Literature DB >> 6403780

Diagnosis clusters: a new tool for analyzing the content of ambulatory medical care.

R Schneeweiss, R A Rosenblatt, D C Cherkin, C R Kirkwood, G Hart.   

Abstract

A clustering method for the analysis of ambulatory morbidity data is presented. This approach reduces spurious variations resulting from idiosyncratic diagnosis labeling and coding habits of physicians and facilitates the analysis of the content of ambulatory medical care through the use of aggregate morbidity data. The clusters provide a tool that allows for the comparison of the content of practice based on different factors such as provider training, practice organization, and patient characteristics. Ninety-two diagnosis clusters were derived using the 1977 and 1978 National Ambulatory Medical Care Survey (NAMCS). These clusters incorporate 86 per cent of all ambulatory visits to office-based physicians in the contiguous United States. The clusters were constructed based on the consensus of a group of clinicians including both generalists, as well as selected subspecialists representing the spectrum of ambulatory medical practice. The diagnosis clusters presented are compatible with the International Classification of Diseases (ICDA-8 and ICD-9-CM) and the International Classifications of Health Problems in Primary Care (ICHPPC and ICHPPC-2). Several applications demonstrating the utility of the method are presented, and directions for future applications are suggested.

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Year:  1983        PMID: 6403780     DOI: 10.1097/00005650-198301000-00008

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  35 in total

1.  Reliability of report coding of hospital referrals in primary care versus practice-based coding.

Authors:  L Letrilliart; M Guiguet; A Flahault
Journal:  Eur J Epidemiol       Date:  2000       Impact factor: 8.082

2.  Member risk adjustment for ambulatory episodes of care.

Authors:  D J Brailer; E A Kroch
Journal:  Health Care Manag Sci       Date:  1999-07

3.  Whom should we profile? Examining diabetes care practice variation among primary care providers, provider groups, and health care facilities.

Authors:  Sarah L Krein; Timothy P Hofer; Eve A Kerr; Rodney A Hayward
Journal:  Health Serv Res       Date:  2002-10       Impact factor: 3.402

Review 4.  An industrial process view of information delivery to support clinical decision making: implications for systems design and process measures.

Authors:  R B Elson; J G Faughnan; D P Connelly
Journal:  J Am Med Inform Assoc       Date:  1997 Jul-Aug       Impact factor: 4.497

5.  Why are clinical problems difficult? General practitioners' opinions concerning 24 clinical problems.

Authors:  H Leclère; M D Beaulieu; G Bordage; A Sindon; M Couillard
Journal:  CMAJ       Date:  1990-12-15       Impact factor: 8.262

6.  The nature content and interpractice variation of general practice: a regional study in Italy.

Authors:  F Taroni; R Stiassi; G Traversa; R Raschetti; F Menniti-Ippolito; M Maggini; S Spila-Alegiani
Journal:  Eur J Epidemiol       Date:  1990-09       Impact factor: 8.082

Review 7.  The efficiency of depression questionnaires for case finding in primary medical care.

Authors:  J L Coulehan; H C Schulberg; M R Block
Journal:  J Gen Intern Med       Date:  1989 Nov-Dec       Impact factor: 5.128

8.  Seasonal variation in diagnoses and visits to family physicians.

Authors:  Wilson D Pace; L Miriam Dickinson; Elizabeth W Staton
Journal:  Ann Fam Med       Date:  2004 Sep-Oct       Impact factor: 5.166

9.  Diagnosis clusters in ambulatory medicine.

Authors:  R Schneeweiss
Journal:  J Gen Intern Med       Date:  1991 Jan-Feb       Impact factor: 5.128

10.  Glycemic response to newly initiated diabetes therapies.

Authors:  Andrew J Karter; Howard H Moffet; Jennifer Liu; Melissa M Parker; Ameena T Ahmed; Alan S Go; Joe V Selby
Journal:  Am J Manag Care       Date:  2007-11       Impact factor: 2.229

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