revealed limited information about these errors, we prospectively studied consecutive, discontinued erroneous orders by querying prescribers in near-real-time to learn more about the erroneous orders. RESULTS: From January 2014 to April 2014, we prospectively emailed prescribers about outpatient drug orders that they had discontinued due to erroneous initial order entry. Of 2 50 806 medication orders in these 4 months, 1133 (0.45%) of these were discontinued due to error. From these 1133, we emailed 542 unique prescribers to ask about their reason(s) for discontinuing these mediation orders in error. We received 312 responses (58% response rate). We categorised these responses using a previously published taxonomy. The top reasons for these discontinued erroneous orders included: medication ordered for wrong patient (27.8%, n=60); wrong drug ordered (18.5%, n=40); and duplicate order placed (14.4%, n=31). Other common discontinued erroneous orders related to drug dosage and formulation (eg, extended release versus not). Oxycodone (3%) was the most frequent drug discontinued error. CONCLUSION: Drugs are not infrequently discontinued 'in error.' Wrong patient and wrong drug errors constitute the leading types of erroneous prescriptions recognised and discontinued by prescribers. Data regarding erroneous medication entries represent an important source of intelligence about how CPOE systems are functioning and malfunctioning, providing important insights regarding areas for designing CPOE more safely in the future.