OBJECTIVE: To develop a patient-based classification system to describe the clinical content of ambulatory medicine practices. DESIGN: A system of 100 diagnosis clusters was developed based on retrospective review of computerized problem lists of patients from a university practice, and then applied to the problem lists of patients in a community practice. Chart review of a 5% random sample (n = 184) of university practice patients who had problem lists was carried out to assess the accuracy of the computerized problem lists. SETTING: A university ambulatory medicine practice and a community ambulatory medicine practice. PATIENTS/PARTICIPANTS: For the same one-year period, all 4,490 patients seen in the university practice and all 1,294 patients seen two or more times in the community practice. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 27,634 problems listed for university patients and the 5,648 problems listed for community patients, 22,629 (82%) and 4,924 (87%), respectively, were assigned to diagnosis clusters. For the university and community practices, the mean numbers of problems per patient were 6.1 (SD 5.4) and 4.4 (SD 3.7), and the mean numbers of diagnosis clusters per patient were 4.5 (SD 3.7) and 3.6 (SD 3.0), respectively. Among the ten most common diagnosis clusters in both practices were HYPERTENSION, SYMPTOM OR SIGN, OBESITY, and DIABETES. Only 18% (SD 3%) of patient problem lists in the university practice omitted one or more chronic, important medical problems (e.g., hypertension, dementia, COPD). CONCLUSIONS: This system of diagnosis clusters effectively and efficiently described the clinical content of two types of internal medicine practices, and has important applications in medical education, epidemiology, clinical and health services research, and public policy.
OBJECTIVE: To develop a patient-based classification system to describe the clinical content of ambulatory medicine practices. DESIGN: A system of 100 diagnosis clusters was developed based on retrospective review of computerized problem lists of patients from a university practice, and then applied to the problem lists of patients in a community practice. Chart review of a 5% random sample (n = 184) of university practice patients who had problem lists was carried out to assess the accuracy of the computerized problem lists. SETTING: A university ambulatory medicine practice and a community ambulatory medicine practice. PATIENTS/PARTICIPANTS: For the same one-year period, all 4,490 patients seen in the university practice and all 1,294 patients seen two or more times in the community practice. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 27,634 problems listed for university patients and the 5,648 problems listed for community patients, 22,629 (82%) and 4,924 (87%), respectively, were assigned to diagnosis clusters. For the university and community practices, the mean numbers of problems per patient were 6.1 (SD 5.4) and 4.4 (SD 3.7), and the mean numbers of diagnosis clusters per patient were 4.5 (SD 3.7) and 3.6 (SD 3.0), respectively. Among the ten most common diagnosis clusters in both practices were HYPERTENSION, SYMPTOM OR SIGN, OBESITY, and DIABETES. Only 18% (SD 3%) of patient problem lists in the university practice omitted one or more chronic, important medical problems (e.g., hypertension, dementia, COPD). CONCLUSIONS: This system of diagnosis clusters effectively and efficiently described the clinical content of two types of internal medicine practices, and has important applications in medical education, epidemiology, clinical and health services research, and public policy.
Authors: R Schneeweiss; D C Cherkin; L G Hart; D A Revicki; L J Wollstadt; M J Stephenson; J Froom; E V Dunn; H L Tindall; R A Rosenblatt Journal: J Fam Pract Date: 1986-01 Impact factor: 0.493
Authors: K E Ellsbury; R Schneeweiss; D E Montano; P O Cleveland; J B Coombs; S C Eggertsen; R H Layton; J N Scardapane; M J Wanderer; E J Gore Journal: J Fam Pract Date: 1987-09 Impact factor: 0.493
Authors: R A Rosenblatt; D C Cherkin; R Schneeweiss; L G Hart; H Greenwald; C R Kirkwood; G T Perkoff Journal: J Fam Pract Date: 1982-10 Impact factor: 0.493
Authors: David M Levine; Stella Savarimuthu; Allison Squires; Joseph Nicholson; Melanie Jay Journal: J Gen Intern Med Date: 2014-08-19 Impact factor: 5.128