Clinical characteristics and treatment exposure of patients with marked treatment-resistant unipolar major depressive disorder: A RECOVER trial report.

BACKGROUND: RECOVER is a randomized sham-controlled trial of vagus nerve stimulation and the largest such trial conducted with a psychiatric neuromodulation intervention. OBJECTIVE: To describe pre-implantation baseline clinical characteristics and treatment history of patients with unipolar, major depressive disorder (MDD), overall and as a function of exposure to interventional psychiatric treatments (INTs), including electroconvulsive therapy, transcranial magnetic stimulation, and esketamine. METHODS: Medical, psychiatric, and treatment records were reviewed by study investigators and an independent Study Eligibility Committee prior to study qualification. Clinical characteristics and treatment history (using Antidepressant Treatment History [Short] Form) were compared in those qualified (N = 493) versus not qualified (N = 228) for RECOVER, and among the qualified group as a function of exposure to INTs during the current major depressive episode (MDE). RESULTS: Unipolar MDD patients who qualified for RECOVER had marked TRD (median of 11.0 lifetime failed antidepressant treatments), severe disability (median WHODAS score of 50.0), and high rate of baseline suicidality (77% suicidal ideation, 40% previous suicide attempts). Overall, 71% had received at least one INT. Compared to the no INT group, INT recipients were younger and more severely depressed (QIDS-C, QIDS-SR), had greater suicidal ideation, earlier diagnosis of MDD, and failed more antidepressant medication trials. CONCLUSIONS: RECOVER-qualified unipolar patients had marked TRD and marked treatment resistance with most failing one or more prior INTs. Treatment with ≥1 INTs in the current MDE was associated with earlier age of MDD onset, more severe clinical presentation, and greater treatment resistance relative to patients without a history of INT. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT03887715.

Medical student remote eConsult participation during the COVID-19 pandemic.

BACKGROUND: Undergraduate medical education was severely impacted by the COVID-19 pandemic. As traditional clinical rotations were suspended, medical students quickly began alternative, novel educational experiences. Third-year medical students at an academic medical center were given the opportunity to join inpatient eConsult teams within the department of medicine. This study describes the development and implementation of this program as well as the experiences of student and faculty participants. METHODS: Student eConsult participation was rapidly developed and implemented within medical subspecialty teams in either infectious diseases (ID) or nephrology. Twelve third-year medical students and 15 subspecialty attendings participated in this program during an eight-week period from April 6 through May 29, 2020. Breadth of student clinical experience was assessed via review of clinical documentation and surveys. Participating students and attending physicians completed surveys to reflect upon their impressions of the program. Surveys were returned by nine students and eight faculty members. Survey responses were summarized with descriptive statistics. RESULTS: Over an eight-week period, student consultants wrote 126 notes on 100 patients; 74 of these patients (74%) were hospitalized with COVID-19. Student experiences were largely positive with most strongly agreeing that attendings promoted interactive and engaged learning (N = 8 of 8, 100%), that the experience helped to expand their knowledge about consultant roles (N = 6, 75%), and that they would participate in a remote eConsult program again if given the opportunity (N = 6, 75%). Faculty also were largely positive about the experience with most agreeing or strongly agreeing with the importance of teaching medical students about telehealth (N = 7 of 8, 88%) and eConsults (N = 6, 75%). In narrative responses, students and faculty agreed that teaching was a strength of the program whereas lack of in-person contact was a challenge. CONCLUSIONS: Rapid development of an inpatient eConsult-based educational experience for third-year medical students was feasible and successful. Student-consultants saw a range of pathology including COVID-19 and related complications. Students were satisfied with the program. They were able to develop a strong relationship with attendings while learning about the role of a consultant. Faculty agreed with the importance of teaching students about telehealth and eConsults specifically.

Risk of Wrong-Patient Orders Among Multiple vs Singleton Births in the Neonatal Intensive Care Units of 2 Integrated Health Care Systems.

IMPORTANCE: Multiple-birth infants in neonatal intensive care units (NICUs) have nearly identical patient identifiers and may be at greater risk of wrong-patient order errors compared with singleton-birth infants. OBJECTIVES: To assess the risk of wrong-patient orders among multiple-birth infants and singletons receiving care in the NICU and to examine the proportion of wrong-patient orders between multiple-birth infants and siblings (intrafamilial errors) and between multiple-birth infants and nonsiblings (extrafamilial errors). DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study was conducted in 6 NICUs of 2 large, integrated health care systems in New York City that used distinct temporary names for newborns per the requirements of The Joint Commission. Data were collected from 4 NICUs at New York-Presbyterian Hospital from January 1, 2012, to December 31, 2015, and 2 NICUs at Montefiore Health System from July 1, 2013, to June 30, 2015. Data were analyzed from May 1, 2017, to December 31, 2017. All infants in the 6 NICUs for whom electronic orders were placed during the study periods were included. MAIN OUTCOMES AND MEASURES: Wrong-patient electronic orders were identified using the Wrong-Patient Retract-and-Reorder (RAR) Measure. This measure was used to detect RAR events, which are defined as 1 or more orders placed for a patient that are retracted (ie, canceled) by the same clinician within 10 minutes, then reordered by the same clinician for a different patient within the next 10 minutes. RESULTS: A total of 10 819 infants were included: 85.5% were singleton-birth infants and 14.5% were multiple-birth infants (male, 55.8%; female, 44.2%). The overall wrong-patient order rate was significantly higher among multiple-birth infants than among singleton-birth infants (66.0 vs 41.7 RAR events per 100 000 orders, respectively; adjusted odds ratio, 1.75; 95% CI, 1.39-2.20; P < .001). The rate of extrafamilial RAR events among multiple-birth infants (36.1 per 100 000 orders) was similar to that of singleton-birth infants (41.7 per 100 000 orders). The excess risk among multiple-birth infants (29.9 per 100 000 orders) appears to be owing to intrafamilial RAR events. The risk increased as the number of siblings receiving care in the NICU increased; a wrong-patient order error occurred in 1 in 7 sets of twin births and in 1 in 3 sets of higher-order multiple births. CONCLUSIONS AND RELEVANCE: This study suggests that multiple-birth status in the NICU is associated with significantly increased risk of wrong-patient orders compared with singleton-birth status. This excess risk appears to be owing to misidentification between siblings. These results suggest that a distinct naming convention as required by The Joint Commission may provide insufficient protection against identification errors among multiple-birth infants. Strategies to reduce this risk include using given names at birth, changing from temporary to given names when available, and encouraging parents to select names for multiple births before they are born when acceptable to families.

Effect of Restriction of the Number of Concurrently Open Records in an Electronic Health Record on Wrong-Patient Order Errors: A Randomized Clinical Trial.

Importance: Recommendations in the United States suggest limiting the number of patient records displayed in an electronic health record (EHR) to 1 at a time, although little evidence supports this recommendation. Objective: To assess the risk of wrong-patient orders in an EHR configuration limiting clinicians to 1 record vs allowing up to 4 records opened concurrently. Design, Setting, and Participants: This randomized clinical trial included 3356 clinicians at a large health system in New York and was conducted from October 2015 to April 2017 in emergency department, inpatient, and outpatient settings. Interventions: Clinicians were randomly assigned in a 1:1 ratio to an EHR configuration limiting to 1 patient record open at a time (restricted; n = 1669) or allowing up to 4 records open concurrently (unrestricted; n = 1687). Main Outcomes and Measures: The unit of analysis was the order session, a series of orders placed by a clinician for a single patient. The primary outcome was order sessions that included 1 or more wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder measure (an electronic query that identifies orders placed for a patient, retracted, and then reordered shortly thereafter by the same clinician for a different patient). Results: Among the 3356 clinicians who were randomized (mean [SD] age, 43.1 [12.5] years; mean [SD] experience at study site, 6.5 [6.0] years; 1894 females [56.4%]), all provided order data and were included in the analysis. The study included 12 140 298 orders, in 4 486 631 order sessions, placed for 543 490 patients. There was no significant difference in wrong-patient order sessions per 100 000 in the restricted vs unrestricted group, respectively, overall (90.7 vs 88.0; odds ratio [OR], 1.03 [95% CI, 0.90-1.20]; P = .60) or in any setting (ED: 157.8 vs 161.3, OR, 1.00 [95% CI, 0.83-1.20], P = .96; inpatient: 185.6 vs 185.1, OR, 0.99 [95% CI, 0.89-1.11]; P = .86; or outpatient: 7.9 vs 8.2, OR, 0.94 [95% CI, 0.70-1.28], P = .71). The effect did not differ among settings (P for interaction = .99). In the unrestricted group overall, 66.2% of the order sessions were completed with 1 record open, including 34.5% of ED, 53.7% of inpatient, and 83.4% of outpatient order sessions. Conclusions and Relevance: A strategy that limited clinicians to 1 EHR patient record open compared with a strategy that allowed up to 4 records open concurrently did not reduce the proportion of wrong-patient order errors. However, clinicians in the unrestricted group placed most orders with a single record open, limiting the power of the study to determine whether reducing the number of records open when placing orders reduces the risk of wrong-patient order errors. Trial Registration: clinicaltrials.gov Identifier: NCT02876588.

Evaluating Serial Strategies for Preventing Wrong-Patient Orders in the NICU.

BACKGROUND: NICU patients have characteristics believed to increase their risk for wrong-patient errors; however, little is known about the frequency of wrong-patient errors in the NICU or about effective interventions for preventing these errors. We conducted a quality improvement study to evaluate the frequency of wrong-patient orders in the NICU and to assess the effectiveness of an ID reentry intervention and a distinct naming convention (eg, "Wendysgirl") for reducing these errors, using non-NICU pediatric units as a comparator. METHODS: Using a validated measure, we examined the rate of wrong-patient orders in NICU and non-NICU pediatric units during 3 periods: baseline (before implementing interventions), ID reentry intervention (reentry of patient identifiers before placing orders), and combined intervention (addition of a distinct naming convention for newborns). RESULTS: We reviewed >850 000 NICU orders and >3.5 million non-NICU pediatric orders during the 7-year study period. At baseline, wrong-patient orders were more frequent in NICU than in non-NICU pediatric units (117.2 vs 74.9 per 100 000 orders, respectively; odds ratio 1.56; 95% confidence interval, 1.34-1.82). The ID reentry intervention reduced the frequency of errors in the NICU to 60.2 per 100 000 (48.7% reduction; P < .001). The combined ID reentry and distinct naming interventions yielded an additional decrease to 45.6 per 100 000 (61.1% reduction from baseline; P < .001). CONCLUSIONS: The risk of wrong-patient orders in the NICU was significantly higher than in non-NICU pediatric units. Implementation of a combined ID reentry intervention and distinct naming convention greatly reduced this risk.

A national survey assessing the number of records allowed open in electronic health records at hospitals and ambulatory sites.

To reduce the risk of wrong-patient errors, safety experts recommend limiting the number of patient records providers can open at once in electronic health records (EHRs). However, it is unknown whether health care organizations follow this recommendation or what rationales drive their decisions. To address this gap, we conducted an electronic survey via 2 national listservs. Among 167 inpatient and outpatient study facilities using EHR systems designed to open multiple records at once, 44.3% were configured to allow ≥3 records open at once (unrestricted), 38.3% allowed only 1 record open (restricted), and 17.4% allowed 2 records open (hedged). Decision-making centered on efforts to balance safety and efficiency, but there was disagreement among organizations about how to achieve that balance. Results demonstrate no consensus on the number of records to be allowed open at once in EHRs. Rigorous studies are needed to determine the optimal number of records that balances safety and efficiency.

Understanding and preventing wrong-patient electronic orders: a randomized controlled trial.

OBJECTIVE: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. MATERIALS AND METHODS: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. RESULTS: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2% (95% CI 70.6% to 81.9%). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95% CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95% CI 0.50 to 0.71). DISCUSSION AND CONCLUSION: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.

Improving hospital venous thromboembolism prophylaxis with electronic decision support.

BACKGROUND: Venous thromboembolism (VTE) disease prophylaxis rates among medical inpatients have been noted to be <50%. OBJECTIVE: Our objective was to evaluate the effectiveness and safety of a computerized decision support application to improve VTE prophylaxis. DESIGN: Observational cohort study. SETTING: Academic medical center. PATIENTS: Adult inpatients on hospital medicine and nonmedicine services. INTERVENTION: A decision support application designed by a quality improvement team was implemented on medicine services in September 2009. MEASUREMENTS: Effectiveness and safety parameters were compared on medicine services and nonmedicine (nonimplementation) services for 6-month periods before and after implementation. Effectiveness was evaluated by retrospective information system queries for rates of any VTE prophylaxis, pharmacologic VTE prophylaxis, and hospital-acquired VTE incidence. Safety was evaluated by queries for bleeding and thrombocytopenia rates. RESULTS: Medicine service overall VTE prophylaxis increased from 61.9% to 82.1% (P < 0.001), and pharmacologic VTE prophylaxis increased from 59.0% to 74.5% (P < 0.001). Smaller but significant increases were observed on nonmedicine services. Hospital-acquired VTE incidence on medicine services decreased significantly from 0.65% to 0.42% (P = 0.008) and nonsignificantly on nonmedicine services. Bleeding rates increased from 2.9% to 4.0% (P < 0.001) on medicine services and from 7.7% to 8.6% (P = 0.043) on nonmedicine services, with nonsignificant changes in thrombocytopenia rates observed on both services. CONCLUSIONS: An electronic decision support application on inpatient medicine services can significantly improve VTE prophylaxis and hospital-acquired VTE rates with a reasonable safety profile.

Hospitalist care and length of stay in patients requiring complex discharge planning and close clinical monitoring.

BACKGROUND: Academic medical centers are increasingly employing hospitalists to staff teaching wards. Although studies have demonstrated reduced lengths of stay (LOSs) associated with hospitalist care, it is unclear which patients are most likely to benefit. We sought to determine whether patients with specific diagnoses or discharge needs account for the association between hospitalist care and reduced LOS. METHODS: Hospital admissions were divided into the following 2 groups based on type of attending physician: teaching hospitalist (full-time faculty hospitalist with no outpatient responsibilities) vs nonhospitalist (full-time or voluntary faculty contributing 1 or 2 months of teaching service per year). We included all patients discharged from an academic teaching service for a 2-year period. Data were extracted from the Montefiore Medical Center's clinical information system and the Social Security Death Registry. RESULTS: Mean LOS was lower for teaching hospitalists than for nonhospitalists (5.01 vs 5.87 days [P < .02]). The reduction in LOS was greatest for patients requiring close clinical monitoring (patients with congestive heart failure, stroke, asthma, or pneumonia) and for those requiring complex discharge planning. There were no significant differences between the groups in readmission, in-hospital mortality, or 30-day mortality. CONCLUSION: Teaching hospitalist care was associated with shorter LOS in patients requiring close clinical monitoring and complex discharge planning, without adversely affecting readmission or mortality rates.