I. Haslock

Bio: I. Haslock is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 869 citations.

What's measured is what matters: targets and gaming in the English public health care system

Abstract: In the 2000s, governments in the UK, particularly in England, developed a system of governance of public services that combined targets with an element of terror This has obvious parallels with the Soviet regime, which was initially successful but then collapsed Assumptions underlying governance by targets represent synecdoche (taking a part to stand for a whole); and that problems of measurement and gaming do not matter We examine the robustness of the regime of targets and terror to these assumptions using evidence from the English public health service on reported successes, problems of measurement, and gaming Given this account, we consider the adequacy of current audit arrangements and ways of developing governance by targets in order to counter the problems we have identified

Using conjoint analysis to elicit preferences for health care.

Abstract: Traditionally the extent of patients' involvement in medical decision making has been minimal. This has been true at both the micro level of the patient consultation with a doctor and the macro level of planning and developing healthcare services. Since 1989, however, greater involvement of patients and the community in these processes has been advocated.1–5 In principle, the elicitation of patients' and the community's values represents a big step forward in terms of enhancing the benefits from the provision of health care. For the exercise to be worth while, however, the information obtained must be useful and scientifically defensible.6 During the 1990s, conjoint analysis was developed to elicit patients' and the community's views on health care. #### Summary points Conjoint analysis is a rigorous method of eliciting preferences It allows estimation of the relative importance of different aspects of care, the trade-offs between these aspects, and the total satisfaction or utility that respondents derive from healthcare services The technique can help with decision making for some of the issues facing the NHS Though further applications of conjoint analysis are encouraged, methodological issues need further consideration This paper explains conjoint analysis, provides examples of applications in health care which were obtained from a systematic review of databases between 1989 and 1999 (Medline, Embase, HealthSTAR, PsychLIT, EconLIT), and uses a study in orthodontic care to show the uses and pitfalls of the technique. The survey method of data collection and analysis known as conjoint analysis was developed in mathematical psychology and has a strong theoretical basis.7–9 It has been successfully used in market research,10 transport economics,11 and environmental economics 12 13 and was recommended to the UK Treasury for valuing quality in the provision of public services.14 Within these areas it has been well received …

Organisational culture and quality of health care

Abstract: “ A student of management and organisation theory could only be stunned by how little the efforts to improve quality [in health care] have learnt from current thinking in management and from the experience of other industries .” Christian Koeck BMJ 1998; 317: 1267–8. Health policy in much of the developed world is concerned with assessing and improving the quality of health care. The USA, in particular, has identified specific concerns over quality issues12 and a recent report from the Institute of Medicine pointed to the considerable toll of medical errors.3 In the UK a series of scandals has propelled quality issues to centre stage45 and made quality improvement a key policy area.6 But how are quality improvements to be wrought in such a complex system as health care? A recent issue of Quality in Health Care was devoted to considerations of organisational change in health care, calling it “the key to quality improvement”.7 In discussing how such change can be managed, the authors of one of the articles asserted that cultural change needs to be wrought alongside structural reorganisation and systems reform to bring about “a culture in which excellence can flourish”.8 A review of policy changes in the UK over the past two decades shows that these appeals for cultural change are not new but have appeared in various guises (box 1). However, talk of “culture” and “culture change” beg some difficult questions about the nature of the underlying substrate to which change programmes are applied. What is “organisational culture” anyway? It is to this issue that this paper is addressed.Many previous policy reforms in the National Health Service (NHS) have invoked the notion of cultural change. In the early 1980s the reforms inspired by Sir Roy Griffiths led …

Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies.

Abstract: Objectives: In an interrupted time series (ITS) design, data are collected at multiple instances over time before and after an intervention to detect whether the intervention has an effect significantly greater than the underlying secular trend. We critically reviewed the methodological quality of ITS designs using studies included in two systematic reviews (a review of mass media interventions and a review of guideline dissemination and implementation strategies). Methods: Quality criteria were developed, and data were abstracted from each study. If the primary study analyzed the ITS design inappropriately, we reanalyzed the results by using time series regression. Results: Twenty mass media studies and thirty-eight guideline studies were included. A total of 66% of ITS studies did not rule out the threat that another event could have occurred at the point of intervention. Thirty-three studies were reanalyzed, of which eight had significant preintervention trends. All of the studies were considered “effective” in the original report, but approximately half of the reanalyzed studies showed no statistically significant differences. Conclusions: We demonstrated that ITS designs are often analyzed inappropriately, underpowered, and poorly reported in implementation research. We have illustrated a framework for appraising ITS designs, and more widespread adoption of this framework would strengthen reviews that use ITS designs.

Ethnicity as a variable in epidemiological research.

Abstract: Ethnicity is used increasingly as a key variable to describe health data, and ethnic monitoring in the NHS will further stimulate this trend. We identify four fundamental problems with ethnicity in this type of research: the difficulties of measurement, the heterogeneity of the populations being studied, lack of clarity about the research purpose of the research, and ethnocentricity affecting the interpretation and use of data. Ethnicity needs to be used carefully to be a useful tool for health research. We make nine recommendations for future practice, one of which is that ethnicity and race should be recognised and treated as distinct concepts. Epidemiology is the study of the distribution and determinants of disease. The main method of study, particularly for investigating the causes of disease, is to compare populations with different risks of disease. Ethnicity is a variable that is used increasingly to define populations for epidemiological studies. Differences by ethnicity in both the characteristics of populations and their experience of disease have been easy to describe, and the literature on ethnicity and health is large and growing.1 We consider here the nature of ethnicity, the attributes of sound epidemiological variables, the measurement and value of ethnicity as an epidemiological variable, and how ethnicity might best be used in future research. By reviewing critically ethnicity as a variable in epidemiology we hope to facilitate better research. This review is relevant to ethnic monitoring in the NHS. Ethnicity is derived from a Greek word meaning a people or tribe. The concept of ethnicity is neither simple nor precise,*RF 2-6* but it implies one or more of the following: shared origins or social background; shared culture and traditions that are distinctive, maintained between generations, and lead to a sense of identity and group; and a common language or religious tradition.*RF 3-6* …