Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System.

Patients with cancer are presumed to be at increased risk from COVID-19 infection-related fatality due to underlying malignancy, treatment-related immunosuppression, or increased comorbidities. A total of 218 COVID-19-positive patients from March 18, 2020, to April 8, 2020, with a malignant diagnosis were identified. A total of 61 (28%) patients with cancer died from COVID-19 with a case fatality rate (CFR) of 37% (20/54) for hematologic malignancies and 25% (41/164) for solid malignancies. Six of 11 (55%) patients with lung cancer died from COVID-19 disease. Increased mortality was significantly associated with older age, multiple comorbidities, need for ICU support, and elevated levels of D-dimer, lactate dehydrogenase, and lactate in multivariate analysis. Age-adjusted CFRs in patients with cancer compared with noncancer patients at our institution and New York City reported a significant increase in case fatality for patients with cancer. These data suggest the need for proactive strategies to reduce likelihood of infection and improve early identification in this vulnerable patient population. SIGNIFICANCE: COVID-19 in patients with cancer is associated with a significantly increased risk of case fatality, suggesting the need for proactive strategies to reduce likelihood of infection and improve early identification in this vulnerable patient population.This article is highlighted in the In This Issue feature, p. 890.

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.

Use of Temporary Names for Newborns and Associated Risks.

BACKGROUND: Because there can be no delay in providing identification wristbands to newborns, some hospitals assign newborns temporary first names such as Babyboy or Babygirl. These nondistinct naming conventions result in a large number of patients with similar identifiers in NICUs. To determine the level of risk associated with nondistinct naming conventions, we performed an intervention study to evaluate if assigning distinct first names at birth would result in a reduction in wrong-patient errors. METHODS: We conducted a 2-year before/after implementation study to examine the effect of a distinct naming convention that incorporates the mother's first name into the newborn's first name (eg, Wendysgirl) on the incidence of wrong-patient errors. We used the Retract-and-Reorder (RAR) tool, an established, automated tool for detecting the outcome of wrong-patient electronic orders. The RAR tool identifies orders placed on a patient that are retracted within 10 minutes and then placed by the same clinician on a different patient within the next 10 minutes. RESULTS: The reduction in RAR events post- versus preintervention was 36.3%. After accounting for clusters of orders within order sessions, the odds ratio of an RAR event post- versus preintervention was 0.64 (95% confidence interval: 0.42-0.97). CONCLUSIONS: The study results suggest that nondistinct naming conventions are associated with an increased risk of wrong-patient errors and that this risk can be mitigated by changing to a more distinct naming convention.

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.

The impact of the heparin-induced thrombocytopenia (HIT) computerized alert on provider behaviors and patient outcomes.

OBJECTIVE: The aim of this study was to measure the effect of an electronic heparin-induced thrombocytopenia (HIT) alert on provider ordering behaviors and on patient outcomes. MATERIALS AND METHODS: A pop-up alert was created for providers when an individual's platelet values had decreased by 50% or to <100,000/mm(3) in the setting of recent heparin exposure. The authors retrospectively compared inpatients admitted between January 24, 2008 and August 24, 2008 to a control group admitted 1 year prior to the HIT alert. The primary outcome was a change in HIT antibody testing. Secondary outcomes included an assessment of incidence of HIT antibody positivity, percentage of patients started on a direct thrombin inhibitor (DTI), length of stay and overall mortality. RESULTS: There were 1006 and 1081 patients in the control and intervention groups, respectively. There was a 33% relative increase in HIT antibody test orders (p=0.01), and 33% more of these tests were ordered the first day after the criteria were met when a pop-up alert was given (p=0.03). Heparin was discontinued in 25% more patients in the alerted group (p=0.01), and more direct thrombin inhibitors were ordered for them (p=0.03). The number who tested HIT antibody-positive did not differ, however, between the two groups (p=0.99). The length of stay and mortality were similar in both groups. CONCLUSIONS: The HIT alert significantly impacted provider behaviors. However, the alert did not result in more cases of HIT being detected or an improvement in overall mortality. Our findings do not support implementation of a computerized HIT alert.