BACKGROUND: Lack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management. OBJECTIVE: To examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND PARTICIPANTS: Clinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY RESULTS: Patients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool. CONCLUSIONS: A health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.
RCT Entities:
BACKGROUND: Lack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management. OBJECTIVE: To examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND PARTICIPANTS: Clinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY RESULTS:Patients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool. CONCLUSIONS: A health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.
Authors: Jerry H Gurwitz; Terry S Field; Leslie R Harrold; Jeffrey Rothschild; Kristin Debellis; Andrew C Seger; Cynthia Cadoret; Leslie S Fish; Lawrence Garber; Michael Kelleher; David W Bates Journal: JAMA Date: 2003-03-05 Impact factor: 56.272
Authors: Truls Østbye; Kimberly S H Yarnall; Katrina M Krause; Kathryn I Pollak; Margaret Gradison; J Lloyd Michener Journal: Ann Fam Med Date: 2005 May-Jun Impact factor: 5.166
Authors: Tejal K Gandhi; Saul N Weingart; Joshua Borus; Andrew C Seger; Josh Peterson; Elisabeth Burdick; Diane L Seger; Kirstin Shu; Frank Federico; Lucian L Leape; David W Bates Journal: N Engl J Med Date: 2003-04-17 Impact factor: 91.245
Authors: Nicolas Rodondi; Tiffany Peng; Andrew J Karter; Douglas C Bauer; Eric Vittinghoff; Simon Tang; Daniel Pettitt; Eve A Kerr; Joe V Selby Journal: Ann Intern Med Date: 2006-04-04 Impact factor: 25.391
Authors: Andrew J Karter; Melissa M Parker; Howard H Moffet; Ameena T Ahmed; Assiamira Ferrara; Jennifer Y Liu; Joe V Selby Journal: Med Care Date: 2004-02 Impact factor: 2.983
Authors: Meekang Sung; Jinyu He; Qi Zhou; Yaolong Chen; John S Ji; Haotian Chen; Zhihui Li Journal: J Med Internet Res Date: 2022-07-20 Impact factor: 7.076
Authors: Van C Willis; Kelly Jean Thomas Craig; Yalda Jabbarpour; Elisabeth L Scheufele; Yull E Arriaga; Monica Ajinkya; Kyu B Rhee; Andrew Bazemore Journal: JMIR Med Inform Date: 2022-01-21