Alberta's Acute Care Funding Plan: Update to December 1994

1995 ◽  
Vol 8 (2) ◽  
pp. 17-22 ◽  
Author(s):  
Philip Jacobs ◽  
Edward M. Hall ◽  
Richard H.M. Plain

From 1990 until 1994 Alberta Health adjusted the acute care portion of hospital budgets based on a case mix index, initially called the Hospital Performance Index (HPI). The HPI formula method was a temporary measure; in November 1993, Alberta Health announced that, commencing in 1994, hospitals would be funded on a prospective basis, although they would still use the core of the HPI in the setting of funding rates. The creation of 17 health regions in June 1994 created the need for a new system of funding which would supplant the modified prospective system. In this paper we review the evolution of the HPI plan and its individual components — patient data, patient classification, funding weights, inpatient costs and adjustment factors.

1992 ◽  
Vol 5 (3) ◽  
pp. 4-11 ◽  
Author(s):  
Philip Jacobs ◽  
Edward M. Hall ◽  
Judith R. Lave ◽  
Murray Glendining

Alberta initiated the Acute Care Funding Project (ACFP) in 1988, a new hospital funding system that institutes case mix budgeting adjustments to the global budget so that hospitals can be treated more equitably. The initiative is a significant departure in principle from the former method of funding. The ACFP is summarized and critiqued, and focuses on the inpatient side of the picture. The various elements of the project are discussed, such as the hospital performance index, the hospital performance measure, the Refined Diagnostic Related Group, case weights, typical and outlier cases, and the costing mechanisms. Since its implementation, the ACFP has undergone substantial changes; these are discussed, as well as some of the problems that still need to be addressed. Overall, the system offers incentives to reduce length of stay and to increase the efficiency with which inpatient care is provided.


2016 ◽  
Vol 32 (3) ◽  
pp. 254-260 ◽  
Author(s):  
Richard L. Fuller ◽  
Norbert I. Goldfield ◽  
Richard F. Averill ◽  
John S. Hughes

In October 2014, the Centers for Medicare & Medicaid Services began reducing Medicare payments by 1% for the bottom performing quartile of hospitals under the Hospital-Acquired Condition Reduction Program (HACRP). A tight clustering of HACRP scores around the penalty threshold was observed resulting in 13.2% of hospitals being susceptible to a shift in penalty status related to single decile changes in the ranking of any one of the complication or infection measures used to compute the HACRP score. The HACRP score also was found to be significantly correlated with several hospital characteristics including hospital case mix index. This correlation was not confirmed when an alternative method of measuring hospital complication performance was used. The sensitivity of the HACRP penalties to small changes in performance and correlation of the HACRP score with hospital characteristics call into question the validity of the HACRP measure and method of risk adjustment.


2021 ◽  
Vol 10 (2) ◽  
pp. e001230
Author(s):  
Michael Reid ◽  
George Kephart ◽  
Pantelis Andreou ◽  
Alysia Robinson

BackgroundRisk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates.MethodsUsing hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence.ResultsCommunity of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management.ConclusionContextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.


2020 ◽  
Vol 41 (S1) ◽  
pp. s104-s105
Author(s):  
Ye Shen ◽  
Jennifer Ellison ◽  
Uma Chandran ◽  
Sumana Fathima ◽  
Jamil Kanji ◽  
...  

Background: This review describes the epidemiology of carbapenemase-producing organisms (CPO) in both the community and hospitalized populations in the province of Alberta. Methods: Newly identified CPO-positive individuals from April 1, 2013, to March 31, 2018, were retrospectively reviewed from 3 data sources, which shared a common provincial CPO case definition: (1) positive CPO results from the Provincial Laboratory for Public Health, which provides all referral and confirmatory CPO testing, (2) CPO cases reported to Alberta Health, and (3) CPO surveillance from Alberta Health Services Infection Prevention and Control (IPC). The 3 data sources were collated, and initial CPO cases were classified according to their likely location of acquisition: hospital-acquired, hospital-identified, on admission, and community-identified. Risk factors and adverse outcomes were obtained from linkage to administrative data. Results: In total, 171 unique individuals were newly identified with a first-time CPO case. Also, 15% (25 of 171) were hospital-acquired (HA), 21% (36 of 171) were hospital-identified (HI), 33% (57 of 171) were on admission, and 31% (53 of 171) were community identified. Overall, 9% (5 of 171) resided in long-term care facilities. Of all patients in acute-care facilities, 30% (35 of 118) had infections and 70% were colonized. Overall, 38% (65 of 171) had an acute-care admission in the 1 year prior to CPO identification; 59% (63 of 106) of those who did not have a previous admission had received healthcare outside Alberta. A large proportion of on-admission cases (81%, 46 of 57) and community-identified (66%, 33 of 53) cases did not have any acute-care admissions in Alberta in the previous year. Overall, 10% (14 of 171) had ICU admissions in Alberta within 30 days of CPO identification, and 5% (8 of 171) died within 30 days. The most common carbapenemase gene identified was NDM-1 (53%, 90 of 171). Conclusions: These findings highlight the robust nature of Alberta’s provincial CPO surveillance network. We reviewed 3 different databases (laboratory, health ministry, IPC) to obtain comprehensive data to better understand the epidemiology of CPO in both the community and hospital settings. More than half of the individuals with CPO were initially identified in the community or on admission. Most had received healthcare outside Alberta, and no acute-care admissions occurred in Alberta in the previous year. It is important to be aware of the growing reservoir of CPO outside the hospital setting because it could impact future screening and management practices.Funding: NoneDisclosures: None


2020 ◽  
Author(s):  
Thomas Gross ◽  
Felix Amsler

Zusammenfassung Hintergrund Es galt herauszufinden, wie kostendeckend die Versorgung potenziell Schwerverletzter in einem Schweizer Traumazentrum ist, und inwieweit Spitalgewinne bzw. -verluste mit patientenbezogenen Unfall‑, Behandlungs- oder Outcome-Daten korrelieren. Methodik Analyse aller 2018 im Schockraum (SR) bzw. mit Verletzungsschwere New Injury Severity Score (NISS) ≥8 notfallmäßig stationär behandelter Patienten eines Schwerverletztenzentrums der Schweiz (uni- und multivariate Analyse; p < 0,05). Ergebnisse Für das Studienkollektiv (n = 513; Ø NISS = 18) resultierte gemäß Spitalkostenträgerrechnung ein Defizit von 1,8 Mio. CHF. Bei einem Gesamtdeckungsgrad von 86 % waren 66 % aller Fälle defizitär (71 % der Allgemein- vs. 42 % der Zusatzversicherten; p < 0,001). Im Mittel betrug das Defizit 3493.- pro Patient (allg. Versicherte, Verlust 4545.-, Zusatzversicherte, Gewinn 1318.-; p < 0,001). Auch „in“- und „underlier“ waren in 63 % defizitär. SR-Fälle machten häufiger Verlust als Nicht-SR-Fälle (73 vs. 58 %; p = 0,002) wie auch Traumatologie- vs. Neurochirurgiefälle (72 vs. 55 %; p < 0,001). In der multivariaten Analyse ließen sich 43 % der Varianz erhaltener Erlöse mit den untersuchten Variablen erklären. Hingegen war der ermittelte Deckungsgrad nur zu 11 % (korr. R2) durch die Variablen SR, chirurgisches Fachgebiet, Intensivaufenthalt, Thoraxverletzungsstärke und Spitalletalität zu beschreiben. Case-Mix-Index gemäß aktuellen Diagnosis Related Groups (DRG) und Versicherungsklasse addierten weitere 13 % zu insgesamt 24 % erklärter Varianz. Diskussion Die notfallmäßige Versorgung potenziell Schwerverletzter an einem Schweizer Traumazentrum erweist sich nur in einem Drittel der Fälle als zumindest kostendeckend, dies v. a. bei Zusatzversicherten, Patienten mit einem hohen Case-Mix-Index oder einer IPS- bzw. kombinierten Polytrauma- und Schädel-Hirn-Trauma-DRG-Abrechnungsmöglichkeit.


2019 ◽  
Vol 29 (7) ◽  
pp. 1972-1986
Author(s):  
Bo Chen ◽  
Keith A Lawson ◽  
Antonio Finelli ◽  
Olli Saarela

There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identify good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.


1996 ◽  
Vol 27 (9) ◽  
pp. 31???32 ◽  
Author(s):  
TIMOTHY P. ADAMS
Keyword(s):  
Case Mix ◽  

Sign in / Sign up

Export Citation Format

Share Document