Biomedical subjects

Nigel Harvey

Publications and source records attributed to Nigel Harvey.

3 recordsLinked to original sources

PubMed2006

Variations in cardiac interventions: doctors' practices and views.

OBJECTIVES: To investigate referral rates for cardiac interventions by clinical specialty, to document doctors' reasons for referrals and to explore doctors' perceptions of the factors that influenced their clinical decisions. STUDY DESIGN: Doctors completed a clinical decision-making exercise involving, in total, 6093 electronic patients with cardiac disease, and subsequently took part in the semi-structured interviews about influences on their decisions. Interviews were audio-recorded, transcribed and coded using a thematic approach, with the coding categories derived from the data. STUDY SETTING: Eighty-eight doctors (GPs, care-of-the-elderly specialists, cardiologists) participated in the full study, in seven areas in southern, central and northern England. Complete interview data were analysed for 76 of these. PRINCIPAL FINDINGS: Not all patients who were eligible for specific investigations or treatment received these. The extent of variations in clinical decisions differed by type of intervention. Apart from the general reasons for referrals, doctors raised nine main influences on their actual decision making. The most commonly reported influence ('barrier') was poor access to equipment for intervention, which increased thresholds for investigation and treatment. CONCLUSIONS: The current emphasis on achieving targets in the British NHS has led to a focus on easily measurable, but crude, process targets such as waiting lists. This study points to the need to include a broader quality assurance element to investigate the cluster of system failures which lead to variations in clinical decisions and thereby to inequitable treatment.

Cardiology↗

PubMed2006

Predicting preferences: a neglected aspect of shared decision-making.

In recent years, shared decision-making between patients and doctors regarding choice of treatment has become an issue of priority. Although patients' preferences lie at the core of the literature on shared decision-making, there has not been any attempt so far to link the concept of shared decision-making with the extensive behavioural literature on people's self-predictions of their future preferences. The aim of the present review is to provide this link. First, we summarize behavioural research that suggests that people mispredict their future preferences and feelings. Secondly, we provide the main psychological accounts for people's mispredictions. Thirdly, we suggest three main empirical questions for inclusion in a programme aimed at enriching our understanding of shared decision-making and improving the procedures used for putting it into practice.

Decision Making↗

PubMedDate not supplied

Assessing the likelihood of an important clinical outcome: new insights from a comparison of clinical and actuarial judgment.

PURPOSE: To assess and rank the performance of different methods of predicting the probability of death following a specified surgical procedure. METHOD: Actuarial estimates of the probability of early mortality for 40 patients were derived from 2 sources: a large published surgical series and a smaller series from the center where surgery was performed. Surgeons and cardiologists also provided probability estimates for these patients. RESULTS: Estimates derived from the published literature were too optimistic and did not differentiate between patients more, or less, likely to die (i.e., failed to discriminate). Doctors' judgments were unbiased but failed to discriminate. Local actuarial estimates (influenced by only 1 or 2 variables) were unbiased, did discriminate, but exhibited more random variation. CONCLUSIONS: The preferred source of estimates depends upon which aspect of accuracy is of greatest importance. Differences in patient selection and error in the identification of risk factors mean that published results will not always appropriately predict surgical risk at other institutions. Risk stratification may be more robust when based on a small set of cross-validated predictors rather than a larger set of predictors that includes some whose reliability has not been confirmed.

Actuarial Analysis↗