OBJECTIVE: To develop a valid quality measure that captures clinical inertia, the failure to initiate or intensify therapy in response to medical need, in diabetes care and to link this process measure with outcomes of glycemic control. DATA SOURCES: Existing databases from 13 Department of Veterans Affairs hospitals between 1997 and 1999. STUDY DESIGN: Laboratory results, medications, and diagnoses were collected on 23,291 patients with diabetes. We modeled the decision to increase antiglycemic medications at individual visits. We then aggregated all visits for individual patients and calculated a treatment intensity score by comparing the observed number of increases to that expected based on our model. The association between treatment intensity and two measures of glycemic control, change in HbA1c during the observation period, and whether the outcome glycosylated hemoglobin (HbA1c) was greater than 8 percent, was then examined. PRINCIPAL FINDINGS: Increases in antiglycemic medications occurred at only 9.8 percent of visits despite 39 percent of patients having an initial HbA1c level greater than 8 percent. A clinically credible model predicting increase in therapy was developed with the principal predictor being a recent HbA1c greater than 8 percent. There were considerable differences in the intensity of therapy received by patients. Those patients receiving more intensive therapy had greater improvements in control (p < .001). CONCLUSIONS: Clinical inertia can be measured in diabetes care and this process measure is linked to patient outcomes of glycemic control. This measure may be useful in efforts to improve clinicians management of patients with diabetes.
OBJECTIVE: To develop a valid quality measure that captures clinical inertia, the failure to initiate or intensify therapy in response to medical need, in diabetes care and to link this process measure with outcomes of glycemic control. DATA SOURCES: Existing databases from 13 Department of Veterans Affairs hospitals between 1997 and 1999. STUDY DESIGN: Laboratory results, medications, and diagnoses were collected on 23,291 patients with diabetes. We modeled the decision to increase antiglycemic medications at individual visits. We then aggregated all visits for individual patients and calculated a treatment intensity score by comparing the observed number of increases to that expected based on our model. The association between treatment intensity and two measures of glycemic control, change in HbA1c during the observation period, and whether the outcome glycosylated hemoglobin (HbA1c) was greater than 8 percent, was then examined. PRINCIPAL FINDINGS: Increases in antiglycemic medications occurred at only 9.8 percent of visits despite 39 percent of patients having an initial HbA1c level greater than 8 percent. A clinically credible model predicting increase in therapy was developed with the principal predictor being a recent HbA1c greater than 8 percent. There were considerable differences in the intensity of therapy received by patients. Those patients receiving more intensive therapy had greater improvements in control (p < .001). CONCLUSIONS: Clinical inertia can be measured in diabetes care and this process measure is linked to patient outcomes of glycemic control. This measure may be useful in efforts to improve clinicians management of patients with diabetes.
Authors: Richard W Grant; Enrico Cagliero; Patricia Murphy-Sheehy; Daniel E Singer; David M Nathan; James B Meigs Journal: Am J Med Date: 2002-06-01 Impact factor: 4.965
Authors: Dan R Berlowitz; Arlene S Ash; Elaine C Hickey; Mark Glickman; Robert Friedman; Boris Kader Journal: Diabetes Care Date: 2003-02 Impact factor: 19.112
Authors: M Diane McKee; Jason Fletcher; Irina Sigal; Jonathon Giftos; Clyde Schechter; Elizabeth A Walker Journal: J Prim Care Community Health Date: 2011-03-28
Authors: Jennifer Elston Lafata; Andrew J Karter; Patrick J O'Connor; Heather Morris; Julie A Schmittdiel; Scott Ratliff; Katherine M Newton; Marsha A Raebel; Ram D Pathak; Abraham Thomas; Melissa G Butler; Kristi Reynolds; Beth Waitzfelder; John F Steiner Journal: J Gen Intern Med Date: 2016-02 Impact factor: 5.128
Authors: Wayne Katon; Joan Russo; Elizabeth H B Lin; Susan R Heckbert; Andy J Karter; Lisa H Williams; Paul Ciechanowski; Evette Ludman; Michael Von Korff Journal: Psychosom Med Date: 2009-10-15 Impact factor: 4.312
Authors: T Alafia Samuels; Shari Bolen; H C Yeh; Marcela Abuid; Spyridon S Marinopoulos; Jonathan P Weiner; Maura McGuire; Frederick L Brancati Journal: J Gen Intern Med Date: 2008-09-12 Impact factor: 5.128
Authors: Shari Danielle Bolen; Eric Bricker; T Alafia Samuels; Hsin-Chieh Yeh; Spyridon S Marinopoulos; Maura McGuire; Marcela Abuid; Frederick L Brancati Journal: Diabetes Care Date: 2008-10-17 Impact factor: 19.112
Authors: Jennifer E Lafata; Elizabeth A Dobie; George W Divine; Marianne E Ulcickas Yood; Bruce D McCarthy Journal: Diabetes Care Date: 2009-08 Impact factor: 19.112