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Terri Jackson

Publications and source records attributed to Terri Jackson.

8 recordsLinked to original sources
PubMed2006

The incidence and cost of adverse events in Victorian hospitals 2003-04.

OBJECTIVES: To determine the incidence of adverse events in patients admitted in the year 2003-04 to selected Victorian hospitals; to identify the main hospital-acquired diagnoses; and to estimate the cost of these complications to the Victorian and Australian health system. DESIGN: The patient-level costing dataset for major Victorian public hospitals, 1 July 2003-30 June 2004, was analysed for adverse events by identifying C-prefixed diagnosis codes denoting complications, preventable or otherwise, arising during the course of hospital treatment. The in-hospital cost of adverse events was estimated using linear regression modelling, adjusting for age and comorbidity. MAIN OUTCOME MEASURES: Cost of each patient admission ("admitted episode"), length of stay and mortality. RESULTS: During the designated timeframe, 979,834 admitted episodes were in the sample, of which 67,435 (6.88%) had at least one adverse event. Patients with adverse events stayed about 10 days longer and had over seven times the risk of in-hospital death than those without complications. After adjusting for age and comorbidity, the presence of an adverse event adds dollar 6826 to the cost of each admitted episode. The total cost of adverse events in this dataset in 2003-04 was dollar 460.311 million, representing 15.7% of the total expenditure on direct hospital costs, or an additional 18.6% of the total inpatient hospital budget. CONCLUSION: Adverse events are associated with significant costs. Administrative datasets are a cost-effective source of information that can be used for a range of clinical governance activities to prevent adverse events.

Adolescent↗
PubMed2006

Prospective casemix-based funding, analysis and financial impact of cost outliers in all-patient refined diagnosis related groups in three Belgian general hospitals.

This study examined the impact of cost outliers in term of hospital resources consumption, the financial impact of the outliers under the Belgium casemix-based system, and the validity of two "proxies" for costs: length of stay and charges. The cost of all hospital stays at three Belgian general hospitals were calculated for the year 2001. High resource use outliers were selected according to the following rule: 75th percentile +1.5 xinter-quartile range. The frequency of cost outliers varied from 7% to 8% across hospitals. Explanatory factors were: major or extreme severity of illness, longer length of stay, and intensive care unit stay. Cost outliers account for 22-30% of hospital costs. One-third of length-of-stay outliers are not cost outliers, and nearly one-quarter of charges outliers are not cost outliers. The current funding system in Belgium does not penalize hospitals having a high percentage of outliers. The billing generated by these patients largely compensates for costs generated. Length of stay and charges are not a good approximation to select cost outliers.

Adult↗
PubMed2006

Analysis of cost outliers within APR-DRGs in a Belgian general hospital: two complementary approaches.

CONTEXT AND OBJECTIVES: The objective of this study was to find factors that could explain high and low resource use outliers, by associating an explanatory analysis with a statistical analysis. METHOD: High resource use outliers were selected according to the following rule: 75th percentile + 1.5* inter-quartile range. Low resource use outliers were selected according to: 25th percentile - 1.5* inter-quartile range. The statistical approach was based on a multivariate analysis using logistic regression. A decision tree approach using predictors from this analysis (intensive care unit (ICU) stay, high severity of illness and social factors associated with longer length of stay) was also tested as a more intuitive tool for use by hospitals in focussing review efforts on "not explained" cost outliers. RESULTS: High resource use outliers accounted for 6.31% of the hospital stays versus 1.07% for low resource use outliers. The probability of a patient being a high resource use outlier was higher with an increase in the length of stay (odds ratios (OR) = 1.08), when the patient was treated in an intensive care unit (OR = 3.02), with a major or extreme severity of illness (OR=1.46), and with the presence of social factors (OR = 1.44). The probability of being a low outlier is lower for older patients (OR = 0.98). The probability of being a low outlier is also lower without readmission within the year (OR = 0.55). The more intuitive decision tree method identified 92.26% of the cases identified through residuals of the regression model. One quarter of the high cost outliers were flagged for additional review ("not justified" on the basis of the model), with nearly three-quarters "justified" by clinical and social factors. CONCLUSION: The analysis of cost outliers can meet different aims (financing of justifiable outliers, improvement of the care process for the outliers not justifiable on medical or social grounds). The two methods are complementary, by proposing a statistical and a didactic approach to achieve the goal of high quality care using fewer resources.

Adolescent↗
PubMed2006

Measurement of adverse events using "incidence flagged" diagnosis codes.

OBJECTIVE: To compare two methods for identifying adverse events using routinely recorded hospital abstract data in all public and private hospitals in Victoria, Australia. METHODS: Secondary analysis of data on all admissions in the period 1 July 2000-30 June 2001 (n = 1,645,992) to estimate the rates of adverse events using International Classification of Diseases 10th Revision Australian Modification codes alone and in combination with an "incidence" data flag indicating complicating diagnoses which arise after hospitalization; rates of incidence and pre-existing adverse events, and rates for same-day and multi-day admissions. RESULTS: In total, 8% of all admissions were recorded with an adverse event. Use of ICD codes alone identified only 59% of the events identified using the combined method, giving a prevalence rate of only 5%. Incident cases, that is, those occurring in the index admission, represented 68% of identified adverse events. The adverse events incidence rate for multi-day admissions was significantly higher at 12%, compared with the same day rate of 0.4%. CONCLUSION: An "incidence flag" is essential to identify those adverse events for which a hospital has unambiguous responsibility. Using such a flag, secondary analysis of administrative data can provide hospital quality assurance programmes with a comprehensive view of all adverse events (not just "sentinel" events) at a reasonable cost and with more timely results than more intensive methods can achieve. Although the method is likely to underestimate the true rate of adverse events (in particular, by not capturing adverse events which only manifest after discharge), in this study of Australian hospitals, rates of adverse events were found to be similar to those derived from studies using manual review of patient records.

Hospitals, Private↗
PubMed2005

Why performance indicators have a place in health policy.

Despite the mixed results of Brown and colleagues' review of the evidence for the use of performance indicators in health policy, this paper argues that they have an important place. Healthcare organizations cannot rely on altruism alone to motivate improved performance. Berwick, supporting the use of performance indicators in healthcare, argues "threats to survival are necessary to build will for improvement." He argues that the job of managers is to create organizations where such threats are clearly perceived, but balanced against a culture of "safety" in which individuals can learn and improve the care provided. This step in the causal chain gets insufficient attention from the "KAB+" evaluation model that Brown and colleagues employ. The use of performance indicators in publicly funded healthcare systems goes beyond arming the "consumer" of healthcare with relevant information; it is a fundamental question of democratic accountability. Methods for evaluating the evidence base of public policy must take into account the contextual (economic, social and political) factors that support or impede the achievement of policy objectives.

Health Policy↗