Characteristics and contributing factors of diagnostic error in surgery: analysis of closed medico-legal cases and complaints in Canada.

BACKGROUND: Diagnostic errors lead to patient harm; however, most research has been conducted in nonsurgical disciplines. We sought to characterize diagnostic error in the pre-, intra-, and postoperative surgical phases, describe their contributing factors, and quantify their impact related to patient harm. METHODS: We performed a retrospective analysis of closed medico-legal cases and complaints using a database representing more than 95% of all Canadian physicians. We included cases if they involved a legal action or complaint that closed between 2014 and 2018 and involved a diagnostic error assigned by peer expert review to a surgeon. RESULTS: We identified 387 surgical cases that involved a diagnostic error. The surgical specialties most often associated with diagnostic error were general surgery (n = 151, 39.0%), gynecology (n = 71, 18.3%), and orthopedic surgery (n = 48, 12.4%), but most surgical specialties were represented. Errors occurred more often in the postoperative phase (n = 171, 44.2%) than in the pre- (n = 127, 32.8%) or intra-operative (n = 120, 31.0%) phases of surgical care. More than 80% of the contributing factors for diagnostic errors were related to providers, with clinical decision-making being the principal contributing factor. Half of the contributing factors were related to the health care team (n = 194, 50.1%), the most common of which was communication breakdown. More than half of patients involved in a surgical diagnostic error experienced at least moderate harm, with 1 in 7 cases resulting in death. CONCLUSION: In our cohort, diagnostic errors occurred in most surgical disciplines and across all surgical phases of care; contributing factors were commonly attributed to provider clinical decision-making and communication breakdown. Surgical patient safety efforts should include diagnostic errors with a focus on understanding and reducing errors in surgical clinical decision-making and improving communication.

Identifying clusters of coexisting conditions and outcomes among adults admitted to hospital with community-acquired pneumonia: a multicentre cohort study.

BACKGROUND: Little is known about patterns of coexisting conditions and their influence on clinical care or outcomes in adults admitted to hospital for community-acquired pneumonia (CAP). We sought to evaluate how coexisting conditions cluster in this population to advance understanding of how multimorbidity affects CAP. METHODS: We studied 11 085 adults admitted to hospital with CAP at 7 hospitals in Ontario, Canada. Using cluster analysis, we identified patient subgroups based on clustering of comorbidities in the Charlson Comorbidity Index. We derived and replicated cluster analyses in independent cohorts (derivation sample 2010-2015, replication sample 2015-2017), then combined these into a total cohort for final cluster analyses. We described differences in medications, imaging and outcomes. RESULTS: Patients clustered into 7 subgroups. The low comorbidity subgroup (n = 3052, 27.5%) had no comorbidities. The DM-HF-Pulm subgroup had prevalent diabetes, heart failure and chronic lung disease (n = 1710, 15.4%). One disease category defined each remaining subgroup, as follows: pulmonary (n = 1621, 14.6%), diabetes (n = 1281, 11.6%), heart failure (n = 1370, 12.4%), dementia (n = 1038, 9.4%) and cancer (n = 1013, 9.1%). Corticosteroid use ranged from 11.5% to 64.9% in the dementia and pulmonary subgroups, respectively. Piperacillin-tazobactam use ranged from 9.1% to 28.0% in the pulmonary and cancer subgroups, respectively. The use of thoracic computed tomography ranged from 5.7% to 36.3% in the dementia and cancer subgroups, respectively. Adjusting for patient factors, the risk of in-hospital death was greater in the cancer (adjusted odds ratio [OR] 3.12, 95% confidence interval [CI] 2.44-3.99), dementia (adjusted OR 1.57, 95% CI 1.05-2.35), heart failure (adjusted OR 1.66, 95% CI 1.35-2.03) and DM-HF-Pulm subgroups (adjusted OR 1.35, 95% CI 1.12-1.61), and lower in the diabetes subgroup (adjusted OR 0.67, 95% CI 0.50-0.89), compared with the low comorbidity group. INTERPRETATION: Patients admitted to hospital with CAP cluster into clinically recognizable subgroups based on coexisting conditions. Clinical care and outcomes vary among these subgroups with little evidence to guide decision-making, highlighting opportunities for research to personalize care.

Using the Hospital Frailty Risk Score to assess mortality risk in older medical patients admitted to the intensive care unit.

BACKGROUND: Prognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge. METHODS: We conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission. RESULTS: The cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77). INTERPRETATION: Among patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

Data-driven approach to identifying potential laboratory overuse in general internal medicine (GIM) inpatients.

BACKGROUND: Reducing laboratory test overuse is important for high quality, patient-centred care. Identifying priorities to reduce low value testing remains a challenge. OBJECTIVE: To develop a simple, data-driven approach to identify potential sources of laboratory overuse by combining the total cost, proportion of abnormal results and physician-level variation in use of laboratory tests. DESIGN, SETTING AND PARTICIPANTS: A multicentre, retrospective study at three academic hospitals in Toronto, Canada. All general internal medicine (GIM) hospitalisations between 1 April 2010 and 31 October 2017. RESULTS: There were 106 813 GIM hospitalisations during the study period, with median hospital length-of-stay of 4.6 days (IQR: 2.33-9.19). There were 21 tests which had a cumulative cost >US$15 400 at all three sites. The costliest test was plasma electrolytes (US$4 907 775), the test with the lowest proportion of abnormal results was red cell folate (0.2%) and the test with the greatest physician-level variation in use was antiphospholipid antibodies (coefficient of variation 3.08). The five tests with the highest cumulative rank based on greatest cost, lowest proportion of abnormal results and highest physician-level variation were: (1) lactate, (2) antiphospholipid antibodies, (3) magnesium, (4) troponin and (5) partial thromboplastin time. In addition, this method identified unique tests that may be a potential source of laboratory overuse at each hospital. CONCLUSIONS: A simple multidimensional, data-driven approach combining cost, proportion of abnormal results and physician-level variation can inform interventions to reduce laboratory test overuse. Reducing low value laboratory testing is important to promote high value, patient-centred care.

Temporal Clustering of Critical Illness Events on Medical Wards.

Importance: Recognizing and preventing patient deterioration is important for hospital safety. Objective: To investigate whether critical illness events (in-hospital death or intensive care unit [ICU] transfer) are associated with greater risk of subsequent critical illness events for other patients on the same medical ward. Design, Setting, and Participants: Retrospective cohort study in 5 hospitals in Toronto, Canada, including 118 529 hospitalizations. Patients were admitted to general internal medicine wards between April 1, 2010, and October 31, 2017. Data were analyzed between January 1, 2020, and April 10, 2023. Exposures: Critical illness events (in-hospital death or ICU transfer). Main Outcomes and Measures: The primary outcome was the composite of in-hospital death or ICU transfer. The association between critical illness events on the same ward across 6-hour intervals was studied using discrete-time survival analysis, adjusting for patient and situational factors. The association between critical illness events on different comparable wards in the same hospital was measured as a negative control. Results: The cohort included 118 529 hospitalizations (median age, 72 years [IQR, 56-83 years]; 50.7% male). Death or ICU transfer occurred in 8785 hospitalizations (7.4%). Patients were more likely to experience the primary outcome after exposure to 1 prior event (adjusted odds ratio [AOR], 1.39; 95% CI, 1.30-1.48) and more than 1 prior event (AOR, 1.49; 95% CI, 1.33-1.68) in the prior 6-hour interval compared with no exposure. The exposure was associated with increased odds of subsequent ICU transfer (1 event: AOR, 1.67; 95% CI, 1.54-1.81; >1 event: AOR, 2.05; 95% CI, 1.79-2.36) but not death alone (1 event: AOR, 1.08; 95% CI, 0.97-1.19; >1 event: AOR, 0.88; 95% CI, 0.71-1.09). There was no significant association between critical illness events on different wards within the same hospital. Conclusions and Relevance: Findings of this cohort study suggest that patients are more likely to be transferred to the ICU in the hours after another patient's critical illness event on the same ward. This phenomenon could have several explanations, including increased recognition of critical illness and preemptive ICU transfers, resource diversion to the first event, or fluctuations in ward or ICU capacity. Patient safety may be improved by better understanding the clustering of ICU transfers on medical wards.

Analysis of Resident and Attending Physician End-of-Rotation Changeover Days and Association With Patient Length of Stay.

Importance: End-of-rotation resident physician changeover is a key part of postgraduate training but could lead to discontinuity in patient care. Objective: To test whether patients exposed to end-of-rotation resident changeover have longer hospital stays and whether this association is mitigated by separating resident and attending changeover days. Design, Setting, and Participants: This retrospective cohort analysis included adult patients admitted to general internal medicine. The changeover day was the same day (first Monday of month) for both resident and attending physicians until June 30, 2013 (preseparation period), and then intentionally staggered by 1 or more days after July 1, 2013 (postseparation period). This was a multicenter analysis at 4 teaching hospitals in Ontario, Canada, from July 1, 2010, to June 30, 2019. Data analysis was conducted from July 2022 to January 2023. Exposures: Patients were classified as changeover patients if the first Monday was a resident changeover day and as control patients if the first Monday was not a resident changeover day. Main Outcomes and Measures: The primary outcome was length of hospital stay. Secondary outcomes were transfer to critical care, in-hospital death, and rate of discharge per 100 patients on the index day. Results: Of 95 282 patients. 22 773 (24%; mean [SD] age, 67.8 [18.8] years; 11 156 [49%] female patients) were exposed to resident changeover, and 72 509 (76%; mean [SD] age, 67.8 [18.7] years; 35 293 [49%] female patients) were not exposed to resident changeover. Exposure to resident changeover day was associated with a slightly longer hospital stay compared with control days (0.20 [95% CI, 0.09-0.30] days; P < .001) and decreased relative risk of patient discharge on the index day (relative risk, 0.92; 95% CI, 0.86-1.00; P = .047). These associations were similar in the preseparation and postseparation periods. Resident changeover was not associated with an increased risk of transfer to critical care or in-hospital death. Conclusions and Relevance: In this study, a small positive association between exposure to resident physician changeover and length of hospital stay as well as reduced rate of discharge was found. These findings suggest that separating changeover days for resident and attending physicians may not significantly change these associations.

Imaging delays among medical inpatients in Toronto, Ontario: A cohort study.

BACKGROUND: Imaging procedures are commonly performed on hospitalized patients and waiting for these could increase length-of-stay. The study objective was to quantify delays for imaging procedures in General Internal Medicine and identify contributing patient, physician, and system factors. METHODS: This was a retrospective cohort study of medical inpatients admitted to 5 hospitals in Toronto, Ontario (2010-2019), with at least one imaging procedure (CT, MRI, ultrasound, or peripherally-inserted central catheter [PICC] insertion). The primary outcome was time-to-test, and the secondary outcome was acute length-of-stay after test ordering. RESULTS: The study cohort included 73,107 hospitalizations. Time-to-test was longest for MRI (median 22 hours) and shortest for CT (median 7 hours). The greatest contributors to time-to-test were system factors such as hospital site (up to 22 additional hours), location of test ordering (up to 10 additional hours), the timing of test ordering relative to admission (up to 13 additional hours), and ordering during weekends (up to 21 additional hours). Older patient age, having more comorbidities, and residence in a low-income neighborhood were also associated with testing delays. Each additional hour spent waiting for a test was associated with increased acute length-of-stay after test ordering, ranging from 0.4 additional hours for CT to 1.2 hours for MRI. CONCLUSIONS: The greatest contributors to testing delays relate to when and where a test was ordered. Wait times affect length-of-stay and the quality of patient care. Hospitals can apply our novel approach to explore opportunities to decrease testing delays locally.

Osmotic Demyelination Syndrome in Patients Hospitalized with Hyponatremia.

Osmotic Demyelination Syndrome and HyponatremiaThe authors estimated the frequency of occurrence of osmotic demyelination syndrome (ODS) after correction of hyponatremia (i.e., serum sodium <130 mmol/l). Among 22,858 patients hospitalized for hyponatremia, there were 12 cases of ODS (0.05%). Correction of the serum sodium occurred in 3632 (17.7%) patients, less than one-half of the ODS cases.

Caractéristiques et issues des hospitalisations pour les cas de COVID-19 et d'influenza dans la région de Toronto.

CONTEXTE: Les caractéristiques des patients, les soins cliniques, l'utilisation des ressources et les issues cliniques des personnes atteintes de la maladie à coronavirus 2019 (COVID-19) hospitalisées au Canada ne sont pas bien connus. MÉTHODES: Nous avons recueilli des données sur tous les adultes hospitalisés atteints de la COVID-19 ou de l'influenza ayant obtenu leur congé d'unités médicales ou d'unités de soins intensifs médicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons comparé les issues cliniques des patients à l'aide de modèles de régression multivariée, en tenant compte des facteurs sociodémographiques et de l'intensité des comorbidités. Nous avons validé le degré d'exactitude de 7 scores de risque mis au point à l'externe pour déterminer leur capacité à prédire le risque de décès chez les patients atteints de la COVID-19. RÉSULTATS: Parmi les hospitalisations retenues, 1027 patients étaient atteints de la COVID-19 (âge médian de 65 ans, 59,1 % d'hommes) et 783 étaient atteints de l'influenza (âge médian de 68 ans, 50,8 % d'hommes). Les patients âgés de moins de 50 ans comptaient pour 21,2 % de toutes les hospitalisations dues à la COVID-19 et 24,0 % des séjours aux soins intensifs. Comparativement aux patients atteints de l'influenza, les patients atteints de la COVID-19 présentaient un taux de mortalité perhospitalière (mortalité non ajustée 19,9 % c. 6,1 %; risque relatif [RR] ajusté 3,46 %, intervalle de confiance [IC] à 95 % 2,56­4,68) et un taux d'utilisation des ressources des unités de soins intensifs (taux non ajusté 26,4 % c. 18,0 %; RR ajusté 1,50, IC à 95 % 1,25­1,80) significativement plus élevés, ainsi qu'une durée d'hospitalisation (durée médiane non ajustée 8,7 jours c. 4,8 jours; rapport des taux d'incidence ajusté 1,45; IC à 95 % 1,25­1,69) significativement plus longue. Le taux de réhospitalisation dans les 30 jours n'était pas significativement différent (taux non ajusté 9,3 % c. 9,6 %; RR ajusté 0,98 %, IC à 95 % 0,70­1,39). Trois scores de risque utilisant un pointage pour prédire la mortalité perhospitalière ont montré une bonne discrimination (aire sous la courbe [ASC] de la fonction d'efficacité du récepteur [ROC] 0,72­0,81) et une bonne calibration. INTERPRÉTATION: Durant la première vague de la pandémie, l'hospitalisation des patients atteints de la COVID-19 était associée à des taux de mortalité et d'utilisation des ressources des unités de soins intensifs et à une durée d'hospitalisation significativement plus importants que les hospitalisations des patients atteints de l'influenza. De simples scores de risque peuvent prédire avec une bonne exactitude le risque de mortalité perhospitalière des patients atteints de la COVID-19.

Association of diabetes with frequency and cost of hospital admissions: a retrospective cohort study.

BACKGROUND: Acute inpatient hospital admissions account for more than half of all health care costs related to diabetes. We sought to identify the most common and costly conditions leading to hospital admission among patients with diabetes compared with patients without diabetes. METHODS: We used data from the General Internal Medicine Inpatient Initiative (GEMINI) study, a retrospective cohort study, of all patients admitted to a general internal medicine service at 7 Toronto hospitals between 2010 and 2015. The Canadian Institute for Health Information (CIHI) Most Responsible Diagnosis code was used to identify the 10 most frequent reasons for admission in patients with diabetes. Cost of hospital admission was estimated using the CIHI Resource Intensity Weight. Comparisons were made between patients with or without diabetes using the Pearson χ2 test for frequency and distribution-free confidence intervals (CIs) for median cost. RESULTS: Among the 150 499 hospital admissions in our study, 41 934 (27.8%) involved patients with diabetes. Compared with patients without diabetes, hospital admissions because of soft tissue and bone infections were most frequent (2.5% v. 1.9%; prevalence ratio [PR] 1.28, 95% CI 1.19-1.37) and costly (Can$8794 v. Can$5845; cost ratio [CR] 1.50, 95% CI 1.37-1.65) among patients with diabetes. This was followed by urinary tract infections (PR 1.16, 95% CI 1.11-1.22; CR 1.23, 95% CI 1.17-1.29), stroke (PR 1.13, 95% CI 1.07-1.19; CR 1.19, 95% CI 1.14-1.25) and electrolyte disorders (PR 1.11, 95% CI 1.03-1.20; CR 1.20, 95% CI 1.08-1.34). INTERPRETATION: Soft tissue and bone infections, urinary tract infections, stroke and electrolyte disorders are associated with a greater frequency and cost of hospital admissions in patients with diabetes than in those without diabetes. Preventive strategies focused on reducing hospital admissions secondary to these disorders may be beneficial in patients with diabetes.

Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area.

BACKGROUND: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19. RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration. INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

Variations in Processes of Care and Outcomes for Hospitalized General Medicine Patients Treated by Female vs Male Physicians.

Importance: Hospitalized medical patients cared for by female physicians may have decreased mortality rates compared with patients of male physicians. However, this association has yet to be assessed outside of the US, and little is known about factors that may explain this difference. Objective: To determine whether mortality, other hospital outcomes, and processes of care differed between the patients cared for by female and male physicians. Design Setting and Participants: This retrospective cross-sectional study included patients admitted to general medical wards at 7 hospitals in Ontario, Canada, between April 1, 2010, and October 31, 2017. The association of physician gender with patient outcomes was examined while adjusting for hospital fixed effects, patient characteristics, physician characteristics, and processes of care. All patients were admitted to a general internal medicine service through the emergency department and were cared for by a general internist or family physician-hospitalist. Patients were excluded if length of stay was greater than 30 days or if the attending physician cared for less than 100 hospitalized general medicine patients over the study period. Statistical analyses were performed from October 15, 2020, to May 8, 2021. Main Outcomes and Measures: In-hospital mortality, length of stay, intensive care unit admission, 30-day readmissions, and process-of-care measures (blood tests, medications, imaging, endoscopy, and interventional radiology services). Results: A total of 171 625 hospitalized patients with a median age of 73 years (interquartile range, 56-84 years) were included (84 221 men [49.1%], 87 402 women [50.9%], and 2 patients with unspecified sex). Patients were cared for by 172 attending physicians (54 female physicians [31.4%] and 118 male physicians [68.6%]). In fully adjusted models, female physicians ordered more imaging tests, including computed tomography (adjusted difference, -1.70%; 95% CI, -2.78% to -0.61%; P = .002), magnetic resonance imaging (-0.88%; 95% CI, -1.37% to -0.38%; P = .001), and ultrasonography (-1.90%; 95% CI, -3.21% to -0.59%; P = .005). Patients treated by female physicians had lower in-hospital mortality (2256 of 46 772 patients [4.8%] vs 6452 of 124 853 patients [5.2%]). This difference persisted after adjustment for patient characteristics but was no longer statistically different after adjustment for other physician characteristics (adjusted difference, 0.29%; 95% CI, -0.08% to 0.65%; P = .12). The difference was similar after further adjustment for processes of care. Conclusions and Relevance: In this cross-sectional study of patients admitted to general medical units in Canada, patients cared for by female physicians had lower mortality rates than those treated by male physicians, adjusting for patient characteristics. This finding was nonsignificant after adjustment for other physician characteristics.

Physician-level variation in clinical outcomes and resource use in inpatient general internal medicine: an observational study.

BACKGROUND: Variations in inpatient medical care are typically attributed to system, hospital or patient factors. Little is known about variations at the physician level within hospitals. We described the physician-level variation in clinical outcomes and resource use in general internal medicine (GIM). METHODS: This was an observational study of all emergency admissions to GIM at seven hospitals in Ontario, Canada, over a 5-year period between 2010 and 2015. Physician-level variations in inpatient mortality, hospital length of stay, 30-day readmission and use of 'advanced imaging' (CT, MRI or ultrasound scans) were measured. Physicians were categorised into quartiles within each hospital for each outcome and then quartiles were pooled across all hospitals (eg, physicians in the highest quartile at each hospital were grouped together). We report absolute differences between physicians in the highest and lowest quartiles after matching admissions based on propensity scores to account for patient-level variation. RESULTS: The sample included 103 085 admissions to 135 attending physicians. After propensity score matching, the difference between physicians in the highest and lowest quartiles for in-hospital mortality was 2.4% (95% CI 0.6% to 4.3%, p<0.01); for readmission was 3.3% (95% CI 0.7% to 5.9%, p<0.01); for advanced imaging was 0.32 tests per admission (95% CI 0.12 to 0.52, p<0.01); and for hospital length of stay was 1.2 additional days per admission (95% CI 0.5 to 1.9, p<0.01). Physician-level differences in length of stay and imaging use were consistent across numerous sensitivity analyses and stable over time. Differences in mortality and readmission were consistent across most sensitivity analyses but were not stable over time and estimates were limited by sample size. CONCLUSIONS: Patient outcomes and resource use in inpatient medical care varied substantially across physicians in this study. Physician-level variations in length of stay and imaging use were unlikely to be explained by patient factors whereas differences in mortality and readmission should be interpreted with caution and could be explained by unmeasured confounders. Physician-level variations may represent practice differences that highlight quality improvement opportunities.

Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.

OBJECTIVE: Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. METHODS: The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients. RESULTS: Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%-100%), sensitivity (95%-100%), specificity (99%-100%), positive predictive value (93%-100%), and negative predictive value (99%-100%) compared to the gold standard. DISCUSSION AND CONCLUSION: Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.

Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials.

OBJECTIVE: To report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline up to August 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES AND METHODS: Randomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured. RESULTS: In 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I2=76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range -0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity. CONCLUSIONS: Most interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

Modelling resource requirements and physician staffing to provide virtual urgent medical care for residents of long-term care homes: a cross-sectional study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities. METHODS: We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments. RESULTS: There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% (n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% (n = 5297), and for 99.8% (n = 5284) of these admissions, the patient received laboratory testing, for 86.9% (n = 4604) the patient received plain radiography, for 41.5% (n = 2197) the patient received computed tomography and for 81.2% (n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each. INTERPRETATION: The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases.

Appropriateness of peripherally inserted central catheter use among general medical inpatients: an observational study using routinely collected data.

BACKGROUND: Peripherally inserted central catheters (PICC) are among the most commonly used medical devices in hospital. This study sought to determine the appropriateness of inpatient PICC use in general medicine at five academic hospitals in Toronto, Ontario, Canada, based on the Michigan Appropriateness Guide for Intravenous Catheters (MAGIC). METHODS: This was a retrospective, cross-sectional study of general internal medicine patients discharged between 1 April 2010 and 31 March 2015 who received a PICC during hospitalisation. The primary outcomes were the proportions of appropriate and inappropriate inpatient PICC use based on MAGIC recommendations. Hospital administrative data and electronic clinical data were used to determine appropriateness of each PICC placement. Multivariable regression models were fit to explore patient predictors of inappropriate use. RESULTS: Among 3479 PICC placements, 1848 (53%, 95% CI 51% to 55%) were appropriate, 573 (16%, 95% CI 15% to 18%) were inappropriate and 1058 (30%, 95% CI 29% to 32%) were of uncertain appropriateness. The proportion of appropriate and inappropriate PICCs ranged from 44% to 61% (p<0.001) and 13% to 21% (p<0.001) across hospitals, respectively. The most common reasons for inappropriate PICC use were placement in patients with advanced chronic kidney disease (n=500, 14%) and use for fewer than 15 days in patients who are critically ill (n=53), which represented 14% of all PICC placements in the intensive care unit. Patients who were older, female, had a Charlson Comorbidity Index score greater than 0 and more severe illness based on the Laboratory-based Acute Physiology Score were more likely to receive an inappropriate PICC. CONCLUSIONS: Clinical practice recommendations can be operationalised into measurable domains to estimate the appropriateness of PICC insertions using routinely collected hospital data. Inappropriate PICC use was common and varied substantially across hospitals in this study, suggesting that there are important opportunities to improve care.

Self-reported test ordering practices among Canadian internal medicine physicians and trainees: a multicenter cross-sectional survey.

BACKGROUND: Over-testing is a recognized problem, but clinicians usually lack information about their personal test ordering volumes. In the absence of data, clinicians rely on self-perception to inform their test ordering practices. In this study we explore clinician self-perception of diagnostic test ordering intensity. METHODS: We conducted a cross-sectional survey of inpatient General Internal Medicine (GIM) attending physicians and trainees at three Canadian teaching hospitals. We collected information about: self-reported test ordering intensity, perception of colleagues test ordering intensity, and importance of clinical utility, patient comfort, and cost when ordering tests. We compared responses of clinicians who self-identified as high vs low utilizers of diagnostic tests, and attending physicians vs trainees. RESULTS: Only 15% of inpatient GIM clinicians self-identified as high utilizers of diagnostic tests, while 73% felt that GIM clinicians in aggregate ("others") order too many tests. Survey respondents identified clinical utility as important when choosing to order tests (selected by 94%), followed by patient comfort (48%) and cost (23%). Self-identified low/average utilizers of diagnostic tests were more likely to report considering cost compared to high utilizers (27% vs 5%, P = 0.04). Attending physicians were more likely to consider patient comfort (70% vs 41%, p = 0.01) and cost (42% vs 17%, p = 0.003) than trainees. CONCLUSIONS: In the absence of data, providers seem to recognize that over investigation is a problem, but few self-identify as being high test utilizers. Moreover, a significant percentage of respondents did not consider cost or patient discomfort when ordering tests. Our findings highlight challenges in reducing over-testing in the current era.

Value of routine echocardiography in the management of stroke.

BACKGROUND: Transthoracic echocardiography is routinely performed in patients with stroke or transient ischemic attack (TIA) to help plan secondary stroke management, but recent data evaluating its usefulness in this context are lacking. We sought to evaluate the value of echocardiography for identifying clinically actionable findings for secondary stroke prevention. METHODS: We conducted a multicentre cohort study of patients admitted to hospital with stroke or TIA between 2010 and 2015 at 2 academic hospitals in Toronto, Ontario, Canada. Clinically actionable echocardiographic findings for secondary stroke prevention included cardiac thrombus, patent foramen ovale, atrial myxoma or valvular vegetation. We identified patient characteristics associated with clinically actionable findings using logistic regression. RESULTS: Of the 1862 patients with stroke or TIA we identified, 1272 (68%) had at least 1 echocardiogram. Nearly all echocardiograms were transthoracic; 1097 (86%) were normal, 1 (0.08%) had an atrial myxoma, 2 (0.2%) had a valvular vegetation, 11 (0.9%) had a cardiac thrombus and 66 (5.2%) had a PFO. Patent foramen ovale was less likely among patients older than 60 years (adjusted odds ratio [OR] 0.34, 95% confidence interval [CI] 0.20-0.57), with prior stroke or TIA (adjusted OR 0.31, 95% CI 0.09-0.76) or with dyslipidemia (adjusted OR 0.39, 95% CI 0.15-0.84). Among the 130 patients with cryptogenic stroke who had an echocardiogram (n = 110), a PFO was detected in 19 (17%) on transthoracic echocardiogram. INTERPRETATION: Most patients with stroke or TIA had a normal echocardiogram, with few having clinically actionable findings for secondary stroke prevention. Clinically actionable findings, specifically PFO, were more common in patients with cryptogenic stroke.