Implementation of Case Costing with Ontario Case Costing Initiative (OCCI)

2013 ◽  
pp. 79-88
Author(s):  
Thuy Thi Thanh Hoang
Keyword(s):  
Decision Making ◽  
Health Care Professionals ◽  
Long Term Care ◽  
Statistical Data ◽  
Evidence Based ◽  
Term Care ◽  
Patient Level ◽  
Evidence Based Decision Making ◽  
Gain Access

Over the past decade there has been a tremendous spread of computerized systems in hospitals. The advancement provided an opportunity for hospitals to gain access to computerized clinical, financial, and statistical data. Case costing information is the integration of clinical, financial, and statistical data to provide costing information at the patient level. Ontario Case Costing Initiative (OCCI) is an undertaking of the Ontario Ministry of Health and Long-Term Care (MOHLTC). This chapter focuses on the implementation of case costing using OCCI as a guideline for a hospital. It addresses the process of implementation by discussing proposals for planning, implementing, transitioning, and evaluation of case costing. The adoption of the OCCI allows health care professionals to analyze integrated health information and further enables evidence-based decision making.

Implementation of Case Costing with Ontario Case Costing Initiative (OCCI)

2015 ◽  
pp. 805-814 ◽  
Author(s):  
Thuy Thi Thanh Hoang
Keyword(s):  
Decision Making ◽  
Health Care Professionals ◽  
Long Term Care ◽  
Statistical Data ◽  
Evidence Based ◽  
Term Care ◽  
Patient Level ◽  
Evidence Based Decision Making ◽  
Gain Access

Over the past decade there has been a tremendous spread of computerized systems in hospitals. The advancement provided an opportunity for hospitals to gain access to computerized clinical, financial, and statistical data. Case costing information is the integration of clinical, financial, and statistical data to provide costing information at the patient level. Ontario Case Costing Initiative (OCCI) is an undertaking of the Ontario Ministry of Health and Long-Term Care (MOHLTC). This chapter focuses on the implementation of case costing using OCCI as a guideline for a hospital. It addresses the process of implementation by discussing proposals for planning, implementing, transitioning, and evaluation of case costing. The adoption of the OCCI allows health care professionals to analyze integrated health information and further enables evidence-based decision making.

scholarly journals Long-Term Care Regulations in Ontario, Canada during COVID-19

2021 ◽  
Vol 17 (23) ◽  
pp. 1
Author(s):  
Steve Hunt ◽  
Elena Hunt
Keyword(s):  
Health Care ◽  
Population Health ◽  
Health Care Professionals ◽  
Constitutional Law ◽  
Long Term Care ◽  
Statistical Data ◽  
Administrative Law ◽  
Term Care ◽  
The Impact

The Ontario, Canada statutory requirements of Nurse Patient ratios and other Health care professionals’ activities are discussed following a descriptive, analytical and investigative approach. Using OECD Statistics and evaluating the impact on population health during COVID-19, as well as drawing from constitutional law and administrative law, the authors apply, explain and clarify opinions of jurists, rulings of judges and arbitrators and pull comparisons to statutory and statistical data from international jurisdictions such as Australia and USA. Recommendations for improvement of the Ontario Health Care system are conferred.

Design and Development of a Web-based Delirium Preventive Application for Long-term Care Facilities: A Methodological Study (Preprint)

2020 ◽  
Author(s):  
Kyoung Ja Moon ◽  
Chang-Sik Son ◽  
Jong-Ha Lee ◽  
Mina Park
Keyword(s):  
Risk Factors ◽  
Long Term Care ◽  
Cloud System ◽  
Evidence Based ◽  
Methodological Study ◽  
Term Care ◽  
Web Based ◽  
Care Facilities ◽  
Delirium Prevention

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none

Evidence-based deprescribing of proton pump inhibitors in long-term care

2018 ◽  
Vol 14 (2) ◽  
pp. 124-126 ◽  
Author(s):  
Janet K. Sluggett ◽  
Ivanka Hendrix ◽  
J. Simon Bell
Keyword(s):  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Long Term Care ◽  
Evidence Based ◽  
Term Care

scholarly journals Automated Nationwide Benchmarking Dashboard for Antimicrobial Stewardship Programs within the Veterans’ Health Administration

2021 ◽  
Vol 1 (S1) ◽  
pp. s23-s24
Author(s):  
Michihiko Goto ◽  
Eli Perencevich ◽  
Alexandre Marra ◽  
Bruce Alexander ◽  
Brice Beck ◽  
...  
Keyword(s):  
Acute Care ◽  
Antimicrobial Stewardship ◽  
Risk Adjustment ◽  
Long Term Care ◽  
Antimicrobial Use ◽  
Veterans Health ◽  
Health Administration ◽  
Term Care ◽  
Patient Level

Group Name: VHA Center for Antimicrobial Stewardship and Prevention of Antimicrobial Resistance (CASPAR) Background: Antimicrobial stewardship programs (ASPs) are advised to measure antimicrobial consumption as a metric for audit and feedback. However, most ASPs lack the tools necessary for appropriate risk adjustment and standardized data collection, which are critical for peer-program benchmarking. We created a system that automatically extracts antimicrobial use data and patient-level factors for risk-adjustment and a dashboard to present risk-adjusted benchmarking metrics for ASP within the Veterans’ Health Administration (VHA). Methods: We built a system to extract patient-level data for antimicrobial use, procedures, demographics, and comorbidities for acute inpatient and long-term care units at all VHA hospitals utilizing the VHA’s Corporate Data Warehouse (CDW). We built baseline negative binomial regression models to perform risk-adjustments based on patient- and unit-level factors using records dated between October 2016 and September 2018. These models were then leveraged both retrospectively and prospectively to calculate observed-to-expected ratios of antimicrobial use for each hospital and for specific units within each hospital. Data transformation and applications of risk-adjustment models were automatically performed within the CDW database server, followed by monthly scheduled data transfer from the CDW to the Microsoft Power BI server for interactive data visualization. Frontline antimicrobial stewards at 10 VHA hospitals participated in the project as pilot users. Results: Separate baseline risk-adjustment models to predict days of therapy (DOT) for all antibacterial agents were created for acute-care and long-term care units based on 15,941,972 patient days and 3,011,788 DOT between October 2016 and September 2018 at 134 VHA hospitals. Risk adjustment models include month, unit types (eg, intensive care unit [ICU] vs non-ICU for acute care), specialty, age, gender, comorbidities (50 and 30 factors for acute care and long-term care, respectively), and preceding procedures (45 and 24 procedures for acute care and long-term care, respectively). We created additional models for each antimicrobial category based on National Healthcare Safety Network definitions. For each hospital, risk-adjusted benchmarking metrics and a monthly ranking within the VHA system were visualized and presented to end users through the dashboard (an example screenshot in Figure 1). Conclusions: Developing an automated surveillance system for antimicrobial consumption and risk-adjustment benchmarking using an electronic medical record data warehouse is feasible and can potentially provide valuable tools for ASPs, especially at hospitals with no or limited local informatics expertise. Future efforts will evaluate the effectiveness of dashboards in these settings.Funding: NoDisclosures: None

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