The Tide is Turning

In the 50’s the youth of the town used to tease visitors by jumping off the bridge in the incoming high tide and ending up back under the bridge. I know because I used to do it. I hope I’m not teasing now, for the signs are all there that a new dawn is here.

Chris Vasilakis drew to our attention to The John Eisenberg lecture: health services research as a citizen in improvement by Don Berwick (1). This heralds a new dawn and sets the scene for new styles of health service research. It’s a breath of fresh air.

Using an analogy "Like Russian dolls" Don Berwick sees four interdependent systems. The outer doll is government, the facilitator of facilitators. At Regional and District level are the macrosystems where managers facilitate the aims. Locally, are the microsystems where the care process is undertaken. And right in the centre, the innermost doll, is the patient - the citizen - who experiences the care that is given. Government should set the aim, not micro manage the system. For aims create systems, not systems the aims. ‘Unless we can decide exactly what we want to accomplish in the way of reducing human suffering we cannot specify the relevant system to work or whom to involve.’

Without contextual knowledge, scientific knowledge remains sterile. Efficacy (performance under laboratory conditions) and effectiveness (performance in the field) are not the same. Scientifically reengineering health care will not be accomplished by trials and guidelines or by a graduate student or a smart doctor figuring out solutions on a back of an envelope.

Rather, our most accomplished health services researchers, co-operating seriously with quantitative scientists from engineering and related disciplines, must be engaged for the long haul in real world settings to test and prove what they discover. The task is difficult and 'creates for health service research a new level of obligation in its ambition and in its complexity, so that it can take its proper role of citizenship in the very system that it seeks to comprehend.'

1. Berwick, D. M. (2005). Health Serve Rev 40 (2): 317-36.

Why does restructuring fail? Research indicates why

A major aim is cost efficiency, but recently published research throws doubt upon the wisdom of constant managerial change. Studying the cost efficiency of change in 20 teaching hospitals Jeffery Braithwaite’s group at the Clinical Governance Research at the University of New South Wales, Sydney, Australia conclude that the twelve teaching hospitals who changed structure and the eight who did not were similar in cost efficiency at the beginning and at the end of the study period (Braithwaite, Westbrook et al. 2006).

In further research the group report the conflict inherent in structural change. Three years after Clinical Directorates were introduced at two large Australian Hospitals 227 professionals were interviewed. No one should be surprised that the doctors were both negative and strongly opinionated. In contrast allied staff were more positive but less certain, and nurses’ attitudes were polarized and intense but more positive than the doctors (Braithwaite and Westbrook 2005). For change is difficult and change takes time.

1. Redfern, S., S. Christian, and I. Norman, Evaluating change in health care practice: lessons from three studies. Journal of evaluation in clinical practice, 2003. 9(2): p. 239-49.
2. Braithwaite, J., J. Westbrook, and R. Iedema, Restructuring as gratification. J R Soc Med, 2005. 98(12): p. 542-4.
3. Braithwaite, J., et al., Does restructuring hospitals result in greater efficiency?--An empirical test using diachronic data. Health Serv Manage Res, 2006. 19(1): p. 1-12.
4.Braithwaite, J. and M. Westbrook, Rethinking clinical organisational structures: an attitude survey of doctors, nurses and allied health staff in clinical directorates. J Health Serv Res Policy, 2005. 10(1): p. 10-7.

Could a DSS do this? Analysis of coping with overcrowding in a hospital emergency department; Red Ceglowski, Department of Accounting and Finance, Faculty of Business and Economics, Monash University.

Editor's comment: Red reveals how nurse managers in Accident and Emergency departments juggle the patients to meet their targets. Now you see them, now you don't. Like small children playing hide and seek, they can't see you, so you can't see them.

MASH News: second issue

More on Queue Theory: John Preater in Keele has come up with some useful comments on Roy Johnston's suggestions in the January issue based on historic experience; these follow at the end.

Recent Papers

Lessons learnt by The Institute for Healthcare Improvement work with 60+ hospitals in the United States and the United Kingdom are discussed. The ED only looks like the problem. The key point is the inability to transfer patient to hospital after decision to admit from ED is made. Initiatives recommended include, separating emergent and planned surgery. Providing a process team for discharge. Synchronising the discharge schedule; Specialised units outside, e.g. for chronic ventilation. Making the nursing home reservation. And extending the chain forwards and backwards by using midlevel providers providers. The challenge to measure flow.

Glickman, T. S. and H. Khamooshi (2005): Using hazard networks to determine risk reduction strategies, Journal of the Operational Research Society 56 (11): 1266-1272.

Hazard networks express the interdependencies between risk factors. Nodes correspond to the hazards and links show how they are interrelated. Reading this theoretical paper, I thought of the links between hospital cleanliness and cross infection and between inappropriate nursing and medical care causing bed-blocking, leading to cancelled operations, and trolley waits. Perhaps hazard networks demonstrate these links.

Fusco, D., C. Saitto, et al. (2006): Influenza outbreaks and hospital bed occupancy in Rome (Italy): current management does not accommodate for seasonal variations in demand, Health Serv Manage Res 19 (1): 36-43.

Time series analysis of daily bed occupancy was used to model bed usage by influenza patients. In winter months bed usage in general medicine increased for influenza patients by 51% versus 25-32% in other specialties: overall 2.8% of total hospital beds and 7% of general hospital beds were used. The authors conclude that any possible winter bed crisis is probably due to ineffective management of available beds rather than the results of an unmanageable excess in demand. Though without knowing the actual pattern of bed usage, I cannot see how they found this. Then I'm a biased medic.

Oliveira, M. D. and G. Bevan (2006): Modelling the redistribution of hospital supply to achieve equity taking account of patient's behaviour, Health Care Management Science 9(1) (1): 19-30. link

Previous methods for analysing the impact of hospital changes have relied on crude assumptions on patients' behaviour in using hospitals. The approach developed in this study is a multi-modelling one based on two mathematical programming location-allocation models to redistribute hospital supply using different objective functions and assumptions about the utilisation behaviour of patients. These models show how different policy objectives seeking equity of geographic access or utilisation produce different results and imply trade-offs in terms of reduction in total utilisation.

Sprivulis, P. C., Da Silva, J.-A., Jacobs, I. G., Frazer, A. R. L. & Jelinek, G. A. (2006): The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments, Medical Journal of Australia 184, 208-212 link

Mathematically, correlation is not causation, but the findings of this study are intuitively not incorrect. The greater the overcrowding, the greater the deaths. Retrospective data analysis of 3084 inpatient deaths (4.9% of 62,495 admissions in three years) in Perth Hospitals shows a statistically significant correlation between overcrowding and death. Discounting winter the mathematical relationship is still there. Seemingly, men are more at risk, though overcrowding is usually associated with more female admissions. The problem is, the factors associated with increased need are also factors associated with death. Namely, increased age, ambulance arrival, injury, triage score, emergency, overcrowding, physician referral. Editor’s comment: To tease out the probabilities of individual risk, a Bayesian analysis would be helpful.

Some navigational notes:

Copyright (c)Roy Johnston, Ray Millard, 2005, for e-version; content is author's copyright,