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Proposed CDS for Colorectal Diseases
CDS Type Examples
Medication dosing support Dosing advisor for antibiotics based on r...
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Computer-Based Clinical Decision Support for Colorectal Diseases: An Overview of Barriers and Facilitators

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Computer-Based Clinical Decision Support for Colorectal Diseases: An Overview of Barriers and Facilitators

  1. 1. Proposed CDS for Colorectal Diseases CDS Type Examples Medication dosing support Dosing advisor for antibiotics based on renal function Order facilitators – Subsequent or corollary orders Order liver function tests with azathioprine Order hemoglobin test with mesalamine Order facilitators – Transfer order set Transfer to surgery step-down unit order set Order facilitators – Procedure-specific order set Post-operative colectomy order set Point of care alerts/reminders – Drug-condition interaction checking Alert when a provider orders flagyl for a female patient of childbearing age Point of care alerts/reminders – Ticklers Alert a provider when colonoscopy has been ordered but not scheduled or performed within three months Expert systems – Prognostic tools Estimate survival for cancer patients based on tumor grade and stage Workflow support – Order approvals Send all colonoscopy orders to CRS for approval Workflow support – Documentation aids Structured documentation template for a CRS visit that has common findings Allison B. McCoy, PhD and Dean F. Sittig, PhD School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), UT Houston-Memorial Hermann Center for Healthcare Quality and Safety, Houston, TX Computer-Based Clinical Decision Support for Colorectal Diseases: An Overview of Barriers and Facilitators Purpose To describe the use of clinical decision support (CDS) within healthcare information technology systems to improve care of patients with colorectal diseases. Conclusions Further research is necessary to develop and optimize CDS alerts for preventive and follow-up care, and to identify additional patient care scenarios where new interventions can benefit patients with colorectal diseases. Existing CDS for Colorectal Diseases CDS Type Examples Point of care alerts/reminders – Care reminders Reminder providers to order a yearly FOBT for patients between 45 and 85 McDonald C. Ann Intern Med (1984), McPhee S. Arch Intern Med (1989) Point of care alerts/reminders – Critical laboratory value checking Notify providers about positive FOBT results Singh H. Am J Gastroenterol (2009) Expert systems – Diagnostic support Perform natural language processing to determine time and status of colonoscopy testing Denny J. J Am Med Inform Assoc (2010) Workflow support – Registry functions Send a letter to all patients to recommend FOBT Ornstein S. J Fam Pract (1991), Goldberg D. Am J Prev Med (2004), Mosen D. Med Care (2010) Notify all patients with an expired colonoscopy order Cameron A. Arch Int Med (2010) Methods The authors reviewed published literature in informatics and medical journals, combined with expert opinion, to summarize relevant prior work. Results In the setting of colorectal diseases, alerts most often prompt primary care clinicians to order or remind patients about cancer screening tests or to follow up with patients after abnormal screening test results. These reminders can increase the rate at which screenings and follow-ups are performed, potentially decreasing the incidence and mortality of colorectal diseases. Noisy Alerts Frequent Provider Overrides Provider Non-Adherence Delivery to the Right Person Effective Workflow Integration High Algorithm Specificity Informative Notifications Complete, Accurate Triggering Data Methods for Overcoming Clinical Decision Support Barriers Sittig DF, Singh H. Eight rights of safe electronic health record use. JAMA 2009;302(10). McCoy AB, Waitman LR, Lewis JB, et al. A framework for evaluating the clinical impact of computerized medication safety alerts. J Am Med Inform Assoc (Under Review). Wright A, Sittig DF, Ash JS, et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc. 2011;8(3). Irrelevant Information

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