View source for A decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes

This is a review for FJ Ampudia-Blasco’s A Decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes.<ref name="Blasco, 2014">Ampudia-Blasco FJ1, Benhamou PY, Charpentier G, et al. A Decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes. Diabetes Technol Ther. 2015;17(3):194-202.

http://wwwncbinlmnihgov.ezproxyhost.library.tmc.edu/pmc/articles/PMC4346378/</ref>

== Introduction ==

Over the past 20 years the number of individuals being diagnosed with type 2 diabetes has increased significantly as have the number of drugs available for the treatment of type 2 diabetes. However, phenotypes in vary widely, with substantial heterogeneity in clinical outcomes. Therefore, healthcare professionals now have many pharmacological approaches available to tailor treatment to individual patient needs. However, the expansion in clinical options is accompanied by a general lack of long-term comparative effectiveness studies to inform clinical decision-making, as well as new uncertainties regarding the long-term benefits of new drugs, for example, on macrovascular complications. The goal was to developed a patient-specific decision support tool based on a systematic analysis of expert opinion.

== Methods ==

Based on the American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) 2012 position statement, a panel of 12 European experts rated the appropriateness (RAND/UCLA Appropriateness Method) of treatment strategies for 930 clinical scenarios, which were permutations of clinical variables considered relevant to treatment choice. These included current treatment, hemoglobin A1c difference from individualized target, risk of hypoglycemia, body mass index, life expectancy, and comorbidities. Treatment options included addition of a second or third agent, drug switches, and replacement by monotherapies if the patient was metformin-intolerant. Treatment costs were not considered. Appropriateness (appropriate, inappropriate, uncertain) was based on the median score and expert agreement. The panel recommendations were embedded in an online decision support tool (DiaScope®; Novo Nordisk Health Care AG, Zürich, Switzerland).
=== The Survey Sites ===

Three hospitals were surveyed with each hospital at a slightly different stage in the CPOE implementation pathway. One organization had begun rolling out their CPOE system on one small clinical unit. The second was in the early, pre-CPOE phase of their roll-out. The Third organization with within 2 months of their planned go-live dates. 

El Camino Hospital, Mountain View, CA - El Camino Hospital is a 395-bed community hospital serving the Mountain View, CA area; the heart of the “Silicon Valley.” The hospital was upgrading from the first implemented Lockheed system in the 1970’s to the new Eclipses Sunrise system.

Kaiser Permanente, Sunnyside Hospital, Portland, OR—Kaiser Permanente's Sunnyside hospital is a 196-bed community hospital serving Kaiser Permanente, Northwest members in the greater Portland metropolitan area. At the time of this survey, Sunnyside hospital had just “gone live” with phase I (Admission/ Discharge/Transfer, new inpatient pharmacy, hospital billing, and Emergency Department tracking systems, all from Epic Systems) of their in-patient clinical information system rollout and were approximately four months away from the planned hospital-wide roll-out of their CPOE system.

Portland Providence Medical Center, Portland, OR—Portland Providence hospital is a 483-bed, community hospital serving the Portland metropolitan area. At the time of this study survey, they were approximately four months into a CPOE pilot on their rehabilitation unit, with plans to begin a phased roll-out to the rest of the in-patient clinical units over the next two years.

=== Methods ===

Following Institutional Review Board approval of the study at Oregon Health & Science University (OHSU), Kaiser Permanente Northwest, and each study site, we created an open ended, semi-structured interview survey template that we customized for each organization. This survey was designed to be administered orally to clinicians and take approximately five minutes to complete, although we did not stop any clinician from discussing the topics in greater depth, if they desired. The survey was administered the survey to clinicians within each organization at common gathering places - a true “convenience” sample.

=== Statistical Analysis===

For calculating the appropriateness of treatments, the mathematical rules that are typically applied in RUAM studies were used.12 The outcome was appropriate if the median panel score was between 7 and 9 and inappropriate if the median was between 1 and 3, both without disagreement between panelists. Disagreement was defined when at least four out of 12 panelists scored in each of the sections 1–3 and 7–9. All other situations were deemed uncertain. Frequency tables and cross-tabulations were used to describe and analyze the appropriateness of treatments by clinical variables.


== Electronic Decision Tool==

The results of the second round were embedded in an electronic decision support tool (DiaScope®; Novo Nordisk Health Care AG, Zürich, Switzerland), which shows the appropriateness of treatments for any given patient profile. Where applicable, the results of separate conditions were combined with overall panel recommendations using the principle that the final outcome is determined by the lowest appropriateness category of the separate conditions.

== Results==
The results of the second rating round were embedded in an electronic decision support tool, called DiaScope. The heart of this decision tool is an interface that allows the user to create a patient profile and to see the appropriateness of the various treatment options . Clicking through on a treatment shows the considerations behind the panel recommendation and provides detailed additional information on, for example, comorbidities. The tool is available in both online and offline formats and is accessible for physicians after registration (http://diascope.org).

=== Discussion ===
Adequate glucose-lowering therapy in subjects with T2DM requires repeated follow-up, with frequent adjustments of goals and treatment as the disease progresses. Real-life audits show that approximately 40% of patients in Europe do not have good glycemic control despite a large therapeutic armamentarium and numerous guidelines.

This tool may offer individual healthcare professionals an opportunity to assess a relevant clinical situation, draw their own conclusions, and then compare them with the panel recommendation. Although the panelists were almost all diabetologists, they took the RAND/UCLA perspective, “an average patient presenting to an average physician in an average care-providing facility,”13 as the starting point for their considerations, making the tool applicable for decision-making in most common healthcare settings. By providing a “second opinion” directly at the point of care, DiaScope should be viewed as an educational tool that could help promote the adoption and utilization of the ADA/EASD statements by presenting them in the context of interactive clinical scenarios.
Each time a CDSS is developed, the inevitable question is whether it will affect clinical care. Several CDSSs for the management of T2D for healthcare providers already exist, with large differences in terms of development methods, algorithms, content and sophistication level.20–24 In-depth literature reviews of CDSSs, one of which is in primary T2DM care, found that these tools can indeed be effective in improving the process of care, although few have shown improvements in patient outcomes.25,26 However, as many factors may influence health outcomes, measuring the effectiveness of CDSSs on these end points is difficult.
On the other hand, there is a risk that CDSSs could generate erroneous advice or misinterpretations. DiaScope is a simple and user-friendly tool that displays experts' opinions for all common treatment options instead of a single solution. Likewise, patient treatment preference was not retained to avoid excluding prematurely valuable options, as preference may be managed by physician–patient discussion or patient education as a subsequent step.
Most important is that a decision support system is only as effective as its underlying knowledge base, which changes rapidly as medical science evolves.27–29 Therefore we plan annual updates of DiaScope to reflect knowledge progression, changing guidelines, and increased expertise, making the tool as “evidence-adaptive” as possible.

== Conclusion ==

Using the RAND/UCLA approach, an expert panel formulated patient-specific recommendations for glucose-lowering therapy in T2DM across numerous clinical scenarios, all embedded in an electronic tool. With the evolving complexity of T2DM management and the increasingly important role of general practitioners in managing these patients, the DiaScope tool may facilitate decision-making and eventually help to reduce clinical inertia. Further research will evaluate its applicability in primary care practice.

== Related Articles ==

*[[Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards]]

== References ==
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