|Innovative Clinical Decision Support Tools Improve Patient Care Quality |
By Tamra Minnier, RN, MSN, FACHE
Chief Quality Officer, University of Pittsburgh Medical Center (UPMC)
Editorial Board Member, Health Care Innovations Exchange
Innovation in technology and clinical decision support is essential to leveraging a return on health care investments. A growing body of evidence suggests that clinical applications of information technology improve quality, boost patient safety, reduce lengths of stay and readmissions, and increase efficiency and timeliness of care.
The featured innovations add clinical value by providing practitioners timely, accurate information they can use to make better patient care and treatment decisions. Primary care physicians and specialists at San Francisco General Hospital use electronic communication to facilitate patient access to specialty care and consultations. The eReferral system, which has been implemented at over 40 hospital-based specialty clinics, has a text messaging program within the patient’s electronic medical record (EMR) that physicians use to request patient referrals and consultations. All communication is captured in real-time and within the patient’s EMR.
The specialty clinics use the eReferral system to provide decision support by assessing patient referral requests and responding to them within 3 business days. The designated specialty clinician approves the request and prioritizes them according to urgency or discusses the case further with the referring physician, which may lead to providing consultation or guidance. The outcome data shows that automated referrals improves patient care by reducing patient wait times by two-thirds and it enhances clinician–specialist communication and primary care provider perceptions of the quality of care provided by specialty clinics.
The program’s success depended on several factors, including a successful pilot, using EMRs, collaboration with the hospital’s information technology staff, and receiving $1.5 million in development grants to spread the eReferral system to multiple medical and surgical specialty clinics. Although staff time is required to maintain the project (information technology, program management, primary care and specialty care staff) those costs were not provided, making it difficult to estimate total program costs.
This program may work best in large hospital systems that have the necessary staff and resources to support an electronic project. In addition, when setting up an electronic referral system, it’s important to decide whether specialists in private practice and other settings should be included to avoid unintentionally promoting one group of physicians over another.
A different type of clinical decision support innovation was developed by pathologists at Massachusetts General Hospital beginning in 1993, which illustrates the long trajectory (17 years) an innovation can take from inception to adoption. The pathologists at the hospital went beyond providing the normal lab results to create a patient-specific analysis that may suggest a diagnosis, consultation, or therapeutic option or provide advice about additional testing. The innovation is that the pathologists anticipated the clinical implications of their lab results and provided information that is useful to clinicians. Pathologists and medical residents serve on a team that prepares each analysis after reviewing options from a software program and discussing test results. In addition, attending pathologists and residents perform a daily case analysis that may include other clinicians, fellows and residents if the case is particularly complex.
As a result of implementing the innovation, interaction has increased between pathologists and clinicians. If this innovation was adopted by several academic institutions, pathologists could become more active team members. A common barrier to adopting clinical innovations is the perception that it will take up too much staff time. The results showed that the interpretation of the results by pathologists saved clinicians up to 6 hours in the diagnostic process.
I recommend that interested institutions develop a similar patient-specific analysis on a small scale using patients with coagulation disorders such as heparin. Another potential barrier to implementation is the proprietary software (DxAuthor) used to generate an outline for the patient-specific analysis. However, that was integrated 5 years after the service started and could be developed by individual institutions based on their needs.
Disclosure Statement: Ms. Minnier reported having no financial interests or business/professional affiliations relevant to the work described in this article.
Original publication: May 26, 2010.
Original publication indicates the date the profile was first posted to the Innovations Exchange.
Last updated: August 27, 2014.
Last updated indicates the date the most recent changes to the profile were posted to the Innovations Exchange.
Date verified by innovator: September 08, 2013.
Date verified by innovator indicates the most recent date the innovator provided feedback during the annual review process. The innovator is invited to review, update, and verify the profile annually.