Clinical-Decision Support to Improve Hypertension Care in Primary Care

Study Overview
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Brief Summary
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Approaches are needed to help primary-care pediatricians address high blood pressure. This study will test whether an electronic health-record-based tool to address high blood pressure is feasible and improves the evaluation and management of high blood pressure in clinical practice. If successful, this approach can be used to address other lifestyle-related and complex health problems (e.g., dyslipidemia and diabetes), then disseminated and used nationwide. The investigators have developed a new, electronic health-record (EHR)-based tool that is designed to help pediatricians: IDENTIFY AND DOCUMENT when a child's blood pressure is elevated, and whether it has been elevated before--including number of prior elevations to document the correct diagnosis (for example, elevated blood pressure, vs. hypertension stage 1, vs. hypertension stage 2), THEN ORDER the next action(s) needed per guideline-based recommendations, AND per prior actions taken--including: laboratories and studies per 2017 updated guidelines follow-up interval in primary care referral to nephrology, when indicated, and patient education on diet/lifestyle modification. The investigators are working on improving this system further with addition of orders for: referral for sleep-apnea testing and treatment, when indicated, and blood-pressure medications (for example, initiation, titration, or addition of agents depending on blood-pressure control, comorbid conditions [e.g., diabetes], and risk for pregnancy)

Detailed Description
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The investigators' data suggest that (1) addressing obesity-related comorbidities (in combination with high BMI/overweight/obesity) and more frequent follow-up are associated with weight-status improvement in overweight (OW) children, (2) that parents rank checking for weight-related health problems as the #1 most important recommended weight-management clinical practice, but that (3) comorbidities are infrequently addressed. Thus, to improve weight status, interventions are needed to improve comorbidity identification, evaluation, and management in primary care. Because the investigators' data suggest that identification of high blood pressure is particularly poor, and that identifying blood-pressure elevations in young children at three separate encounters is complex, they are using existing underutilized data to automate addressing high blood pressure/hypertension in an electronic health record system (EPIC-based), and, if successful, applying the method to other obesity-related comorbidities in primary care. OF NOTE: in developing and pilot-testing this decision support tool, providers wanted access to it for patients without overweight/obesity. Thus, although the trial was borne out of work in weight-management research, the initial trial will focus on all children irrespective of weight status.

Official Title
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Automated Clinical-Decision Support to Improve Hypertension Care Among Overweight Children in Primary Care

Conditions
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Hypertension, Blood Pressure, Overweight and Obesity

Intervention / Treatment
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* Other: clinical decision support


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria for Providers: Pediatric primary care providers practicing at participating clinics that have agreed to have the decision-support system implemented. Exclusion Criteria for Providers: Lack of electronic health record Inclusion Criteria for Children: Measured systolic or diastolic blood pressure >=90th % for age/sex, or >=120 mmHg systolic or >=80 mmHg diastolic (whichever is lower) Exclusion Criteria for Children: Diagnosis/visit for high blood pressure or hypertension in past 2 years Taking anti-hypertensive medication

Ages Eligible for Study
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Minimum Age: 3 Years
Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Intervention Model: Single Group Assignment
Interventional Model Description: Four-month time series trial
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: clinical decision support activated<br>TWO MED ASSIST ALERTS Enter height (when missing) Repeat BP (when high) ONE PROVIDER ALERT BP high & prior BP/BP%s Defines elev. BP, HTN stage 1-2 with button to enter diagnosis Link to tailored ordersets TAILORED ORDERSETS Elevated BP Button to schedule f-up <6 m Button for diet/lifestyle counseling/check-out instructions HTN stage 1 Buttons to order labs/studies pre-checked for stage 1 recs Button for nephrology referral Button to schedule f-up in 1-2 wk/<1 m Button for diet/lifestyle counseling/check-out instructions HTN stage 2 Buttons to order labs/studies for stage 2 Button for nephrology referral (pre-checked) Button to f-up 1 wk Button for diet/lifestyle counseling/check-out instruction | Other: clinical decision support<br>* Decision support system (see description of hypertension decision support system alerts and order sets for details)<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| High blood pressure addressed | Proportion of patients with high blood pressure addressed post decision-support implementation (vs. pre-), defined as proportion of patients with elevated blood pressures or higher who have one or more diagnosis or orders placed to diagnose, evaluate, or manage hypertension. We will compare the slope of the post- vs. pre-implementation periods to examine change in monthly rate of HTN eval/management (indicated elements ordered/patients eligible) related to decision support that are in excess of secular trends in clinical practice. In the post-implementation period, the denominator will be determined by tracking patient-level decision-support fires. In the pre-implementation period, the denominator will be determined by applying the decision-support algorithm to retrospective data in monthly increments to identify patients who would have been eligible. | 4-month time series analysis |