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Hospitalisation Risk
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Improving care Hospital Admission Risk Program
Public report
Victoria Health 2006
http://www.health.vic.gov.au/harp/downloads/improvingcare.pdf
Hospital Admission Risk Program Monitoring and Evaluation Framework
Victoria Health 2011
http://docs.health.vic.gov.au/docs/doc/885B6C2873DDFF6BCA257A290009D769/$FILE/HARP%20M&E%20framework071111.pdf
Hospital Admission Risk Program Monitoring Measures
Victoria Health 2012
http://docs.health.vic.gov.au/docs/doc/BB89C5EC08A8B137CA257A2900056FE4/$FILE/HARP%20Monitoring%20Measures%20Jan12.doc
Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions scoreJulia Hippisley-Cox Carol Coupland
BMJ Open 2013;3:e003482 doi:10.1136/bmjopen-2013-003482
http://bmjopen.bmj.com/content/3/8/e003482.full.pdf+html
Proactive care programme: CCG support for implementation
First published: June 2014
Prepared by NHS England
HouseThis document is supporting guidnace for CCGs for the Avoiding Unplanned Admissions enhanced service (ES) which is designed to help reduce avoidable unplanned admissions by improving services for vulnerable patients and those with complex physical or mental health needs, who are at high risk of hosptial admission or re-admission.which is designed to help reduce avoidable unplanned admissions by improving services for vulnerable patients and those with complex physical or mental health needs, who are at high risk of hosptial admission or re-admission.
http://www.england.nhs.uk/wp-content/uploads/2014/06/avoid-unpln-admss-ccg-guid.pdf
Implementation Guide to Reduce Avoidable Readmissions
US Dept of Health and Human Services
http://www.dcha.org/wp-content/uploads/readmission_changepackage_508.pdf
The Readmission Reduction Program of Kaiser Permanente Southern California—Knowledge Transfer and Performance Improvement
Philip Tuso, MD, FACP; Dan Ngoc Huynh, MD, FACP; Lynn Garofalo, DPPD, MHA; Gail Lindsay, RN, MA; Heather L Watson, MBA, CHM; Douglas L Lenaburg, MSN, RN; Helen Lau, RN, MHROD, BSN, BMus; Brandy Florence, MHA; Jason Jones, PhD; Patti Harvey, RN, MPH, CPHQ; Michael H Kanter, MD
Perm J 2013 Summer;17(3):58-63
In 2011, Kaiser Permanente Northwest Region (KPNW) won the Lawrence Patient Safety Award for its innovative work in reducing hospital readmission rates. In 2012, Kaiser Permanente Southern California (KPSC) won the Transfer Projects Lawrence Safety Award for the successful implementation of the KPNW Region’s “transitional care” bundle to a Region that was almost 8 times the size of KPNW. The KPSC Transition in Care Program consists of 6 KPNW bundle elements and 2 additional bundle elements added by the KPSC team. The 6 KPNW bundle elements were risk stratification, standardized discharge summary, medication reconciliation, a postdischarge phone call, timely follow-up with a primary care physician, and a special transition phone number on discharge instructions. The 2 additional bundle elements added by KPSC were palliative care consult if indicated and a complex-case conference. KPSC has implemented most of the KPNW and KPSC bundle elements during the first quarter of 2012 for our Medicare risk population at all of our 13 medical centers. Each year, KPSC discharges approximately 40,000 Medicare risk patients. After implementation of bundle elements, KPSC Medicare risk all-cause 30-day Healthcare Effectiveness Data and Information Set readmissions observed-over-expected ratio and readmission rates from December 2010 to November 2012 decreased from approximately 1.0 to 0.80 and 12.8% to 11%, respectively.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783066/pdf/permj17_3p0058.pdf
Strategies to help reduce hospital readmissions
Danielle Snyderman, Brooke Salzman, Geoffrey Mills, Lauren Hersh, Susan Parks
The Journal of Family Practice | August 2014 | Vol 63, No 8 p 430-
The risk assessment tools, medication reconciliation steps, and discharge script provided here can help you keep your patients from going back into the hospital.
http://www.jfponline.com/fileadmin/qhi/jfp/pdfs/6308/JFP_06308_Article2.pdf
Triumph of hope over experience: learning from interventions to reduce avoidable hospital
admissions identified through an Academic Health and Social Care Network
Victoria Woodhams, Simon de Lusignan Shakeel Mughal, Graham Head, Safia Debar Terry Desombre Sean Hilton and Houda Al Sharifi
BMC Health Services Research 2012, 12:153
Background: Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare.
Method: We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN). We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops.
Results: We identified three types of intervention: pre-hospital; within the emergency department (ED); and postadmission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR – Patients at risk of readmission and ACG – Adjusted Clinical Groups) sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP) within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission.
Conclusions: Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don’t change.
http://www.biomedcentral.com/content/pdf/1472-6963-12-153.pdf
Reducing Hospital Readmissions: Lessons from Top-Performing Hospitals
Sharon Silow-Carroll, Jennifer N. Edwards, and Aimee Lashbrook
The commonwealth Fund Synthesis Report • April 2011
http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=34&ved=0CCoQFjADOB4&url=http%3A%2F%2Fwww.iienet2.org%2Fuploadedfiles%2FSHSNew%2FLessonsfromtopperforminghospitals.pdf&ei=RS0zVLXyG4TooAT234DgAQ&usg=AFQjCNFeZZvIn9qEyUcA-6CoDp3UBCz3SA&bvm=bv.76943099,d.cGU&cad=rja
A Brief Risk-stratification Tool to Predict Repeat Emergency Department Visits and Hospitalizations
in Older Patients Discharged from the Emergency Department
Stephen W. Meldon, MD, Lorraine C. Mion, PhD, RN, Robert M. Palmer, MD, MPH,
Barbara L. Drew, PhD, RN, Jason T. Connor, MS, Linda J. Lewicki, PhD, RN,
David M. Bass, PhD, Charles L. Emerman,
Academic Emergency Medicine 2003; 10:224–232.
http://onlinelibrary.wiley.com/doi/10.1197/aemj.10.3.224/pdf
Risk Prediction Tools
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/215490/dh_129779.pdf
Predictive risk project literature review: summary
http://www.kingsfund.org.uk/sites/files/kf/predictive-risk-literature-review-summary-june2005_0.pdf
Predictive risk project literature review
http://www.kingsfund.org.uk/sites/files/kf/field/field_document/predictive-risk-literature-review-june2005.pdf
PARR case finding report
http://www.kingsfund.org.uk/sites/files/kf/field/field_document/PARR-case-finding-algorithms-feb06.pdf
Combined Predictive Model Final Report
http://www.kingsfund.org.uk/sites/files/kf/field/field_document/PARR-combined-predictive-model-final-report-dec06.pdf
Developing Decision Trees to Classify Patients Suited for Similar Interventions by Combining Clinical
Judgments with Leeds Risk Stratification Tool
London School of Economics and Political Science
Chenxi Yi
3rd Sep, 2012
http://www.cihm.leeds.ac.uk/new/wp-content/uploads/2011/10/Report-NHS-Risk-Stratification.pdf
Risk stratification for patients with high care needs: the experience of the integrated care team in the Singapore General Hospital
Lian Leng Low,
Int J Integr Care 2013; WCIC Conf Suppl; URN:NBN:NL:UI:10-1-115974
http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=103&ved=0CCcQFjACOGQ&url=http%3A%2F%2Fwww.ijic.org%2Findex.php%2Fijic%2Farticle%2Fdownload%2F1494%2F2334&ei=WUEzVIehM4iyogTG74HgBw&usg=AFQjCNFYm_U5IY5GBx4yPSA-xaugRcbHQA&cad=rja
What it takes to make integrated care work
McKinsey
https://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CC0QFjAC&url=https%3A%2F%2Fwww.mckinsey.com%2Fclient_service%2Fhealthcare_systems_and_services%2Fpeople%2F~%2Fmedia%2FCE30B324913D46A6A3C8A2329DFF0C8D.ashx&ei=EEIzVLSGBtDuoASqloLoAg&usg=AFQjCNGAm0JB2s_4sGWw4WY__OuxG1Hdrw&cad=rja
Development of an algorithm to stratify patients by risk of acute hospitalisation
Tom Love, James Swansson, Claire Whelen
Report prepared for the Greater Auckland Integrated Health Network 28 April 2014
http://www.healthpointpathways.co.nz/assets/AtRiskIndividuals/Risk%20prediction%20report%20FINAL%202014.04.28.pdf
Risk Stratification: Recalibration of the ACG System Predictive Models
NHS
http://www.cscsu.nhs.uk/wp-content/uploads/2014/08/Risk-Stratification-Recalibration-of-the-ACG-System-Predictive-Models.pdf
Early Identification Of People At-Risk Of Hospitalization
Hospital Admission Risk Prediction (HARP) – a new tool for supporting providers and patients
https://secure.cihi.ca/free_products/HARP_reportv_En.pdf
Tool and Resource Evaluation Template
Adapted by NARI from an evaluation template created by Melbourne Health.
http://www.health.vic.gov.au/older/toolkit/03Assessment/docs/Identification%20of%20Seniors%20at%20Risk%20%28ISAR%29.pdf
Hospitalization Risk Screening Tool for Primary Care Providers and Teams
http://www.ihconline.org/UserDocs/Pages/HARMS-8.pdf
Predictive risk modelling in health: options for New Zealand and Australia.
Panattoni LE. Vaithianathan R. Ashton T. Lewis GH.
Australian Health Review. 35(1):45-51, 2011 Feb.
Predictive risk models (PRMs) are case-finding tools that enable health care systems to identify patients at risk of expensive and potentially avoidable events such as emergency hospitalisation. Examples include the PARR (Patients-at-Risk-of-Rehospitalisation) tool and Combined Predictive Model used by the National Health Service in England. When such models are coupled with an appropriate preventive intervention designed to avert the adverse event, they represent a useful strategy for improving the cost-effectiveness of preventive health care. This article reviews the current knowledge about PRMs and explores some of the issues surrounding the potential introduction of a PRM to a public health system. We make a particular case for New Zealand, but also consider issues that are relevant to Australia.
Identifying Potentially Preventable Readmissions
Norbert I. Goldfield, M.D., Elizabeth C. McCullough, M.S., John S. Hughes, M.D., Ana M. Tang, Beth Eastman, M.S., Lisa K. Rawlins, and Richard F. Averill, M.S.
Health Care Financing Review/Fall 2008/Volume 30, Number 1
The potentially preventable readmission (PPR) method uses administrative data to identify hospital readmissions that may indicate problems with quality of care. The PPR logic determines whether the reason for readmission is clinically related to a prior admission, and therefore potentially preventable. The likelihood of a PPR was found to be dependent on severity of illness, extremes of age, and the presence of mental health diagnoses. Analyses using PPRs show that readmission rates increase with increasing severity of illness and increasing time between admission and readmission, vary by the type of prior admission, and are stable within hospitals over time.
Predicting and preventing avoidable hospital admissions: a review. [Review]
Purdey S. Huntley A.
Journal of the Royal College of Physicians of Edinburgh. 43(4):340-4, 2013.
The strongest risk factors for avoidable hospital admission are age and deprivation but ethnicity, distance to hospital, rurality, lifestyle and meteorological factors are also important, as well as access to primary care. There is still considerable uncertainty around which admissions are avoidable. In terms of services to reduce admissions there is evidence of effectiveness for education, self-management, exercise and rehabilitation, and telemedicine in certain patient populations, mainly respiratory and cardiovascular. Specialist heart failure services and end-of-life care also reduce these admissions. However, case management, specialist clinics, care pathways and guidelines, medication reviews, vaccine programmes and hospital at home do not appear to reduce avoidable admissions. There is insufficient evidence on the role of combinations or coordinated system-wide care services, emergency department interventions, continuity of care, home visits or pay-by-performance schemes. This highlights the importance of robust evaluation of services as they are introduced into health and social care systems.
Reducing avoidable hospital admission in older people: health status, frailty and predicting risk of ill-defined conditions diagnoses in older people admitted with collapse.
Hunt K. Walsh B. Voegeli D. Roberts H.
Archives of Gerontology & Geriatrics. 57(2):172-6, 2013 Sep-Oct.
Emergency hospital admissions for patients with ill-defined conditions International Classification of Diseases-10 R codes (ICD-10 R codes) are rising. Policy literature has suggested that they are attributable to 'social' problems and could potentially be avoided yet there is no research evidence to support this view. Therefore, this study sought to describe patients with ill-defined conditions and determine clinical and demographic factors predicting assignment of such codes. Patients aged over 70 admitted to a hospital acute admissions unit with collapse or falls were recruited in one hospital. Measures of functional status, frailty, depression, routine blood tests, demographic and service use data were collected. 80 patients were recruited, 35 were discharged with ill-defined conditions codes. Functional limitations were common in patients with ill-defined conditions and 77% had frailty. Blood profiles did not indicate acute medical problems. Deprivation was the only significant independent predictor of assignment of ill-defined conditions codes at discharge (OR 0.64, 95% CI: 0.45-0.93). Whilst our data confirm policy suppositions that patients with ill-defined conditions have functional impairment and frailty, it is the social and organisational factors that are important in determining risk of ill-defined conditions rather than clinical indicators. Copyright 2013 Elsevier Ireland Ltd. All rights reserved.
Identifying potentially avoidable hospital admissions from canadian long-term care facilities.
Walker JD. Teare GF. Hogan DB. Lewis S. Maxwell CJ.
Medical Care. 47(2):250-4, 2009 Feb.
BACKGROUND: The provision of preventive services and continuity of care are important aspects of long-term care (LTC). A proposed quality indicator of such care is the rate of hospitalizations due to ambulatory care sensitive conditions (ACSCs). As the ACSC approach to identifying potentially avoidable hospitalizations (PAH) was developed for younger community-dwelling adults in the United States, we sought to examine its applicability as a quality indicator for older institutionalized residents in Canada.
METHODS: ACSCs were identified in a linked hospital-based LTC and acute care administrative database at the Institute for Clinical Evaluative Sciences in Ontario, Canada. An expert panel was then convened to assess the applicability of existing ACSCs to an older institutionalized population in Canada and to develop consensus-based revisions appropriate to this setting. The revised definition of PAH was then applied to the same linked database.
RESULTS: The proportion of hospitalizations categorized as a PAH using the original ACSCs was 47% (4177 of 8885). The panel suggested the inclusion of 2 new conditions (septicemia and falls/fractures) coupled with the deletion of 4 of the original ACSCs (immunization-preventable conditions; nutritional deficiency; severe ear, nose and throat infections; tuberculosis) that were rare hospital diagnoses in this population. Using the revised definition, 55% of hospitalizations (4874) were identified as potentially avoidable.
CONCLUSIONS: Changes to the original list of ACSCs led to more hospitalizations being categorized as potentially avoidable. Significant variation between LTC facilities and over time in our PAH indicator may identify areas for improvement in preventive services and continuity of care for LTC residents.
Avoidable admissions and repeat admissions: what do they tell us?.
Porter J. Herring J. Lacroix J. Levinton C.
Healthcare Quarterly. 10(1):26-8, 2007.
Avoidable hospitalisations: potential for primary and public health initiatives in Canterbury, New Zealand.
Sheerin I. Allen G. Henare M. Craig K.
New Zealand Medical Journal. 119(1236):U2029, 2006.
AIM: To investigate the extent of potentially "avoidable hospitalisations" in the Canterbury District Health Board area; specifically, to identify the leading causes, recent trends, and estimated costs of avoidable hospitalisations.
METHODS: All hospitalisations in Christchurch Hospital from 2000 to 2004 were analysed and potentially "avoidable admissions" were categorised using ICD10 clinical codes. Costs of these admissions were estimated for the financial year ending 30 June 2003 using diagnostic-related groups (DRGs).