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Monitoring Length of Stay of Acute Myocardial Infarction Patients: A Times Series Analysis Using Statistical Process Control. J Healthc Manag 2022; 67:353-366. [PMID: 36074699 DOI: 10.1097/jhm-d-21-00235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
GOAL Given that length of stay (LOS) of acute myocardial infarction (AMI) patients has a significant impact on the utilization of hospital resources and the health status of communities, this study focused on how best to monitor LOS of AMI patients admitted to U.S. hospitals by employing statistical process control (SPC). METHODS Data were abstracted from the Healthcare Cost and Utilization Project Nationwide Readmissions Database between 2010 and 2016. A total of 1,491 patients were examined in the study. Patients who were admitted to nonfederal government (public) hospitals in metropolitan areas of at least 1 million residents with the primary diagnosis of AMI were abstracted. They were excluded if they developed AMI secondary to an interventional procedure or surgery, died during their index hospitalization, and were admitted and discharged on the same day. Patients were also excluded if they were discharged to short-term hospitals, nursing facilities, intermediate care facilities, home healthcare, or against medical advice. Individual moving range (I-MR) charts were used to monitor LOS of individual AMI patients in each subgroup from 2010 to 2016. PRINCIPAL FINDINGS The results showed I-MR charts could be used to indicate statistically out-of-control signals on LOS. Specifically, I-MR charts showed that LOS decreased between 2010 and 2016. LOS appeared to be longer at teaching hospitals compared to nonteaching hospitals and varied by gender. Female patients appeared to stay longer than male patients in the hospitals. PRACTICAL APPLICATIONS The application of SPC and control charts can facilitate improved decision-making in healthcare organizations. This study shows the value of integrating control charts in administrative and medical decision-making processes. It may also help healthcare providers and managers achieve higher quality and lower cost of care.
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Ippoliti R, Falavigna G, Zanelli C, Bellini R, Numico G. Neural networks and hospital length of stay: an application to support healthcare management with national benchmarks and thresholds. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2021; 19:67. [PMID: 34627288 PMCID: PMC8502324 DOI: 10.1186/s12962-021-00322-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 09/23/2021] [Indexed: 11/25/2022] Open
Abstract
Background The problem of correct inpatient scheduling is extremely significant for healthcare management. Extended length of stay can have negative effects on the supply of healthcare treatments, reducing patient accessibility and creating missed opportunities to increase hospital revenues by means of other treatments and additional hospitalizations. Methods Adopting available national reference values and focusing on a Department of Internal and Emergency Medicine located in the North-West of Italy, this work assesses prediction models of hospitalizations with length of stay longer than the selected benchmarks and thresholds. The prediction models investigated in this case study are based on Artificial Neural Networks and examine risk factors for prolonged hospitalizations in 2018. With respect current alternative approaches (e.g., logistic models), Artificial Neural Networks give the opportunity to identify whether the model will maximize specificity or sensitivity. Results Our sample includes administrative data extracted from the hospital database, collecting information on more than 16,000 hospitalizations between January 2018 and December 2019. Considering the overall department in 2018, 40% of the hospitalizations lasted more than the national average, and almost 3.74% were outliers (i.e., they lasted more than the threshold). According to our results, the adoption of the prediction models in 2019 could reduce the average length of stay by up to 2 days, guaranteeing more than 2000 additional hospitalizations in a year. Conclusions The proposed models might represent an effective tool for administrators and medical professionals to predict the outcome of hospital admission and design interventions to improve hospital efficiency and effectiveness.
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Affiliation(s)
- Roberto Ippoliti
- Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany.
| | - Greta Falavigna
- Research Institute on Sustainable Economic Growth (IRCrES), National Research Council of Italy (CNR), Moncalieri, TO, Italy
| | - Cristian Zanelli
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, AL, Italy
| | - Roberta Bellini
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, AL, Italy
| | - Gianmauro Numico
- Medical Oncology Unit, Azienda Ospedaliera Santa Croce e Carle, Cuneo, CN, Italy
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Roberts SB, Hansen BE, Shin S, Abrahamyan L, Lapointe-Shaw L, Janssen HLA, Razak F, Verma AA, Hirschfield GM. Internal medicine hospitalisations and liver disease: a comparative disease burden analysis of a multicentre cohort. Aliment Pharmacol Ther 2021; 54:689-698. [PMID: 34181776 DOI: 10.1111/apt.16488] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 04/07/2021] [Accepted: 06/04/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Liver disease is an increasing burden on population health globally. AIMS To characterise burden of liver disease among general internal medicine inpatients at seven Toronto-area hospitals and compare it to other common medical conditions. METHODS Data from April 2010 to October 2017 were obtained from hospitals participating in the GEMINI collaborative. Using these cohort data from hospital information systems linked to administrative data, we defined liver disease admissions using most responsible discharge diagnoses categorised according to international classification of diseases, 10th Revision-enhanced Canadian version (ICD-10-CA). We identified admissions for heart failure, chronic obstructive pulmonary disease (COPD) and pneumonia as comparators. We calculated standardised mortality ratios (SMRs) as the ratio of observed to expected deaths. RESULTS Among 239 018 discharges, liver disease accounted for 1.7% of most responsible discharge diagnoses. Liver disease was associated with marked premature mortality, with SMR of 8.84 (95% CI 8.06-9.67) compared to 1.06 (95% CI 0.99-1.12) for heart failure, 1.05 (95% CI 0.96-1.15) for COPD and 1.28 (95% CI 1.20-1.37) for pneumonia. The majority of deaths were among patients younger than 65 years (57.7%) compared to 3.3% in heart failure, 5.6% in COPD and 10.7% in pneumonia. Liver disease patients presented with worse Laboratory-Based Acute Physiology Scores, were more frequently admitted to the intensive care unit (14.4%), incurred higher average total costs (median $6723 CAD), had higher in-hospital mortality (11.4%), and were more likely to be a readmission from 30 days prior (19.8%). Non-alcoholic fatty liver disease admissions increased from 120 in 2011-2012 to 215 in 2016-2017 (P < 0.01). CONCLUSION In Canada's largest urban centre, liver disease admissions resulted in premature morbidity and mortality with higher resource use compared to common cardio-respiratory conditions. Re-evaluation of approaches to caring for inpatients with liver disease is timely and justified.
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Affiliation(s)
- Surain B Roberts
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Bettina E Hansen
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Saeha Shin
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Lusine Abrahamyan
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Lauren Lapointe-Shaw
- Department of Medicine, University of Toronto, Toronto, ON, Canada.,Division of General Internal Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Harry L A Janssen
- Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Fahad Razak
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Amol A Verma
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Gideon M Hirschfield
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, ON, Canada
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Numico G, Bellini R, Zanelli C, Ippoliti R, Boverio R, Kozel D, Davio P, Aiosa G, Bellora A, Chichino G, Ruiz L, Ladetto M, Polla B, Manganaro M, Pistis G, Gemme C, Stobbione P, Desperati M, Centini G. Organizational determinants of hospital stay: establishing the basis of a widespread action on more efficient pathways in medical units. Intern Emerg Med 2020; 15:1011-1019. [PMID: 31907767 DOI: 10.1007/s11739-019-02267-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/18/2019] [Indexed: 12/16/2022]
Abstract
Given the high hospital costs, the increasing clinical complexity and the overcrowding of emergency departments, it is crucial to improve the efficiency of medical admissions. We aimed at isolating organizational drivers potentially targetable through a widespread improvement action. We studied all medical admissions in a large tertiary referral hospital from January 1st to December 31st, 2018. Data were retrieved from the administrative database. Available information included age, sex, type (urgent or elective) and Unit of admission, number of internal transfers, main ICD-9 diagnosis, presence of cancer among diagnoses, surgical or medical code, type of discharge, month, day and hour of admission and discharge. National Ministry of Health database was used for comparisons. 8099 admissions were analyzed. Urgent admissions (80.5% of the total) were responsible for longer stays and were the object of the multivariate analysis. The variables most influencing length-of-stay (LOS) were internal transfers and assisted discharge: they contributed, respectively, to 62% and 40% prolongation of LOS. Also, the daily and weekly kinetics of admission accounted for a significant amount of variation in LOS. Long admissions (≥ 30 days) accounted for the 15.5% of total bed availability. Type of discharge and internal transfers were again among the major determinants. A few factors involved in LOS strictly depend on the organizational environment and are potentially modifiable. Re-engineering should be focused on making more efficient internal and external transitions and at ensuring continuity of the clinical process throughout the day and the week.
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Affiliation(s)
- Gianmauro Numico
- Department of Medicine and Medical Oncology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Via Venezia 16, 15121, Alessandria, Italy.
| | - Roberta Bellini
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Cristian Zanelli
- Quality and Management Control Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Roberto Ippoliti
- Department of Business, Administration and Economics, University of Bielefeld, Bielefeld, Deutschland
| | - Riccardo Boverio
- Emergency Department Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Daniela Kozel
- General and Medical Direction, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Piero Davio
- Internal Medicine Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Giuseppe Aiosa
- Internal Medicine Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Aldo Bellora
- Geriatric Medicine Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Guido Chichino
- Infectious Diseases Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Luigi Ruiz
- Neurology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Marco Ladetto
- Hematology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Biagio Polla
- Respiratory Medicine Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Marco Manganaro
- Nephrology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Gianfranco Pistis
- Cardiology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Carlo Gemme
- Gastroenterology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Paolo Stobbione
- Rheumathology Unit, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Massimo Desperati
- General and Medical Direction, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Giacomo Centini
- General and Medical Direction, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
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Jang S, Yeo I, Feldman DN, Cheung JW, Minutello RM, Singh HS, Bergman G, Wong SC, Kim LK. Associations Between Hospital Length of Stay, 30-Day Readmission, and Costs in ST-Segment-Elevation Myocardial Infarction After Primary Percutaneous Coronary Intervention: A Nationwide Readmissions Database Analysis. J Am Heart Assoc 2020; 9:e015503. [PMID: 32468933 PMCID: PMC7428974 DOI: 10.1161/jaha.119.015503] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background Readmission after ST-segment-elevation myocardial infarction (STEMI) poses an enormous economic burden to the US healthcare system. There are limited data on the association between length of hospital stay (LOS), readmission rate, and overall costs in patients who underwent primary percutaneous coronary intervention for STEMI. Methods and Results All STEMI hospitalizations were selected in the Nationwide Readmissions Database from 2010 to 2014. From the patients who underwent primary percutaneous coronary intervention, we examined the 30-day outcomes including readmission, mortality, reinfarction, repeat revascularization, and hospital charges/costs according to LOS (1-2, 3, 4, 5, and >5 days) stratified by infarct locations. The 30-day readmission rate after percutaneous coronary intervention for STEMI was 12.0% in the anterior wall (AW) STEMI group and 9.9% in the non-AW STEMI group. Patients with a very short LOS (1-2 days) were readmitted less frequently than those with a longer LOS regardless of infarct locations. However, patients with a very short LOS had significantly increased 30-day readmission mortality versus an LOS of 3 days (hazard ratio, 1.91; CI, 1.16-3.16 [P=0.01]) only in the AW STEMI group. Total costs (index admission+readmission) were the lowest in the very short LOS cohort in both the AW STEMI group (P<0.001) and the non-AW STEMI group (P<0.001). Conclusions For patients who underwent primary percutaneous coronary intervention for STEMI, a very short LOS was associated with significantly lower 30-day readmission and lower cumulative cost. However, a very short LOS was associated with higher 30-day mortality compared with at least a 3-day stay in the AW STEMI cohort.
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Affiliation(s)
- Sun‐Joo Jang
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
- Dalio Institute of Cardiovascular ImagingDepartment of RadiologyWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Ilhwan Yeo
- Division of CardiologyNew York Presbyterian Queens HospitalNew YorkNY
- Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Dmitriy N. Feldman
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Jim W. Cheung
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Robert M. Minutello
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Harsimran S. Singh
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Geoffrey Bergman
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - S. Chiu Wong
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
| | - Luke K. Kim
- Weill Cornell Cardiovascular Outcomes Research Group (CORG)Division of CardiologyDepartment of MedicineWeill Cornell MedicineNew York Presbyterian HospitalNew YorkNY
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Hobin E, Swanson A, Booth G, Russell K, Rosella LC, Smith BT, Manley E, Isaranuwatchai W, Whitehouse S, Brunton N, McGavock J. Physical activity trails in an urban setting and cardiovascular disease morbidity and mortality in Winnipeg, Manitoba, Canada: a study protocol for a natural experiment. BMJ Open 2020; 10:e036602. [PMID: 32075847 PMCID: PMC7045157 DOI: 10.1136/bmjopen-2019-036602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
INTRODUCTION Aspects of the built environment that support physical activity are associated with better population health outcomes. Few experimental data exist to support these observations. This protocol describes the study of the creation of urban trials on cardiovascular disease (CVD)-related morbidity and mortality in a large urban centre. METHODS AND ANALYSIS Between 2008 and 2010, the city of Winnipeg, Canada, built four, paved, multiuse (eg, cycling, walking and running), two-lane trails that are 5-8 km long and span ~60 neighbourhoods. Linking a population-based health data with census and environmental data, we will perform an interrupted time series analysis to assess the impact of this natural experiment on CVD-related morbidity and mortality among individuals 30-65 years of age residing within 400-1200 m of the trail. The primary outcome of interest is a composite measure of incident major adverse CVD events (ie, CVD-related mortality, ischaemic heart disease, stroke and congestive heart failure). The secondary outcome of interest is a composite measure of incident CVD-related risk factors (ie, diabetes, hypertension and dyslipidaemia). Outcomes will be assessed quarterly in the 10 years before the intervention and 5 years following the intervention, with a 4-year interruption. We will adjust analyses for differences in age, sex, ethnicity, immigration status, income, gentrification and other aspects of the built environment (ie, greenspace, fitness/recreation centres and walkability). We will also assess trail use and trail user profiles using field data collection methods. ETHICS AND DISSEMINATION Ethical approvals for the study have been granted by the Health Research Ethics Board at the University of Manitoba and the Health Information Privacy Committee within the Winnipeg Regional Health Authority. We have adopted an integrated knowledge translation approach. Information will be disseminated with public and government partners. TRIAL REGISTRATION NUMBER NCT04057417.
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Affiliation(s)
- Erin Hobin
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Gillian Booth
- Department of Endocrinology and Metabolism, University of Toronto, Toronto, Ontario, Canada
| | - Kelly Russell
- Department of Pediatrics and Child Health, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Brendan T Smith
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Ed Manley
- The Bartlett Centre for Advanced Spatial Analysis (CASA), University College London, London, UK
| | - Wanrudee Isaranuwatchai
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | | | - Nicole Brunton
- Department of Pediatrics and Child Health, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jonathan McGavock
- Department of Pediatrics and Child Health, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
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Verma AA, Guo Y, Kwan JL, Lapointe-Shaw L, Rawal S, Tang T, Weinerman A, Razak F. Prevalence and Costs of Discharge Diagnoses in Inpatient General Internal Medicine: a Multi-center Cross-sectional Study. J Gen Intern Med 2018; 33:1899-1904. [PMID: 30054888 PMCID: PMC6206337 DOI: 10.1007/s11606-018-4591-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 06/06/2018] [Accepted: 07/12/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Understanding the most common and costly conditions treated by inpatient general medical services is important for implementing quality improvement, developing health policy, conducting research, and designing medical education. OBJECTIVE To determine the prevalence and cost of conditions treated on general internal medicine (GIM) inpatient services. DESIGN Retrospective cross-sectional study involving 7 hospital sites in Toronto, Canada. PARTICIPANTS All patients discharged between April 1, 2010 and March 31, 2015 who were admitted to or discharged from an inpatient GIM service. MAIN MEASURES Hospital administrative data were used to identify diagnoses and costs associated with admissions. The primary discharge diagnosis was identified for each admission and categorized into clinically relevant and mutually exclusive categories using the Clinical Classifications Software (CCS) tool. KEY RESULTS Among 148,442 admissions, the most common primary discharge diagnoses were heart failure (5.1%), pneumonia (5.0%), urinary tract infection (4.6%), chronic obstructive pulmonary disease (4.5%), and stroke (4.4%). The prevalence of the 20 most common conditions was significantly correlated across hospitals (correlation coefficients ranging from 0.55 to 0.95, p ≤ 0.01 for all comparisons). No single condition represented more than 5.1% of all admissions or more than 7.9% of admissions at any hospital site. The costliest conditions were stroke (median cost $7122, interquartile range 5587-12,354, total cost $94,199,422, representing 6.0% of all costs) and the group of delirium, dementia, and cognitive disorders (median cost $12,831, IQR 9539-17,509, total cost $77,372,541, representing 4.9% of all costs). The 10 most common conditions accounted for only 36.2% of hospitalizations and 36.8% of total costs. The remaining hospitalizations included 223 different CCS conditions. CONCLUSIONS GIM services care for a markedly heterogeneous population but the most common conditions were similar across 7 hospitals. The diversity of conditions cared for in GIM may be challenging for healthcare delivery and quality improvement. Initiatives that cut across individual diseases to address processes of care, patient experience, and functional outcomes may be more relevant to a greater proportion of the GIM population than disease-specific efforts.
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Affiliation(s)
- Amol A Verma
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Room 714-3, 2 Queen St. East, Toronto, Ontario, M5C 3G7, Canada.
- Department of Medicine, University of Toronto, Toronto, Canada.
| | - Yishan Guo
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Room 714-3, 2 Queen St. East, Toronto, Ontario, M5C 3G7, Canada
| | - Janice L Kwan
- Department of Medicine, University of Toronto, Toronto, Canada
- Department of Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada
| | | | - Shail Rawal
- Department of Medicine, University of Toronto, Toronto, Canada
- Division of General Internal Medicine, University Health Network, Toronto, Canada
| | - Terence Tang
- Department of Medicine, University of Toronto, Toronto, Canada
- Institute for Better Health, Trillium Health Partners, Toronto, Canada
| | - Adina Weinerman
- Department of Medicine, University of Toronto, Toronto, Canada
- Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Fahad Razak
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Room 714-3, 2 Queen St. East, Toronto, Ontario, M5C 3G7, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
- Harvard Center for Population and Development Studies, Cambridge, USA
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
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The Impact of the Implementation of the Enhanced Recovery After Surgery (ERAS®) Program in an Entire Health System: A Natural Experiment in Alberta, Canada. World J Surg 2018. [DOI: 10.1007/s00268-018-4559-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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McAlister FA, Bakal JA, Green L, Bahler B, Lewanczuk R. The effect of provider affiliation with a primary care network on emergency department visits and hospital admissions. CMAJ 2018; 190:E276-E284. [PMID: 29530868 PMCID: PMC5849446 DOI: 10.1503/cmaj.170385] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/23/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Primary care networks are designed to facilitate access to inter-professional, team-based care. We compared health outcomes associated with primary care networks versus conventional primary care. METHODS We obtained data on all adult residents of Alberta who visited a primary care physician during fiscal years 2008 and 2009 and classified them as affiliated with a primary care network or not, based on the physician most involved in their care. The primary outcome was an emergency department visit or nonelective hospital admission for a Patient Medical Home indicator condition (asthma, chronic obstructive pulmonary disease, heart failure, coronary disease, hypertension and diabetes) within 12 months. RESULTS Adults receiving care within a primary care network (n = 1 502 916) were older and had higher comorbidity burdens than those receiving conventional primary care (n = 1 109 941). Patients in a primary care network were less likely to visit the emergency department for an indicator condition (1.4% v. 1.7%, mean 0.031 v. 0.035 per patient, adjusted risk ratio [RR] 0.98, 95% confidence interval [CI] 0.96-0.99) or for any cause (25.5% v. 30.5%, mean 0.55 v. 0.72 per patient, adjusted RR 0.93, 95% CI 0.93-0.94), but were more likely to be admitted to hospital for an indicator condition (0.6% v. 0.6%, mean 0.018 v. 0.017 per patient, adjusted RR 1.07, 95% CI 1.03-1.11) or all-cause (9.3% v. 9.1%, mean 0.25 v. 0.23 per patient, adjusted RR 1.08, 95% CI 1.07-1.09). Patients in a primary care network had 169 fewer all-cause emergency department visits and 86 fewer days in hospital (owing to shorter lengths of stay) per 1000 patient-years. INTERPRETATION Care within a primary care network was associated with fewer emergency department visits and fewer hospital days.
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Affiliation(s)
- Finlay A McAlister
- Division of General Internal Medicine (McAlister) and Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Bakal), Alberta SPOR Support Unit Data Platform; Department of Family Medicine (Green); Department of Medicine (Lewanczuk), University of Alberta and Primary Health Care, Alberta Health Services (Bahler), Edmonton, Alta.
| | - Jeffrey A Bakal
- Division of General Internal Medicine (McAlister) and Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Bakal), Alberta SPOR Support Unit Data Platform; Department of Family Medicine (Green); Department of Medicine (Lewanczuk), University of Alberta and Primary Health Care, Alberta Health Services (Bahler), Edmonton, Alta
| | - Lee Green
- Division of General Internal Medicine (McAlister) and Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Bakal), Alberta SPOR Support Unit Data Platform; Department of Family Medicine (Green); Department of Medicine (Lewanczuk), University of Alberta and Primary Health Care, Alberta Health Services (Bahler), Edmonton, Alta
| | - Brad Bahler
- Division of General Internal Medicine (McAlister) and Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Bakal), Alberta SPOR Support Unit Data Platform; Department of Family Medicine (Green); Department of Medicine (Lewanczuk), University of Alberta and Primary Health Care, Alberta Health Services (Bahler), Edmonton, Alta
| | - Richard Lewanczuk
- Division of General Internal Medicine (McAlister) and Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Bakal), Alberta SPOR Support Unit Data Platform; Department of Family Medicine (Green); Department of Medicine (Lewanczuk), University of Alberta and Primary Health Care, Alberta Health Services (Bahler), Edmonton, Alta
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Verma AA, Guo Y, Kwan JL, Lapointe-Shaw L, Rawal S, Tang T, Weinerman A, Cram P, Dhalla IA, Hwang SW, Laupacis A, Mamdani MM, Shadowitz S, Upshur R, Reid RJ, Razak F. Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: the General Medicine Inpatient Initiative (GEMINI) retrospective cohort study. CMAJ Open 2017; 5:E842-E849. [PMID: 29237706 PMCID: PMC5741428 DOI: 10.9778/cmajo.20170097] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The precise scope of hospital care delivered under general internal medicine services remains poorly quantified. The purpose of this study was to describe the demographic characteristics, medical conditions, health outcomes and resource use of patients admitted to general internal medicine at 7 hospital sites in the Greater Toronto Area. METHODS This was a retrospective cohort study involving all patients who were admitted to or discharged from general internal medicine at the study sites between Apr. 1, 2010, and Mar. 31, 2015. Clinical data from hospital electronic information systems were linked to administrative data from each hospital. We examined trends in resource use and patient characteristics over the study period. RESULTS There were 136 208 admissions to general internal medicine involving 88 121 unique patients over the study period. General internal medicine admissions accounted for 38.8% of all admissions from the emergency department and 23.7% of all hospital bed-days. Over the study period, the number of admissions to general internal medicine increased by 32.4%; there was no meaningful change in the median length of stay or cost per hospital stay. The median patient age was 73 (interquartile range [IQR] 57-84) years, and the median number of coexisting conditions was 6 (IQR 3-9). The median acute length of stay was 4.6 (IQR 2.5-8.6) days, and the median total cost per hospital stay was $5850 (IQR $3915-$10 061). Patients received at least 1 computed tomography scan in 52.2% of admissions. The most common primary discharge diagnoses were pneumonia (5.0% of admissions), heart failure (4.7%), chronic obstructive pulmonary disease (4.1%), urinary tract infection (4.0%) and stroke (3.6%). INTERPRETATION Patients admitted to general internal medicine services represent a large, heterogeneous, resource-intensive and growing population. Understanding and improving general internal medicine care is essential to promote a high-quality, sustainable health care system.
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Affiliation(s)
- Amol A Verma
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Yishan Guo
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Janice L Kwan
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Lauren Lapointe-Shaw
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Shail Rawal
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Terence Tang
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Adina Weinerman
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Peter Cram
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Irfan A Dhalla
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Stephen W Hwang
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Andreas Laupacis
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Muhammad M Mamdani
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Steven Shadowitz
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Ross Upshur
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Robert J Reid
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
| | - Fahad Razak
- Affiliations: Li Ka Shing Centre for Healthcare Analytics Research and Training (Verma, Mamdani), St. Michael's Hospital; Eliot Phillipson Clinician-Scientist Training Program (Verma), Department of Medicine, University of Toronto; Li Ka Shing Knowledge Institute (Guo, Dhalla, Hwang, Laupacis, Mamdani, Razak), St. Michael's Hospital; Department of Medicine (Kwan), Sinai Health System; Department of Medicine (Kwan, Lapointe-Shaw, Rawal, Razak, Tang, Weinerman), University of Toronto; Division of General Internal Medicine (Rawal), University Health Network; Institute for Better Health (Tang, Reid), Trillium Health Partners; Sunnybrook Health Sciences Centre (Weinerman); University Health Network, Sinai Health System (Cram), University of Toronto; Health Quality Ontario (Dhalla); Sunnybrook Health Sciences Centre (Shadowitz), University of Toronto; Bridgepoint Health (Upshur), University of Toronto, Toronto, Ont.; Harvard Center for Population and Development Studies (Razak), Cambridge, Mass
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11
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McAlister FA, Shojania KG. Inpatient bedspacing: could a common response to hospital crowding cause increased patient mortality? BMJ Qual Saf 2017; 27:1-3. [DOI: 10.1136/bmjqs-2017-007524] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2017] [Indexed: 11/04/2022]
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12
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Zapatero-Gaviria A, Javier Elola-Somoza F, Casariego-Vales E, Fernandez-Perez C, Gomez-Huelgas R, Bernal JL, Barba-Martín R. RECALMIN: The association between management of Spanish National Health Service Internal Medical Units and health outcomes. Int J Qual Health Care 2017; 29:507-511. [PMID: 28541515 DOI: 10.1093/intqhc/mzx055] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 05/05/2017] [Indexed: 11/14/2022] Open
Abstract
Objective To investigate the association between management of Internal Medical Units (IMUs) with outcomes (mortality and length of stay) within the Spanish National Health Service. Design Data on management were obtained from a descriptive transversal study performed among IMUs of the acute hospitals. Outcome indicators were taken from an administrative database of all hospital discharges from the IMUs. Setting Spanish National Health Service. Participants One hundred and twenty-four acute general hospitals with available data of management and outcomes (401 424 discharges). Main Outcome Measures IMU risk standardized mortality rates were calculated using a multilevel model adjusted by Charlson Index. Risk standardized myocardial infarction and heart failure mortality rates were calculated using specific multilevel models. Length of stay was adjusted by complexity. Results Greater hospital complexity was associated with longer average length of stays (r: 0.42; P < 0.001). Crude in-hospital mortality rates were higher at larger hospitals, but no significant differences were found when mortality was risk adjusted. There was an association between nurse workload with mortality rate for selected conditions (r: 0.25; P = 0.009). Safety committee and multidisciplinary ward rounds were also associated with outcomes. Conclusions We have not found any association between complexity and intra-hospital mortality. There is an association between some management indicators with intra-hospital mortality and the length of stay. Better disease-specific outcomes adjustments and a larger number of IMUs in the sample may provide more insights about the association between management of IMUs with healthcare outcomes.
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Affiliation(s)
| | | | | | | | | | - José Luis Bernal
- Control Management Service, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Raquel Barba-Martín
- Department of Internal Medicine, Hospital Universitario Rey Juan Carlos, Móstoles, Madrid, Spain
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Yarnell CJ, Shadowitz S, Redelmeier DA. Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules. Am J Med 2016; 129:706-714.e2. [PMID: 26976386 DOI: 10.1016/j.amjmed.2016.02.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 02/16/2016] [Accepted: 02/17/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Physician call schedules are a critical element for medical practice and hospital efficiency. We compared readmission rates prior to and after a change in physician call system at Sunnybrook Health Sciences Centre. METHODS We studied patients discharged over a decade (2004 through 2013) and identified whether or not each patient was readmitted within the subsequent 28 days. We excluded patients discharged for a surgical, obstetrical, or psychiatric diagnosis. We used time-to-event analysis and time-series analysis to compare rates of readmission prior to and after the physician call system change (January 1, 2009). RESULTS A total of 89,697 patients were discharged, of whom 10,001 (11%) were subsequently readmitted and 4280 died. The risk of readmission was increased by about 26% following physician call system change (9.7% vs 12.2%, P <.001). Time-series analysis confirmed a 26% increase in the readmission rate after call system change (95% confidence interval, 22%-31%; P <.001). The increase in readmission rate after call system change persisted across patients with diverse ages, estimated readmission risks, and medical diagnoses. The net effect was equal to 7240 additional patient days in the hospital following call system change. A modest increase was observed at a nearby acute care hospital that did not change physician call system, and no increase in risk of death was observed with increased hospital readmissions. CONCLUSION We suggest that changes in physician call systems sometimes increase subsequent hospital readmission rates. Further reductions in readmissions may instead require additional resources or ingenuity.
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Affiliation(s)
- Christopher J Yarnell
- Department of Medicine, University of Toronto, Ont., Canada; Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ont., Canada
| | - Steven Shadowitz
- Department of Medicine, University of Toronto, Ont., Canada; Division of General Internal Medicine, University of Toronto, Ont., Canada
| | - Donald A Redelmeier
- Department of Medicine, University of Toronto, Ont., Canada; Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ont., Canada; Division of General Internal Medicine, University of Toronto, Ont., Canada; Institute of Clinical Evaluative Sciences (ICES) in Ontario, Toronto, Canada; Institute for Health Policy Management and Evaluation, Toronto, Ont., Canada.
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McAlister FA, Bakal JA, Rosychuk RJ, Rowe BH. Does Reducing Inpatient Length of Stay Have Upstream Effects on the Emergency Room: Exploring the Impact of the General Internal Medicine Care Transformation Initiative. Acad Emerg Med 2016; 23:711-7. [PMID: 26850577 DOI: 10.1111/acem.12935] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 01/04/2016] [Accepted: 01/09/2016] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The General Internal Medicine (GIM) Care Transformation Initiative implemented at one of four teaching hospitals in the same city resulted in improved efficiency of in-hospital care. Whether it had beneficial effects upstream in the emergency department (ED) is unclear. METHODS Controlled before-after study of ED length of stay (LOS) and crowding metrics for the intervention site (n = 108,951 visits) compared to the three other teaching hospitals (controls, n = 300,930 visits). Our primary outcome was ED LOS for GIM patients but secondary outcomes included ED LOS for all adults and ED crowding metrics. RESULTS The GIM Care Transformation was associated with an additional 2.8-hour reduction in median ED LOS (from 25.6 hours to 13.5 hours) over and above the 9.3-hour decline (from 30.6 hours to 21.3 hours) seen in the three control EDs for GIM patients who were hospitalized (p < 0.001). As less than one in 30 ED visits resulted in a GIM ward admission, the median ED LOS for all patients declined by 15 minutes (from 4.6 hours to 4.3 hours, p < 0.001) in the control hospitals and by 30 minutes (from 5.7 hours to 5.1 hours, p < 0.001) at the intervention hospital pre versus post (p = 0.04 for the 15-minute additional reduction, p < 0.001 for level change on interrupted time series). Other metrics of ED crowding improved by similar amounts at the intervention and control hospitals with no statistically significant differences. CONCLUSION Although the GIM Care Transformation Initiative was associated with substantial reductions in ED LOS for patients admitted to GIM wards at the intervention hospital, it resulted in only minor changes in overall ED LOS and no appreciable changes in ED crowding metrics.
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Affiliation(s)
- Finlay A. McAlister
- Division of General Internal Medicine; University of Alberta; Edmonton Alberta Canada
- Patient Health Outcomes Research and Clinical Effectiveness Unit; University of Alberta; Edmonton Alberta Canada
| | - Jeffrey A. Bakal
- Patient Health Outcomes Research and Clinical Effectiveness Unit; University of Alberta; Edmonton Alberta Canada
- Data Integration Measurement and Reporting; Alberta Health Services; Edmonton Alberta Canada
| | - Rhonda J. Rosychuk
- Department of Pediatrics; University of Alberta; Edmonton Alberta Canada
| | - Brian H. Rowe
- Patient Health Outcomes Research and Clinical Effectiveness Unit; University of Alberta; Edmonton Alberta Canada
- Department of Emergency Medicine and School of Public Health; University of Alberta; Edmonton Alberta Canada
- Emergency Strategic Clinical Network; Alberta Health Services; Edmonton Alberta Canada
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Liu X, Liu Y, Lv Y, Li C, Cui Z, Ma J. Prevalence and temporal pattern of hospital readmissions for patients with type I and type II diabetes. BMJ Open 2015; 5:e007362. [PMID: 26525716 PMCID: PMC4636613 DOI: 10.1136/bmjopen-2014-007362] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Repeated hospitalisation for patients is common and costly, yet partly preventable. However, we know little about readmissions for patients with diabetes in China. The current study aims to assess the frequency and temporal pattern of and risk factors for all-cause readmission among hospitalised patients with diabetes in Tianjin, China. METHOD This retrospective, cohort analysis used the Tianjin Basic Medical Insurance Register System data of 2011. The patterns of and the reasons for all-cause readmissions for patients with diabetes were described. The differences of readmission-free survival (RFS) between newly and previously diagnosed patients were compared. Time-dependent Cox models were established to identify the risk factors for readmission at different time intervals after discharge. RESULTS Readmission rates were approximately 30%, with the most common diagnoses of cerebral infarction (for type I) or diabetes (for type II) for patients with diabetes. The majority of patients were readmitted to the hospital after more than 90 days, followed by 8-30 days (all p=0.002). Approximately 37.2% and 42.8% of readmitted patients with type I and type II diabetes were diagnosed previously, and the RFS rates for previously diagnosed patients were significantly lower than for newly diagnosed patients at any time interval after discharge. Prior history of diabetes (all p<0.05), length of stay (all p<0.01) and reimbursement ratio (90% vs >92%, all p<0.0002) were consistently associated with the RFS for patients readmitted to the hospital at <7, 8-30, 31-60 and 61-90 days. CONCLUSIONS Hospital readmissions among patients with diabetes were affected by the diagnosis status. Patient characteristics and the quality of healthcare might regulate short-interval and long-interval hospital readmission, respectively, after discharge.
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Affiliation(s)
- Xiaoqian Liu
- College of Public Health, Tianjin Medical University, Tianjin, China
| | - Yuanyuan Liu
- College of Public Health, Tianjin Medical University, Tianjin, China
| | - Yuanjun Lv
- Division of General Internal Medicine, Tianjin Hospital, Tianjin, China
| | - Changping Li
- College of Public Health, Tianjin Medical University, Tianjin, China
| | - Zhuang Cui
- College of Public Health, Tianjin Medical University, Tianjin, China
| | - Jun Ma
- College of Public Health, Tianjin Medical University, Tianjin, China
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McAlister FA, Youngson E, Bakal JA, Holroyd-Leduc J, Kassam N. Physician experience and outcomes among patients admitted to general internal medicine teaching wards. CMAJ 2015; 187:1041-1048. [PMID: 26283716 DOI: 10.1503/cmaj.150316] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2015] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Physician scores on examinations decline with time after graduation. However, whether this translates into declining quality of care is unknown. Our objective was to determine how physician experience is associated with negative outcomes for patients admitted to hospital. METHODS We conducted a retrospective cohort study involving all patients admitted to general internal medicine wards over a 2-year period at all 7 teaching hospitals in Alberta, Canada. We used files from the Alberta College of Physicians and Surgeons to determine the number of years since medical school graduation for each patient's most responsible physician. Our primary outcome was the composite of in-hospital death, or readmission or death within 30 days postdischarge. RESULTS We identified 10 046 patients who were cared for by 149 physicians. Patient characteristics were similar across physician experience strata, as were primary outcome rates (17.4% for patients whose care was managed by physicians in the highest quartile of experience, compared with 18.8% in those receiving care from the least experienced physicians; adjusted odds ratio [OR] 0.88, 95% confidence interval [CI] 0.72-1.06). Outcomes were similar between experience quartiles when further stratified by physician volume, most responsible diagnosis or complexity of the patient's condition. Although we found substantial variability in length of stay between individual physicians, there were no significant differences between physician experience quartiles (mean adjusted for patient covariates and accounting for intraphysician clustering: 7.90 [95% CI 7.39-8.42] d for most experienced quartile; 7.63 [95% CI 7.13-8.14] d for least experienced quartile). INTERPRETATION For patients admitted to general internal medicine teaching wards, we saw no negative association between physician experience and outcomes commonly used as proxies for quality of inpatient care.
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Affiliation(s)
- Finlay A McAlister
- Division of General Internal Medicine (McAlister, Kassam); Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Youngson, Bakal), University of Alberta, Edmonton, Alta.; Data Integration Measurement and Reporting (Bakal), Alberta Health Services, Calgary, Alta.; Departments of Medicine and Community Health Sciences (Holroyd-Leduc), University of Calgary, Calgary, Alta.
| | - Erik Youngson
- Division of General Internal Medicine (McAlister, Kassam); Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Youngson, Bakal), University of Alberta, Edmonton, Alta.; Data Integration Measurement and Reporting (Bakal), Alberta Health Services, Calgary, Alta.; Departments of Medicine and Community Health Sciences (Holroyd-Leduc), University of Calgary, Calgary, Alta
| | - Jeffrey A Bakal
- Division of General Internal Medicine (McAlister, Kassam); Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Youngson, Bakal), University of Alberta, Edmonton, Alta.; Data Integration Measurement and Reporting (Bakal), Alberta Health Services, Calgary, Alta.; Departments of Medicine and Community Health Sciences (Holroyd-Leduc), University of Calgary, Calgary, Alta
| | - Jayna Holroyd-Leduc
- Division of General Internal Medicine (McAlister, Kassam); Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Youngson, Bakal), University of Alberta, Edmonton, Alta.; Data Integration Measurement and Reporting (Bakal), Alberta Health Services, Calgary, Alta.; Departments of Medicine and Community Health Sciences (Holroyd-Leduc), University of Calgary, Calgary, Alta
| | - Narmin Kassam
- Division of General Internal Medicine (McAlister, Kassam); Patient Health Outcomes Research and Clinical Effectiveness Unit (McAlister, Youngson, Bakal), University of Alberta, Edmonton, Alta.; Data Integration Measurement and Reporting (Bakal), Alberta Health Services, Calgary, Alta.; Departments of Medicine and Community Health Sciences (Holroyd-Leduc), University of Calgary, Calgary, Alta
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17
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Pendharkar SR, Woiceshyn J, da Silveira GJC, Bischak D, Flemons W, McAlister F, Ghali WA. What happens when healthcare innovations collide? BMJ Qual Saf 2015; 25:9-13. [PMID: 26271920 DOI: 10.1136/bmjqs-2015-004441] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 07/21/2015] [Indexed: 11/04/2022]
Affiliation(s)
- Sachin R Pendharkar
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
| | - Jaana Woiceshyn
- Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada
| | | | - Diane Bischak
- O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada
| | - Ward Flemons
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
| | | | - William A Ghali
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
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18
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Portela MC, Pronovost PJ, Woodcock T, Carter P, Dixon-Woods M. How to study improvement interventions: a brief overview of possible study types. Postgrad Med J 2015; 91:343-54. [PMID: 26045562 PMCID: PMC4484358 DOI: 10.1136/postgradmedj-2014-003620rep] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Improvement (defined broadly as purposive efforts to secure positive change) has become an increasingly important activity and field of inquiry within healthcare. This article offers an overview of possible methods for the study of improvement interventions. The choice of available designs is wide, but debates continue about how far improvement efforts can be simultaneously practical (aimed at producing change) and scientific (aimed at producing new knowledge), and whether the distinction between the practical and the scientific is a real and useful one. Quality improvement projects tend to be applied and, in some senses, self-evaluating. They are not necessarily directed at generating new knowledge, but reports of such projects if well conducted and cautious in their inferences may be of considerable value. They can be distinguished heuristically from research studies, which are motivated by and set out explicitly to test a hypothesis, or otherwise generate new knowledge, and from formal evaluations of improvement projects. We discuss variants of trial designs, quasi-experimental designs, systematic reviews, programme evaluations, process evaluations, qualitative studies, and economic evaluations. We note that designs that are better suited to the evaluation of clearly defined and static interventions may be adopted without giving sufficient attention to the challenges associated with the dynamic nature of improvement interventions and their interactions with contextual factors. Reconciling pragmatism and research rigour is highly desirable in the study of improvement. Trade-offs need to be made wisely, taking into account the objectives involved and inferences to be made.
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Affiliation(s)
- Margareth Crisóstomo Portela
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK Department of Health Administration and Planning, National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
| | - Peter J Pronovost
- Departments of Anesthesiology, Critical Care Medicine, and Surgery, Armstrong Institute for Patient Safety and Quality, School of Medicine, and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Woodcock
- NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, London, UK
| | - Pam Carter
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK
| | - Mary Dixon-Woods
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK
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19
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Pannick S, Beveridge I, Ashrafian H, Long SJ, Athanasiou T, Sevdalis N. A stepped wedge, cluster controlled trial of an intervention to improve safety and quality on medical wards: the HEADS-UP study protocol. BMJ Open 2015; 5:e007510. [PMID: 26100026 PMCID: PMC4479997 DOI: 10.1136/bmjopen-2014-007510] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
INTRODUCTION The majority of preventable deaths in healthcare are due to errors on general wards. Staff perceptions of safety correlate with patient survival, but effectively translating ward teams' concerns into tangibly improved care remains problematic. The Hospital Event Analysis Describing Significant Unanticipated Problems (HEADS-UP) trial evaluates a structured, multidisciplinary team briefing, capturing safety threats and adverse events, with rapid feedback to clinicians and service managers. This is the first study to rigorously assess a simpler intervention for general medical units, alongside an implementation model applicable to routine clinical practice. METHODS/ANALYSIS 7 wards from 2 hospitals will progressively incorporate the intervention into daily practice over 14 months. Wards will adopt HEADS-UP in a pragmatic sequence, guided by local clinical enthusiasm. Initial implementation will be facilitated by a research lead, but rapidly delegated to clinical teams. The primary outcome is excess length of stay (a surplus stay of 24 h or more, compared to peer institutions' Healthcare Resource Groups-predicted length of stay). Secondary outcomes are 30-day readmission or excess length of stay; in-hospital death or death/readmission within 30 days; healthcare-acquired infections; processes of escalation of care; use of traditional incident-reporting systems; and patient safety and teamwork climates. HEADS-UP will be analysed as a stepped wedge cluster controlled trial. With 7840 patients, using best and worst case predictions, the study would achieve between 75% and 100% power to detect a 2-14% absolute risk reduction in excess length of stay (two-sided p<0.05). Regression analysis will use generalised linear mixed models or generalised estimating equations, and a time-to-event regression model. A qualitative analysis will evaluate facilitators and barriers to HEADS-UP implementation and impact. ETHICS AND DISSEMINATION Participating institutions' Research and Governance departments approved the study. Results will be published in peer-reviewed journals and at conference presentations. TRIAL REGISTRATION NUMBER ISRCTN34806867.
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Affiliation(s)
- Samuel Pannick
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK West Middlesex University Hospital NHS Trust, London, UK
| | - Iain Beveridge
- West Middlesex University Hospital NHS Trust, London, UK
| | - Hutan Ashrafian
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Susannah J Long
- NIHR Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | | | - Nick Sevdalis
- Centre for Implementation Science, Health Service & Population Research Department, King's College London, London, UK
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20
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Portela MC, Pronovost PJ, Woodcock T, Carter P, Dixon-Woods M. How to study improvement interventions: a brief overview of possible study types. BMJ Qual Saf 2015; 24:325-36. [PMID: 25810415 PMCID: PMC4413733 DOI: 10.1136/bmjqs-2014-003620] [Citation(s) in RCA: 176] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 02/16/2015] [Indexed: 12/16/2022]
Abstract
Improvement (defined broadly as purposive efforts to secure positive change) has become an increasingly important activity and field of inquiry within healthcare. This article offers an overview of possible methods for the study of improvement interventions. The choice of available designs is wide, but debates continue about how far improvement efforts can be simultaneously practical (aimed at producing change) and scientific (aimed at producing new knowledge), and whether the distinction between the practical and the scientific is a real and useful one. Quality improvement projects tend to be applied and, in some senses, self-evaluating. They are not necessarily directed at generating new knowledge, but reports of such projects if well conducted and cautious in their inferences may be of considerable value. They can be distinguished heuristically from research studies, which are motivated by and set out explicitly to test a hypothesis, or otherwise generate new knowledge, and from formal evaluations of improvement projects. We discuss variants of trial designs, quasi-experimental designs, systematic reviews, programme evaluations, process evaluations, qualitative studies, and economic evaluations. We note that designs that are better suited to the evaluation of clearly defined and static interventions may be adopted without giving sufficient attention to the challenges associated with the dynamic nature of improvement interventions and their interactions with contextual factors. Reconciling pragmatism and research rigour is highly desirable in the study of improvement. Trade-offs need to be made wisely, taking into account the objectives involved and inferences to be made.
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Affiliation(s)
- Margareth Crisóstomo Portela
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK Department of Health Administration and Planning, National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
| | - Peter J Pronovost
- Departments of Anesthesiology, Critical Care Medicine, and Surgery, Armstrong Institute for Patient Safety and Quality, School of Medicine, and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Woodcock
- NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, London, UK
| | - Pam Carter
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK
| | - Mary Dixon-Woods
- Social Science Applied to Healthcare Research (SAPPHIRE) Group, Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK
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21
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McAlister FA, Youngson E, Padwal RS, Majumdar SR. Similar outcomes among general medicine patients discharged on weekends. J Hosp Med 2015; 10:69-74. [PMID: 25537769 DOI: 10.1002/jhm.2310] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Revised: 10/20/2014] [Accepted: 10/28/2014] [Indexed: 11/08/2022]
Abstract
BACKGROUND Hospitals reduce staffing levels and services on weekends. This raises the question of whether weekend discharges may be inadequately prepared and thus at higher risk for adverse events postdischarge. OBJECTIVE To compare death or nonelective readmission rates 30 days after weekend versus weekday discharge. DESIGN Retrospective cohort. SETTING All teaching hospitals in Alberta, Canada. PATIENTS General internal medicine (GIM) discharges (only 1 per patient). MEASUREMENTS Analyses were adjusted for demographics, comorbidity, and length of stay based on a previously validated index. RESULTS Of 7991 patients (mean age, 62.1 years; 51.9% male; mean Charlson 2.56; 57.5% LACE ≥10) discharged from 7 teaching hospitals, 1146 (14.3%) were discharged on a weekend. Although they had substantially shorter lengths of stay (5.64 days, 95% confidence interval [CI]: 5.35-5.93 vs 7.86 days, 95% CI: 7.71-8.00, adjusted P value < 0.0001) and were less likely to be discharged with homecare support (10.9% vs 19.3%) or to long-term care facilities (3.1% vs 7.8%), patients discharged on weekends exhibited similar rates of death or readmission at 30 days compared to those discharged on weekdays (10.6% vs 13.2%, adjusted odds ratio [aOR]: 0.94, 95% CI: 0.77-1.16), even among the 4591 patients deemed to be at high risk for postdischarge events based on LACE (length of hospital stay, acuity of admission, comorbidity burden quantified using the Charlson Comorbidity Index, and emergency department visits in the 6 months prior to admission) score ≥10 (16.8% vs 16.5% for weekday discharges, aOR: 1.09 [95% CI: 0.85-1.41]). CONCLUSIONS GIM patients discharged from teaching hospitals on weekends have shorter lengths of stay and exhibit similar postdischarge outcomes as patients discharged on weekdays.
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Affiliation(s)
- Finlay A McAlister
- Division of General Internal Medicine, University of Alberta, Edmonton, Alberta, Canada; Patient Health Outcomes Research and Clinical Effectiveness Unit, University of Alberta, Edmonton, Alberta, Canada
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Pannick S, Beveridge I, Wachter RM, Sevdalis N. Improving the quality and safety of care on the medical ward: A review and synthesis of the evidence base. Eur J Intern Med 2014; 25:874-87. [PMID: 25457434 DOI: 10.1016/j.ejim.2014.10.013] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Revised: 09/13/2014] [Accepted: 10/13/2014] [Indexed: 11/17/2022]
Abstract
Despite its place at the heart of inpatient medicine, the evidence base underpinning the effective delivery of medical ward care is highly fragmented. Clinicians familiar with the selection of evidence-supported treatments for specific diseases may be less aware of the evolving literature surrounding the organisation of care on the medical ward. This review is the first synthesis of that disparate literature. An iterative search identified relevant publications, using terms pertaining to medical ward environments, and objective and subjective patient outcomes. Articles (including reviews) were selected on the basis of their focus on medical wards, and their relevance to the quality and safety of ward-based care. Responses to medical ward failings are grouped into five common themes: staffing levels and team composition; interdisciplinary communication and collaboration; standardisation of care; early recognition and treatment of the deteriorating patient; and local safety climate. Interventions in these categories are likely to improve the quality and safety of care in medical wards, although the evidence supporting them is constrained by methodological limitations and inadequate investment in multicentre trials. Nonetheless, with infrequent opportunities to redefine their services, institutions are increasingly adopting multifaceted strategies that encompass groups of these themes. As the literature on the quality of inpatient care moves beyond its initial focus on the intensive care unit and operating theatre, physicians should be mindful of opportunities to incorporate evidence-based practice at a ward level.
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Affiliation(s)
- Samuel Pannick
- NIHR Patient Safety Translational Research Centre, Imperial College London, and West Middlesex University Hospital NHS Trust, UK.
| | | | - Robert M Wachter
- Division of Hospital Medicine, University of CA, San Francisco, USA.
| | - Nick Sevdalis
- NIHR Patient Safety Translational Research Centre, Imperial College London, UK.
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