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BRIAN TUTT LIBRARY AND KNOWLEDGE SERVICES

connecting people to information



Hospitalisation Risk

Articles available in full-text can be retrieved by clicking the links at the end of the reference.


To order any other articles, please use the “Request Article” link in the Library catalogue at http://internal4.health.nsw.gov.au/eureka/loginLMSID.jsp?callFrom=toc&callWhat=addCustomRefArticle.jsp or contact the library ills@doh.health.nsw.gov.au



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 score


Julia 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).

RESULTS: The leading causes of potentially "avoidable hospitalisations" in Christchurch Hospital were cardiovascular disease, stroke, respiratory, gastrointestinal, and urinary disorders. The total estimated costs of avoidable hospitalisations in 2003 were NZ 96.6 million dollars, accounting for an estimated 94,462 bed days. The estimated costs of cardiovascular admissions (excluding stroke) were 50.6 million dollars, with stroke accounting for an additional 6.2 million dollars.

CONCLUSION: Potentially "avoidable admissions" to Christchurch Hospital comprised 31% of all hospital admissions. There is considerable opportunity to invest in public and primary health initiatives aimed at early detection and intervention, with the major opportunities being identified as cardiovascular disease, stroke, respiratory, gastrointestinal, and urinary disorders.

Continuity of care and the risk of preventable hospitalization in older adults.

Nyweide DJ. Anthony DL. Bynum JP. Strawderman RL. Weeks WB. Casalino LP. Fisher ES.



JAMA Internal Medicine. 173(20):1879-85, 2013 Nov 11.

IMPORTANCE: Preventable hospitalizations are common among older adults for reasons that are not well understood.

OBJECTIVE: To determine whether Medicare patients with ambulatory visit patterns indicating higher continuity of care have a lower risk of preventable hospitalization.

DESIGN: Retrospective cohort study.

SETTING: Ambulatory visits and hospital admissions.

PARTICIPANTS: Continuously enrolled fee-for-service Medicare beneficiaries older than 65 years with at least 4 ambulatory visits in 2008.

EXPOSURES: The concentration of patient visits with physicians measured for up to 24 months using the continuity of care score and usual provider continuity score on a scale from 0 to 1.

MAIN OUTCOMES AND MEASURES: Index occurrence of any 1 of 13 preventable hospital admissions, censoring patients at the end of their 24-month follow-up period if no preventable hospital admissions occurred, or if they died.

RESULTS: Of the 3,276,635 eligible patients, 12.6% had a preventable hospitalization during their 2-year observation period, most commonly for congestive heart failure (25%), bacterial pneumonia (22.7%), urinary infection (14.9%), or chronic obstructive pulmonary disease (12.5%). After adjustment for patient baseline characteristics and market-level factors, a 0.1 increase in continuity of care according to either continuity metric was associated with about a 2% lower rate of preventable hospitalization (continuity of care score hazard ratio [HR], 0.98 [95% CI, 0.98-0.99; usual provider continuity score HR, 0.98 [95% CI, 0.98-0.98). Continuity of care was not related to mortality rates.

CONCLUSIONS AND RELEVANCE: Among fee-for-service Medicare beneficiaries older than 65 years, higher continuity of ambulatory care is associated with a lower rate of preventable hospitalization.



Geriatrics and the triple aim: defining preventable hospitalizations in the long-term care population.

Ouslander JG. Maslow K.



Journal of the American Geriatrics Society. 60(12):2313-8, 2012 Dec.

Reducing preventable hospitalizations is fundamental to the "triple aim" of improving care, improving health, and reducing costs. New federal government initiatives that create strong pressure to reduce such hospitalizations are being or will soon be implemented. These initiatives use quality measures to define which hospitalizations are preventable. Reducing hospitalizations could greatly benefit frail and chronically ill adults and older people who receive long-term care (LTC) because they often experience negative effects of hospitalization, including hospital-acquired conditions, morbidity, and loss of functional abilities. Conversely, reducing hospitalizations could mean that some people will not receive hospital care they need, especially if the selected measures do not adequately define hospitalizations that can be prevented without jeopardizing the person's health and safety. An extensive literature search identified 250 measures of preventable hospitalizations, but the measures have not been validated in the LTC population and generally do not account for comorbidity or the capacity of various LTC settings to provide the required care without hospitalization. Additional efforts are needed to develop measures that accurately differentiate preventable from necessary hospitalizations for the LTC population, are transparent and fair to providers, and minimize the potential for gaming and unintended consequences. As the new initiatives take effect, it is critical to monitor their effect and to develop and disseminate training and resources to support the many community- and institution-based healthcare professionals and emergency department staff involved in decisions about hospitalization for this population. 2012, Copyright the Authors Journal compilation



Predictors of preventable hospitalization in chronic disease: priorities for change. [Review] [47 refs]

Muenchberger H. Kendall E.



Journal of Public Health Policy. 31(2):150-63, 2010 Jul.

Research in the area of preventable hospitalization, hospital admissions that could otherwise be avoided, provides little guidance in terms of priority areas for change. This synthesis of multiple electronic databases searched systematically for studies related to preventable hospitalization identifies six priority areas for future action in three broad conceptual areas: person priorities (symptom management and supportive relationships), programme priorities (self-management supports and service delivery), and place priorities (local infrastructure and socio-economic opportunities). Attention to these priorities could help reduce preventable hospitalization while simultaneously improving health access and quality of care. [References: 47]



Recognising potential for preventing hospitalisation.

Banham D. Woollacott T. Gray J. Humphrys B. Mihnev A. McDermott R.



Australian Health Review. 34(1):116-22, 2010 Mar.

To identify the incidence and distribution of public hospital admissions in South Australia that could potentially be prevented with appropriate use of primary care services, analysis was completed of all public hospital separations from July 2006 to June 2008 in SA. This included those classified as potentially preventable using the Australian Institute of Health and Welfare criteria for selected potentially preventable hospitalisations (SPPH), by events and by individual, with statistical local area geocoding and allocation of relative socioeconomic disadvantage quintile. A total of 744 723 public hospital separations were recorded, of which 79 424 (10.7%) were classified as potentially preventable. Of these, 59% were for chronic conditions, and 29% were derived from the bottom socioeconomic status (SES) quintile. Individuals in the lowest SES quintile were 2.5 times more likely to be admitted for a potentially preventable condition than those from the top SES quintile. Older individuals, males, those in the most disadvantaged quintiles, non-metropolitan areas and Indigenous people were more likely to have more than one preventable admission.



Causes of unplanned hospital admissions: implications for practice and policy.

Silver MP. Ferry RJ. Edmonds C.



Home Healthcare Nurse. 28(2):71-81, 2010 Feb.

Unplanned hospitalizations among home health patients were reviewed to identify preventable hospital admissions and their causes. Study methods included treatment record review; interviews with home health visit staff, supervisors, and managers; and review of orientation materials and policies. Findings from this study suggest focus areas for home health agencies and other stakeholders to reduce acute care hospitalization rates and in other quality improvement initiatives.




Continuity of care and the risk of preventable hospitalization in older adults.

Nyweide DJ. Anthony DL. Bynum JP. Strawderman RL. Weeks WB. Casalino LP. Fisher ES.



JAMA Internal Medicine. 173(20):1879-85, 2013 Nov 11.

IMPORTANCE: Preventable hospitalizations are common among older adults for reasons that are not well understood.

OBJECTIVE: To determine whether Medicare patients with ambulatory visit patterns indicating higher continuity of care have a lower risk of preventable hospitalization.

DESIGN: Retrospective cohort study.

SETTING: Ambulatory visits and hospital admissions.

PARTICIPANTS: Continuously enrolled fee-for-service Medicare beneficiaries older than 65 years with at least 4 ambulatory visits in 2008.

EXPOSURES: The concentration of patient visits with physicians measured for up to 24 months using the continuity of care score and usual provider continuity score on a scale from 0 to 1.

MAIN OUTCOMES AND MEASURES: Index occurrence of any 1 of 13 preventable hospital admissions, censoring patients at the end of their 24-month follow-up period if no preventable hospital admissions occurred, or if they died.

RESULTS: Of the 3,276,635 eligible patients, 12.6% had a preventable hospitalization during their 2-year observation period, most commonly for congestive heart failure (25%), bacterial pneumonia (22.7%), urinary infection (14.9%), or chronic obstructive pulmonary disease (12.5%). After adjustment for patient baseline characteristics and market-level factors, a 0.1 increase in continuity of care according to either continuity metric was associated with about a 2% lower rate of preventable hospitalization (continuity of care score hazard ratio [HR], 0.98 [95% CI, 0.98-0.99; usual provider continuity score HR, 0.98 [95% CI, 0.98-0.98). Continuity of care was not related to mortality rates.

CONCLUSIONS AND RELEVANCE: Among fee-for-service Medicare beneficiaries older than 65 years, higher continuity of ambulatory care is associated with a lower rate of preventable hospitalization.



Preventable hospital-acquired conditions: the whys and wherefores.

Catalano K.



Plastic Surgical Nursing. 28(3):158-61, 2008 Jul-Sep.

The changes to the Centers for Medicare & Medicaid Services (CMS) Inpatient Prospective Payment System published in the Federal Register (, Vol. 72, No. 162) on August 22, 2007, introduced the term preventable "hospital-acquired conditions" (HACs). These printed rules and regulations came about through a provision in the Deficit Reduction Act of 2005 (Pub. No. 109-171) and required the Secretary of the Department of Health and Human Services to track and report on conditions considered to be high cost, high volume (or both); assigned a higher paying "diagnosis related group" (DRG) when present as a secondary diagnosis; and were thought to be reasonably preventable when evidence-based guidelines were employed. In order to comply with this mandate, the CMS, a federal agency within Health and Human Services, was assigned the task of choosing "preventable" HACs, also referred to as "serious preventable events," for which reporting and tracking would be conducted; with the added opportunity of reporting and tracking data regarding a patient's "present on admission" condition. These preventable HACs become of particular interest to hospitals on October 1, 2008. That is the day CMS begins freeing itself from paying hospitals for the targeted preventable HACs that afflict Medicare patients during their hospital stay and which were not present at the time of the patients' admission to the hospital. It is a form of pay-for-performance.



Risk-Stratification Methods for Identifying Patients for Care Coordination

Lindsey R. Haas, MPH; Paul Y. Takahashi, MD; Nilay D. Shah, PhD; Robert J. Stroebel, MD; Matthew E. Bernard, MD; Dawn M. Finnie, MPA; and James M. Naessens



Am J Manag Care. 2013;19(9):725-732

Background: Care coordination is a key component of the patient-centered medical home. However, the mechanism for identifying primary care patients who may benefit the most from this model of care is unclear.


Objectives: To evaluate the performance of several risk-adjustment/stratification instruments in predicting healthcare utilization.
Study Design: Retrospective cohort analysis.
Methods: All adults empaneled in 2009 and 2010 (n = 83,187) in a primary care practice were studied. We evaluated 6 models: Adjusted Clinical Groups (ACGs), Hierarchical Condition Categories (HCCs), Elder Risk Assessment, Chronic Comorbidity Count, Charlson Comorbidity Index, and Minnesota Health Care Home Tiering. A seventh model combining Minnesota Tiering with ERA score was also assessed. Logistic regression models using demographic characteristics and diagnoses from 2009 were used to predict healthcare utilization and costs for 2010 with binary outcomes (emergency department [ED] visits, hospitalizations, 30-day readmissions, and highcost users in the top 10%), using the C statistic and goodness of fit among the top decile.
Results: The ACG model outperformed the others in predicting hospitalizations with a C statistic range of 0.67 (CMS-HCC) to 0.73. In predicting ED visits, the C statistic ranged from 0.58 (CMSHCC) to 0.67 (ACG). When predicting the top 10% highest cost users, the performance of the ACG model was good (area under the curve = 0.76) and superior to the others.
Conclusions: Although ACG models generally performed better in predicting utilization, use of any of these models will help practices implement care coordination more efficiently. - See more at: http://www.ajmc.com/publications/issue/2013/2013-1-vol19-n9/Risk-Stratification-Methods-for-Identifying-Patients-for-Care-Coordination#sthash.qOZ7gN3W.dpuf
A systematic review of predictors and screening instruments to identify older hospitalized patients at risk for functional decline.

Hoogerduijn JG, Schuurmans MJ, Duijnstee MS, de Rooij SE, Grypdonck MF.



Journal of Clinical Nursing 2007 16(1), 46-57


Predicting risk of hospitalisation or death: a retrospective population-based analysis
Daniel Z Louis Mary Robeson John McAna Vittorio Maio Scott W Keith Mengdan Liu Joseph S Gonnella Roberto Grilli


BMJ Open 2014;4:e005223 doi:10.1136/bmjopen-2014-005223
Objectives Develop predictive models using an administrative healthcare database that provide information for Patient-Centred Medical Homes to proactively identify patients at risk of hospitalisation for conditions that may be impacted through improved patient care.
Design Retrospective healthcare utilisation analysis with multivariate logistic regression models.
Data A population-based longitudinal database of residents served by the Emilia-Romagna, Italy, health service in the years 2004–2012 including demographic information and utilisation of health services by 3 726 380 people aged ≥18 years.
Outcome measures Models designed to predict risk of hospitalisation or death in 2012 for problems that are potentially avoidable were developed and evaluated using the area under the receiver operating curve C-statistic, in terms of their sensitivity, specificity and positive predictive value, and for calibration to assess performance across levels of predicted risk.
Results Among the 3 726 380 adult residents of Emilia-Romagna at the end of 2011, 449 163 (12.1%) were hospitalised in 2012; 4.2% were hospitalised for the selected conditions or died in 2012 (3.6% hospitalised, 1.3% died). The C-statistic for predicting 2012 outcomes was 0.856. The model was well calibrated across categories of predicted risk. For those patients in the highest predicted risk decile group, the average predicted risk was 23.9% and the actual prevalence of hospitalisation or death was 24.2%.
Conclusions We have developed a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for residents of the Emilia-Romagna region with a level of performance as high as, or higher than, similar models. The results of this model, along with profiles of patients identified as high risk are being provided to the physicians and other healthcare professionals associated with the Patient Centred Medical Homes to aid in planning for care management and interventions that may reduce their patients’ likelihood of a preventable, high-cost hospitalisation.


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