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RESEARCH: John Billings, Jennifer Dixon, Tod Mijanovich, and David Wennberg Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients BMJ 2006; 333: 327 [Abstract] [Full text]

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Readmissions to hospital

Iris González-Morales, María C. Fragoso-Marchante, Alfredo A. Espinosa-Roca, Yenisei Quintero-Méndez , Alfredo D. Espinosa-Brito   (13 August 2006)

The PARR tool is a huge step forward.

Kelechi E Nnoaham   (16 August 2006)

Reducing hospital readmission rates

Stuart G Parker   (19 August 2006)

Implications of prediction

Michael Tremblay   (30 August 2006)

Readmissions to hospital 13 August 2006
Iris González-Morales, Chief of Internal Medicine Department Hospital Dr. Gustavo Aldereguía Lima, Ave 5 de Septiembre and Calle 51 A, Cienfuegos 55 100, Cuba, María C. Fragoso-Marchante, Alfredo A. Espinosa-Roca, Yenisei Quintero-Méndez , Alfredo D. Espinosa-Brito Send response to journal: Re: Readmissions to hospital	If health improving management of patients for whom care is costly is an important strategy, for better health outcomes and controlling health care expenditures, in developed countries, (1) this aim is crucial for National Health Systems –with universal and equity services for all-in poor countries, with scarce resources, as Cuba is. Our group reviewed 1 442 medical records of patients who were admitted to Internal Medicina and Geriatrics Departments at the Hospital Dr. Gustavo Aldereguía Lima, Cienfuegos, Cuba, since January to April 2001. (2) In this period, 43 patients fulfilled the criteria of readmissions (for this study we selected cases with a previous discharge from our hospital, four months before or less). Readmissions represented 3% of all patients. Analysing the main characteristics of this group, we found that the mean age was high (63.3 years), and the great majority of them were suffering underlying Chronic Non Communicable Diseases: respiratory diseases (39.3%), heart failure (34.8%) and neoplasms (11.6%). The first cause of hospitalization was an acute exacerbation of their chronic conditions (67.4%). Slighty higher rates of alcoholics (9.2%) and smokers (44.2%) were reported in this group, when we compared them with those without readmissions. Only a small proportion (16.2%) referred difficulties for primary care follow up before their admissions. Other common outcome indicators of institutional efficiency –as mean hospital stay and mortality rates- did not differ of the rest of the patients. Therefore, we welcome the research of Billings, Dixon, Mijanovich, Wennberg as a good intent to develop a method of identifying patients at high risk of readmissions to hospital, not only to control health care expenditures but mainly to achieve better health care for them. (3) Iris González-Morale MD, María C. Fragoso-Marchante MD, Alfredo A. Espinosa-Roca MD, Yenisei Quintero-Méndez MD, Alfredo D. Espinosa Brito MD, PhD Internal Medicine Department Hospital Dr. Gustavo Aldereguía Lima Cienfuegos, Cuba References 1. Department of Health. Supporting people with long term conditions: an NHS and social care model to support local innovation and integration. London: Department of Health, 2005 (available at: www.dh.gov.uk/assetRoot/04/09/98/68/04099868.pdf). 2. Espinosa-Roca AA, Espinosa-Brito AD, Quintero-Méndez Y. Pacientes reingresados en el hospital. Servicios de Medicina Interna y Geriatría. Enero-abril 2001. Cienfuegos: Hospital Dr. Gustavo Aldereguía Lima, 2001. 7 p. 3. Billings J, Dixon J, Mijanovich T, Wennberg D. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ 2006;333:327-30. Competing interests: None declared
The PARR tool is a huge step forward. 16 August 2006
Kelechi E Nnoaham, Specialist Registrar in Public Health University of Oxford, Old Road, Headington, Oxford OX3 7LF Send response to journal: Re: The PARR tool is a huge step forward.	Editor, The PARR algorithm as described by Billings et al is a welcome tool in the bid of the Health Service to cut down on unplanned secondary care expenditures. Such tools have been developed in various settings before now but most of these have focused on one condition or have used far fewer variables to predict risk of rehospitalisation (1). The authors however do not make explicit the degree of overlap between the two 10% samples used to develop and test the algorithm. If any reasonable degree of overlap existed, it could turn to be a case of ‘garbage in, garbage out’. Conclusions on the validity of the algorithm would more convincing if it was tested on a population not included in its development. It has to be said however that the use of a 10% sample, rather than higher, would minimise the likelihood of a significant overlap. In describing the limitations of their approach, the authors call attention to the inherent quality deficiencies in the Hospital Episode Statistics (HES) data, stating that there was however a tendency to under- prediction, rather than over-prediction. One wonders how true this is considering the tendency of HES data to include Finished Consultant Episodes (FCEs) rather than actual admissions. Could they have been more explicit about how this possibility was dealt with, especially as some of the conditions they focused on could attract more than one FCEs per admission (e.g. alcohol related diagnoses could attract traumatologist, hepatologist and psychiatrist attention)? Overall, the effort was well thought out and the PARR tool promises to mark a key turning point in the way intensive case management is thought about, especially when the next phase of the development incorporating a wider set of variables is completed. Reference 1. Stukenborg G, Wagner DP, Dembling BP, Connors AF. A Method for Assessing the Risk of Influenza Attributable Rehospitalization. Available at http://www.academyhealth.org/arm/ViewAbstract.cfm?uid=GWHSR0001853. Accessed 15th August 2006. Competing interests: None declared
Reducing hospital readmission rates 19 August 2006
Stuart G Parker, Professor of Health Care for Older People Sheffield Institute for Studies on Ageing, University of Sheffield, UK Send response to journal: Re: Reducing hospital readmission rates	Sir, Billings et al are to be congratulated in producing an algorithm which is predictive of hospital readmission and can identify at risk groups for targeted intervention. The authors suggest a conservative and incremental approach to developing appropriate interventions based on further research with identified 'high risk' individuals. I agree that such research is necessary, but would suggest that we already know much that can help and inform service providers and purchasers from existing evidence. In a systematic review of discharge arrangements for older people commissioned by the NHS R&D Health Technology Assessment programme (1), readmission to acute hospital care was cited as a key undesirable outcome. Exploration of potential causes for heterogeneity for this outcome identified 'working across the health and social care interface' as an important factor in reducing readmission rates after discharge from inpatient hospital care. In a recent partial update (2), trials that specifically considered discharge arrangements which were provided in both the acute and community setting and reported readmission outcomes were selected for review. The general models of care identified in the review included comprehensive geriatric assessment, discharge planning, discharge support and education (but not case management). Conclusions were that discharge arrangements across the hospital–community interface based on these general models of care are safe (not associated with increased mortality or other adverse outcomes) and can reduce hospital readmission rates by about 20%. This is a worthwhile gain, particularly for older people at risk of repeated hospital admission which could be achieved through the adoption of a range of well known, clearly described and evaluated discharge practices. The concept of 'working across the hospital community interface' implies cooperation between health and social care organisations and professionals providing services in acute and community settings. An implication is that policies or local practices which create barriers to co-operation between health and social care, or acute and community services are unlikely to result in improvements in hospital readmission rates. Therefore I suggest a simple approach to improving readmission rates, which does not depend on the generation of new research evidence would be this: providers and purchasers should critically examine and increase the extent to which local structures and processes around hospital discharge encourage effective cooperation between acute and community health and social care organisations and professionals. References 1. Parker SG, Peet SM, McPherson AM, Cannaby AM, Abrams K, Baker R, Wilson A, Lindesay J, Parker G, Jones. DR A systematic review of discharge arrangements for older people. Health Technology Assessment 2002;6(4). 2. Parker S G. Do current discharge arrangements from inpatient hospital care for the elderly reduce readmission rates, the length of inpatient stay or mortality, or improve health status? WHO Health Evidence Network report 2005. WHO regional office for Europe, Copenhagen. Competing interests: None declared
Implications of prediction 30 August 2006
Michael Tremblay, health policy consultant Canada/UK Send response to journal: Re: Implications of prediction	Having conducted policy research for the Department of Health on predictive modelling, I remain concerned about the unexamined implications of their use. Yes, the algorithm does help sort through health databases to identify high-risk individuals. But what actions can properly be taken with that information by the NHS? Have patients consented to be profiled; indeed what would they make of using ethnicity as a criterion? Should we expect people identified to be at high risk and high utilisation to be called in by their GP for a 'chat' about their potential future use of the health care system? There is a relatively small administrative step between determining what resources are appropriate for what level of risk as determined by these sorts of algorithms and deciding what to do when a patient runs over their apparent allocation, especially since it is a founding value of the NHS precisely not to do this. Perhaps, all patients will want to be profiled in order to ascertain their fair share of resources for their own healthcare. And then what happens to the relationship between the NHS and the people? The authors speak of designing appropriate interventions. Will patients feel pressured or coerced into participating? And of course once designed, these interventions had better work as failure will point once again to the features of the patient and not the system. As I reported in my work (which for its own reasons the BMJ chose not to publish...) there are considerable benefits and risks from using predictive algorithms in the NHS. The social context of their use and misuse need examination particularly in respect of data protection and confidentiality. My concerns here are exemplified by the capabilities reported in this paper. Competing interests: The author conducted policy research for the Department of Health on predictive modelling.