Biomedical subjects

Robert Gibberd

Publications and source records attributed to Robert Gibberd.

6 recordsLinked to original sources

PubMed2006

A prospective, multi-method, multi-disciplinary, multi-level, collaborative, social-organisational design for researching health sector accreditation [LP0560737].

BACKGROUND: Accreditation has become ubiquitous across the international health care landscape. Award of full accreditation status in health care is viewed, as it is in other sectors, as a valid indicator of high quality organisational performance. However, few studies have empirically demonstrated this assertion. The value of accreditation, therefore, remains uncertain, and this persists as a central legitimacy problem for accreditation providers, policymakers and researchers. The question arises as to how best to research the validity, impact and value of accreditation processes in health care. Most health care organisations participate in some sort of accreditation process and thus it is not possible to study its merits using a randomised controlled strategy. Further, tools and processes for accreditation and organisational performance are multifaceted. METHODS/DESIGN: To understand the relationship between them a multi-method research approach is required which incorporates both quantitative and qualitative data. The generic nature of accreditation standard development and inspection within different sectors enhances the extent to which the findings of in-depth study of accreditation process in one industry can be generalised to other industries. This paper presents a research design which comprises a prospective, multi-method, multi-level, multi-disciplinary approach to assess the validity, impact and value of accreditation. DISCUSSION: The accreditation program which assesses over 1,000 health services in Australia is used as an exemplar for testing this design. The paper proposes this design as a framework suitable for application to future international research into accreditation. Our aim is to stimulate debate on the role of accreditation and how to research it.

Accreditation↗

PubMed2005

Surgeon and hospital volume and the management of colorectal cancer patients in Australia.

BACKGROUND: The evidence for a relationship between patient outcomes and clinician and hospital volume is increasing. The National Colorectal Cancer Care Survey was undertaken to determine the management patterns in Australia for individuals newly diagnosed with colorectal cancer in a 3 month period in the year 2000. METHODS: All new cases of colorectal cancer registered at each Australian State Cancer Registry were entered into the survey. This generated a questionnaire that was sent to the treating surgeon. Chi-squared tests and logistic regression analyses were used to determine levels of statistical significance. RESULTS: Of 2,383 surgical questionnaires generated, 2,015 (85%) were completed. The majority (58%) of surgeons treated one or two patients with colorectal cancer over the 3 months of the survey. There was variation across surgeon cohorts for preoperative measures including the use of deep vein thrombosis prophylaxis. Patients seen by low volume surgeons were most likely to be given a permanent stoma (P < 0.0001). Patients with rectal cancer who were operated on by high volume surgeons were significantly more likely to receive a colonic pouch (P < 0.0001). CONCLUSION: This nationwide population-based survey of the treatment of colorectal cancer patients suggests that the delivery of care by surgeons (the majority) who treat patients with rectal cancer infrequently should be evaluated.

Adolescent↗

PubMed2004

The effective use of a summary table and decision tree methodology to analyze very large healthcare datasets.

Very large datasets typically consists of millions of records, with many variables. Such datasets are stored and maintained by organizations because of the perceived potential information they contain. However, the problem with very large datasets is that traditional methods of data mining are not capable of retrieving this information because the software may be overwhelmed by the memory or computing requirements. In this article we outline a method that can analyze very large datasets. The method initially performs a data reduction step through the use of a summary table, which is then used as a reference dataset that is recursively partitioned to grow a decision tree.

Data Interpretation, Statistical↗

PubMed2004

Complications after discharge for surgical patients.

AIM: To measure the type and frequency of complications for surgical patients 1 month after discharge. METHODS: A post-discharge patient survey was conducted in 2000 for patients who had undergone one of five elective operations: transurethral resection of the prostate, hysterectomy, major joint replacement, cholecystectomy, herniorrhaphy. Two hundred and fourteen patients (74%) returned the survey forms, which were sent 1 month after surgery. Patients were recruited from two teaching hospitals in the Hunter Area Health Service, New South Wales, Australia. RESULTS: One hundred and thirty-five (63%) patients reported one or more complications and 78 (37%) received treatment for 109 complications. Eighty-six per cent reported pain after discharge and 41% reported moderate to severe pain. Seventeen per cent reported infections after discharge and 94% of these patients were given treatment. Twenty-eight per cent reported bleeding after discharge and 20% of these were given treatment. Eleven (5%) patients were readmitted for treatment of problems related to their surgery including four who required further surgery. One hundred and seventy-two patients accessed a range of health services during the first month after discharge, resulting in 266 occasions of service. Twenty-eight per cent of post-discharge services were unplanned. CONCLUSIONS: The lack of post-discharge monitoring conceals information about surgical outcomes. Patient reporting is an effective method of monitoring post-discharge outcomes. There is scope to develop post-discharge services to improve the quality of care in the areas of post-discharge pain management, the use of prophylactic measures and to provide treatment for complications that occur during this period.

Arthroplasty, Replacement↗

PubMed2004

Using indicators to quantify the potential to improve the quality of health care.

PURPOSE: Although clinical indicators allow individual providers to monitor and improve their own performance and quality of care, another important role for the indicators is to provide comparative information across all providers. We show that the 'league table' approach is ineffective, and provide an alternative method that uses the comparative rates to quantify the potential for improvement at both the provider and the national level. DATA SOURCES: The methods are applied to English and Australian hospital clinical indicators. METHODS: The key is to regard clinical indicators as screening tools that measure performance in one or more dimensions. All screening processes require explicit tests to determine whether the result should be classified as either positive (requires further investigation) or negative (requires continued monitoring). A clinical indicator will be defined as positive if any of the three following criteria are met: (1) large variation between all areas or hospitals, as defined by the 20th centile gains: requires improvement in the health care system; (2) large variation between strata (rural/urban, teaching/non-teaching, public/private, State): requires action in the relevant stratum; (3) outlier hospitals: requires quality improvement in the individual hospitals. Two techniques are used to determine whether any of the three criteria are positive: (1) empirical Bayesian estimation to calculate 'shrunken' rates; and (2) use of the 20th centile to quantify the potential gains or improvement. RESULTS: For 185 Australian indicators, 55 clinical indicators had system gains involving better outcomes for at least 1000 patients per indicator. Using a set of criteria and subjective judgement, we identified some key areas for quality improvement in Australia. CONCLUSION: Ranking of hospitals does not quantify the potential gains that could be achieved. Indicators that measure health care processes should be reported by quantifying the potential gains, thus encouraging action. Estimating the gains across many indicators allows priorities to be established, such as identifying the areas with the greatest potential for improvement. The main tasks are to then provide the tools and resources to tackle those areas with the most gains.

Australia↗

PubMed2003

Using hierarchical models to analyse clinical indicators: a comparison of the gamma-Poisson and beta-binomial models.

BACKGROUND: Clinical indicators (CIs) are used to assess, compare and determine the potential to improve the care provided by hospitals and physicians. The results for Australian hospitals in 1998-2000 have been reported using a new methodology. The gamma-Poisson hierarchical model was used to correct for the effects of sampling variation by obtaining the empirical Bayesian shrunken estimates for the CI proportions for each hospital. Then, an estimate of the potential system gains that could be achieved if the mean proportion was shifted to the 20th centile is obtained for each of the 185 CIs. The results are sed to prioritize quality improvement activity. OBJECTIVES: To describe the 20th centile method of calculating potential system gains in the health care system; to determine the impact of using the beta-binomial model rather than the gamma-Poisson model to obtain shrunken estimates for the CI proportions; and to compare the computationally simpler Method of Moments (MoM) with the maximum likelihood (ML) method for parameter estimation. METHODS: The formulae for the gamma-Poisson and beta-binomial shrinkage estimators were compared analytically. Each of the shrinkage estimators and the two methods of parameter estimation were applied to the Obstetric and Gynecological CIs, and the results compared. RESULTS The comparison of the formulae for the two shrinkage estimators showed that the gamma-Poisson model results in: greater shrinkage towards the overall mean. This was verified empirically using the clinical indicators. Additionally, the MoM was not a viable alternative to the ML method. CONCLUSIONS: The gamma-Poisson model provided smaller estimates of the potential system gains by up to 6.7% of the numerator for the clinical indicators. The difference in estimation increased with increasing mean proportions and between-hospital variation. We recommend that the beta-binomial model should be used on the basis of both theoretical and empirical grounds.

Australia↗