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A decision tool for negotiating home care funding levels in Ontario.

https://arctichealth.org/en/permalink/ahliterature166900
Source
Home Health Care Serv Q. 2006;25(3-4):91-106
Publication Type
Article
Date
2006
Author
Carolyn R Busby
Michael W Carter
Author Affiliation
Mechanical and Industrial Engineering, University of Toronto, ON, Canada. busby@mie.utoronto.ca
Source
Home Health Care Serv Q. 2006;25(3-4):91-106
Date
2006
Language
English
Publication Type
Article
Keywords
Decision Support Techniques
Home Care Services - economics
Humans
Negotiating
Ontario
Abstract
This paper describes a decision tool created for the Simcoe County Community Care Access Center (SCCCAC) in Ontario. The tool allows the SCCCAC to quantitatively assess the trade-offs between cost, quality, and waiting time of their home care patients. This information can then be used to negotiate reasonable funding levels with the Ontario government and to appropriately allocate this funding among the various patient groups at the SCCCAC. This work can be expanded to other health care organizations that use prioritized waiting lists.
PubMed ID
17062513 View in PubMed
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How much do operational processes affect hospital inpatient discharge rates?

https://arctichealth.org/en/permalink/ahliterature150786
Source
J Public Health (Oxf). 2009 Dec;31(4):546-53
Publication Type
Article
Date
Dec-2009
Author
Hannah Wong
Robert C Wu
George Tomlinson
Michael Caesar
Howard Abrams
Michael W Carter
Dante Morra
Author Affiliation
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Source
J Public Health (Oxf). 2009 Dec;31(4):546-53
Date
Dec-2009
Language
English
Publication Type
Article
Keywords
Aged
Aged, 80 and over
Female
Hospital Administration
Hospitals, General
Humans
Internal Medicine
Male
Middle Aged
Multivariate Analysis
Ontario
Patient Discharge - trends
Retrospective Studies
Abstract
The objective of this study is to determine the effect of day of the week, holiday, team admission and rotation schedules, individual attending physicians and their length of coverage on daily team discharge rates.
We conducted a retrospective analysis of the General Internal Medicine (GIM) inpatient service at our institution for years 2005 and 2006, which included 5088 patients under GIM care.
Weekend discharge rate was more than 50% lower compared with reference rates whereas Friday rates were 24% higher. Holiday Monday discharge rates were 65% lower than regular Mondays, with an increase in pre-holiday discharge rates. Teams that were on-call or that were on call the next day had 15% higher discharge rates compared with reference whereas teams that were post-call had 20% lower rates. Individual attending physicians and length of attending coverage contributed small variations in discharge rates. Resident scheduling was not a significant predictor of discharge rates.
Day of the week and holidays followed by team organization and scheduling are significant predictors of daily variation in discharge rates. Introducing greater holiday and weekend capacity as well as reorganizing internal processes such as admitting and attending schedules may potentially optimize discharge rates.
PubMed ID
19465455 View in PubMed
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Physician workload and the Canadian Emergency Department Triage and Acuity Scale: the Predictors of Workload in the Emergency Room (POWER) Study.

https://arctichealth.org/en/permalink/ahliterature149716
Source
CJEM. 2009 Jul;11(4):321-9
Publication Type
Article
Date
Jul-2009
Author
Chris K Anderson
Gregory S Zaric
Jonathan F Dreyer
Michael W Carter
Shelley L McLeod
Author Affiliation
School of Hotel Administration, Cornell University, New York, New York, Unites States.
Source
CJEM. 2009 Jul;11(4):321-9
Date
Jul-2009
Language
English
Publication Type
Article
Keywords
Adult
Confidence Intervals
Emergency Service, Hospital - manpower
Female
Humans
Male
Middle Aged
Ontario
Physicians - statistics & numerical data
Prospective Studies
Triage - manpower
Workload
Abstract
The Canadian Emergency Department Triage and Acuity Scale (CTAS) is a 5-level triage tool used to determine the priority by which patients should be treated in Canadian emergency departments (EDs). To determine emergency physician (EP) workload and staffing needs, many hospitals in Ontario use a case-mix formula based solely on patient volume at each triage level. The purpose of our study was to describe the distribution of EP time by activity during a shift in order to estimate the amount of time required by an EP to assess and treat patients in each triage category and to determine the variability in the distribution of CTAS scoring between hospital sites.
Research assistants directly observed EPs for 592 shifts and electronically recorded their activities on a moment-by-moment basis. The duration of all activities associated with a given patient were summed to derive a directly observed estimate of the amount of EP time required to treat the patient.
We observed treatment times for 11 716 patients in 11 hospital-based EDs. The mean time for physicians to treat patients was 73.6 minutes (95% confidence interval [CI] 63.6-83.7) for CTAS level 1, 38.9 minutes (95% CI 36.0-41.8) for CTAS-2, 26.3 minutes (95% CI 25.4-27.2) for CTAS-3, 15.0 minutes (95% CI 14.6-15.4) for CTAS-4 and 10.9 minutes (95% CI 10.1-11.6) for CTAS-5. Physician time related to patient care activities accounted for 84.2% of physicians' ED shifts.
In our study, EPs had very limited downtime. There was significant variability in the distribution of CTAS scores between sites and also marked variation in EP time related to each triage category. This brings into question the appropriateness of using CTAS alone to determine physician staffing levels in EDs.
PubMed ID
19594970 View in PubMed
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Understanding hospital and emergency department congestion: an examination of inpatient admission trends and bed resources.

https://arctichealth.org/en/permalink/ahliterature146040
Source
CJEM. 2010 Jan;12(1):18-26
Publication Type
Article
Date
Jan-2010
Author
Hannah J Wong
Dante Morra
Michael Caesar
Michael W Carter
Howard Abrams
Author Affiliation
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario.
Source
CJEM. 2010 Jan;12(1):18-26
Date
Jan-2010
Language
English
Publication Type
Article
Keywords
Aged
Aged, 80 and over
Emergency Service, Hospital - utilization
Female
Health Services Accessibility - statistics & numerical data
Hospital Bed Capacity - statistics & numerical data
Hospitals, General - utilization
Humans
Length of Stay - statistics & numerical data
Male
Middle Aged
Ontario
Patient Admission - trends
Patient Discharge - statistics & numerical data
Resource Allocation - statistics & numerical data
Retrospective Studies
Abstract
Patients in the emergency department (ED) who have been admitted to hospital (inpatient "boarders") are associated with ED overcrowding. They are also a symptom of a hospital-wide imbalance between demand and supply of resources. We analyzed the trends of inpatient admissions, ED boarding volumes, lengths of stay and bed resources of 3 major admitting services at our teaching institution.
We used hospital databases from Jan. 1, 2004, to Dec. 31, 2007, to analyze ED visits that resulted in admission to hospital.
During the study period, 21 986 ED patients were admitted to hospital. The percentage of cancer-related admissions to the oncology admitting service decreased from 48% in 2004 to 24% in 2007, and admissions to general internal medicine (GIM) increased nearly 2-fold, from 28% in 2004 to 54% in 2007. In addition, GIM admitted about 10% more myocardial infarction and heart failure patients than did cardiology. General internal medicine constituted the majority of ED boarders and had a median boarding length of stay of approximately 15 hours. Inpatient beds on oncology and cardiology services remained static.
Without bed capacity to admit more patients, our specialty services relied on GIM to serve as a safety net. At the same time, GIM was cited as a main source of ED congestion as their patients occupied more ED beds for longer periods than any other admitting service. The data presented in this study has helped effect positive change within our institution. Other hospitals running at or near capacity and faced with similar ED congestion may apply the methods we used in this study to analyze the cause and nature of their situation.
PubMed ID
20078914 View in PubMed
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