Electronic Decision Support for Intervention in Poorly Controlled Type 2 Diabetes

2016-10-06 00:38:23 | BioPortfolio


To determine the impact of an electronic decision support tool on physician decision making and patient outcomes for the treatment of poorly controlled diabetes mellitus. Primary endpoint will measure change in hemoglobin A1c. Secondary endpoints will examine cost of therapy and patient satisfaction with therapy. Study hypothesis is that use of the PATH decision tool will produce greater reduction overall in measurements of hemoglobin A1c in patients who participate and follow the PATH decision tool than patients who elect not to follow the PATH decision tool. PATH decision tool will provide more cost effective solutions for management of diabetic medication than current methods.



1. Patients of selected St. Elizabeth Physicians Primary Care Physicians without a new intervention related to the management of the patient's diabetes in the last 3 months will be contacted via telephone. Patients will be drawn from a list of poorly controlled patients fitting the study criteria as generated from the St. Elizabeth Physicians diabetes registry.

2. During the telephone call, patients will be offered the opportunity to manage their diabetic medication by following the PATH decision tool. PATH is a computer program which finds a balance between low cost and high effectiveness. If participation is elected, informed consent will be mailed with a follow up phone call from certified staff.

i) If a patient elects not to participate, the reason for not participating will be recorded in the screening log. As part of the standard of care, patients not selecting the study will be offered an appointment outside the study to address their poorly controlled diabetes.

ii) If a patient elects to participate and a hemoglobin A1c has not been drawn in the last 3 months, one will be ordered through their insurance as part of the standard of care.

3. Patients who agree will be contacted by telephone by a licensed provider (MD or ARNP) who will enter non identifiable data from the patient's chart into the PATH decision support software. Information will be gathered from the Epic EHR and the questionnaire found on Article #1.

i) Study specific items will be recorded as illustrated in Article #4 ii) Time spent populating the data will be recorded, as part of the secondary analysis.

4. Patient and provider will discuss options on the telephone and further customize options and discuss regimen selections including risks and benefits with an appropriate regimen selected and recorded by the provider for score and content i) Regimen selected will be classified by "partial recommendation, full recommendation, alternate recommendation, or "no action" in relation to the options presented by the PATH program. --> final score of the course of action will be calculated and recorded.

i. Definitions:

1. Full recommendation: the regimen selected is the exact regimen shown in PATH, with no additions or deletions.

2. Subset recommendation: the regimen selected is a subset of a regimen shown in PATH (i.e., with one or more deletions), with no additions.

1. Example: PATH recommends the regimen (A, B, C, D)

2. Valid subset regimens would include (A, B, C), (B, C, D), (A, B), and (A), among others.

3. Superset recommendation: the regimen selected contains all of the elements of one regiment recommended by PATH, plus at least one other therapy.

1. Example: PATH recommends the regimen (A, B, C)

2. A valid superset recommendation would be (A, B, C, Exercise).

4. Alternate recommendation: Any other combination of additions and deletions from a PATH recommended regimen.

5. No action: No regimen was selected.

5. Manufacturer Discount coupons will be offered and mailed if applicable. i) Mailed coupons will be documented in the participant's study chart.

6. Patient will be offered immediate "in person" follow up (traditional visit) to review the changes. As this is appropriate to the treatment of diabetes, traditional CPT billing will be used and the in-person visit will be billed to insurance as part of standard of care.

i) Patient's choice to participate in a traditional office visit will be recorded in the participant's study chart.

7. Follow up visit or phone call will be scheduled at 3 months (+/- 15 days) with repeat hemoglobin A1c, following the standard of care for the treatment of diabetes. During this visit, diabetes evaluation will follow the standard of care including adherence, efficacy, and adverse events. Adverse events will be categorized through the "drug intolerance" and "comorbidities" section of the PATH software. See Article 5 i) Type of follow up will be recorded ("phone follow up, live follow up, no follow up"). Reason for no follow up will also be documented.

Study Design

Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Treatment


Diabetes Mellitus


PATH electronic decision support tool


St. Elizabeth Healthcare
United States




St Elizabeth Healthcare

Results (where available)

View Results


Published on BioPortfolio: 2016-10-06T00:38:23-0400

Clinical Trials [5155 Associated Clinical Trials listed on BioPortfolio]

Clinical Decision Support for Stroke Prevention in Atrial Fibrillation

A cluster randomised study in the primary care setting to evaluate a electronic clinical decision tool for stroke prophylaxis in patients with atrial fibrillation.

AML Electronic Decision Aid

The purpose of this study is to test the feasibility and preliminary efficacy of a novel electronic decision aid to improve AML patients' understanding of their illness, prognosis, and tre...

Impact of a Computerized Guidelines on the Management of Hypertension and Diabetes

The purpose is to determine wether an electronic decision support system based on national guidelines is effective to improve the follow-up and the treatment of two conditions: hypertensio...

Electronic Support for Pulmonary Embolism Emergency Disposition

To evaluate the impact of an integrated electronic clinical decision support system to facilitate risk stratification and site-of-care decision-making for patients with acute pulmonary emb...

Nevus Doctor Clinical Decision Support for GPs

The study investigates if a computer-based clinical decision support tool for skin cancer may improve the diagnostic accuracy of general practitioners (GPs). The aim of the program is to h...

PubMed Articles [16574 Associated PubMed Articles listed on BioPortfolio]

Acceptability of a decision-support electronic health record system and its impact on diabetes care goals in South Asia: a mixed-methods evaluation of the CARRS trial.

To describe physicians' acceptance of decision-support electronic health record system and its impact on diabetes care goals among people with Type 2 diabetes.

A Pilot Study to Reduce Computed Tomography Utilization for Pediatric Mild Head Injury in the Emergency Department Using a Clinical Decision Support Tool and a Structured Parent Discussion Tool.

The American College of Emergency Physicians embarked on the "Choosing Wisely" campaign to avoid computed tomographic (CT) scans in patients with minor head injury who are at low risk based on validat...

Gestational Diabetes Mellitus Risk score: A practical tool to predict Gestational Diabetes Mellitus risk in Tanzania.

Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to p...

Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support.

 Pediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we ...

Using Technology to Support Care in Gestational Diabetes Mellitus: Quantitative Outcomes of an Exploratory Randomised Control Trial of Adjunct Telemedicine for Gestational Diabetes Mellitus (TeleGDM).

The increasing incidence and prevalence of gestational diabetes mellitus (GDM) on a background of limited resources calls for innovative approaches healthcare provision. Our aim was to explore the eff...

Medical and Biotech [MESH] Definitions

A subclass of DIABETES MELLITUS that is not INSULIN-responsive or dependent (NIDDM). It is characterized initially by INSULIN RESISTANCE and HYPERINSULINEMIA; and eventually by GLUCOSE INTOLERANCE; HYPERGLYCEMIA; and overt diabetes. Type II diabetes mellitus is no longer considered a disease exclusively found in adults. Patients seldom develop KETOSIS but often exhibit OBESITY.

Diabetes mellitus induced experimentally by administration of various diabetogenic agents or by PANCREATECTOMY.

Mathematical or statistical procedures used as aids in making a decision. They are frequently used in medical decision-making.

Urination of a large volume of urine with an increase in urinary frequency, commonly seen in diabetes (DIABETES MELLITUS; DIABETES INSIPIDUS).

A subtype of DIABETES MELLITUS that is characterized by INSULIN deficiency. It is manifested by the sudden onset of severe HYPERGLYCEMIA, rapid progression to DIABETIC KETOACIDOSIS, and DEATH unless treated with insulin. The disease may occur at any age, but is most common in childhood or adolescence.

More From BioPortfolio on "Electronic Decision Support for Intervention in Poorly Controlled Type 2 Diabetes"

Quick Search


Relevant Topics

Diabetes is a lifelong condition that causes a person's blood sugar level to become too high. The two main types of diabetes are: type 1 diabetes type 2 diabetes In the UK, diabetes affects approximately 2.9 million people. There are a...

Blood is a specialized bodily fluid that delivers necessary substances to the body's cells (in animals) – such as nutrients and oxygen – and transports waste products away from those same cells.  In vertebrates, it is composed of blo...

Searches Linking to this Trial