The effect of an electronic health record-based tool on abnormal pediatric blood pressure recognition

Abstract

Background Recognition of high blood pressure (BP) in children is poor, partly due to the need to compute age-sex-height referenced percentiles. This study examined the change in abnormal BP recognition before versus after the introduction of an electronic health record (EHR) app designed to calculate BP percentiles with a training lecture. Methods and Results Clinical data were extracted on all ambulatory, non-urgent encounters for children 3–18 years old seen in primary care, endocrinology, cardiology, or nephrology clinics at an urban, academic hospital in the year before and the year after app introduction. Outpatients with at least 1 BP above the age-gender-height referenced 90th percentile were included. Abnormal BP recognition was defined as a BP related ICD-9 code, referral to nephrology or cardiology, an echocardiogram or renal ultrasound to evaluate BP concern, or a follow-up primary care visit for BP monitoring. Multivariable adjusted logistic regression compared odds of recognition before and after app introduction. Of 78,768 clinical encounters, 3,521 had abnormal BP in the pre and 3,358 in the post-app period. App use occurred in 13% of elevated BP visits. Overall, abnormal BP was recognized in 4.9% pre-app period visits and 7.1% of visits post-app (p<0.0001). Recognition was significantly higher when the app was actually used (adjusted OR 3.17 95% CI 2.29–4.41, p<0.001). Without app use recognition was not different. Conclusions BP app advent modestly increased abnormal BP recognition in the entire cohort, but actual app use was associated with significantly higher recognition. Predictors of abnormal BP recognition deserve further scrutiny.

The Use of Data Collected From mHealth Apps to Inform Evidence‐Based Quality Improvement: An Integrative Review

“…Studies varied greatly in their aims, methodologies, and sample sizes. A variety of study designs were selected for analysis, including four systematic reviews (Holmen et al., ; Kitsiou, Pare, Jaana, & Gerber, ; Lee et al., ; Peiris, Praveen, Johnson, & Mogulluru, ), four randomized controlled trials (Armstrong, Coyte, Brown, Beber, & Semple, ; Cingi et al., ; Lakshminarayana et al., ; Wolf et al., ), one nonrandomized controlled trial (Sundberg et al., ), three retrospective cohort studies (Dickson, Sumathipala, & Reeves, ; Khanna, Sambandam, Gul, & Mounasamy, ; Twichell et al., ), six pilot studies (Foo et al., ; Gunter et al., ; Jakel et al., ; Macpherson et al., ; Patel, Siegler, Stromberg, Ravitz, & Hanson, ; Semple, Sharpe, Murnaghan, Theodoropoulos, & Metcalfe, ), and one case report (Gernart et al., ).…”

“…As well as the ability to collect an assortment of data types, the apps reviewed were, at times, able to collect large amounts of data. For example, a study on communication (Patel et al., ) reviewed 708, 456 text messages, while another looked at over 6,800 abnormal blood pressure measurements (Twichell et al., ). There was also considerable variance in study participants, ranging from eight orthopedic residents in a study about the use of smartphone technology in India (Gunter et al., ) to over 1,450 in a communication study (Patel et al., ).…”

“…Data collected through apps were used both in real time to deliver health care, inform healthcare decision‐making, and create self‐awareness (Armstrong et al., ; Cingi et al., ; Dickson et al., ; Foo et al., ; Gunter et al., ; Jakel et al., ; Khanna et al., ; Macpherson et al., ; Semple et al., ; Sundberg et al., ; Twichell et al., ), and retrospectively to inform healthcare management (Gernart et al., ; Lakshminarayana et al., ; Semple et al., ; Wolf et al., ), communication strategies (Khanna et al., ; Patel et al., ), and healthcare delivery changes (Foo et al., ).…”

“…While not the main focus of the apps, a number of quality improvements and outcomes were reported in the studies reviewed. These included improvements to clinical outcomes (Cingi et al., ), disease control and management (Kitsiou et al., ; Lakshminarayana et al., ), quality of life (Cingi et al., ; Sundberg et al., ), time to treatment (Dickson et al., ; Twichell et al., ), communication (Foo et al., ; Khanna et al., ; Macpherson et al., ), awareness of patient information (Khanna et al., ; Lee et al., ), symptom burden (Sundberg et al., ), emotional functioning (Sundberg et al., ), medication adherence (Lakshminarayana et al., ), time efficiency (Foo et al., ; Khanna et al., ; Lee et al., ; Patel et al., ; Semple et al., ), disease management (Holmen et al., ), follow‐up time (Armstrong et al., ; Semple et al., ), and patient management (Foo et al., ; Lakshminarayana et al., ). Of the 19 studies reviewed, only one article (Foo et al., ) made explicit links outlining that data collected from the app were used to inform quality improvement.…”

“…Although the purpose of the apps was improvement focused, it was the data collected by the mHealth apps that informed these changes. The data from these apps were used for a variety of purposes resulting in quality improvement (e.g., faster identification of changes in patient condition; Semple et al., ; Twichell et al., ), improving communication processes (Foo et al., ; Khanna et al., ; Patel et al., ), enhancing understanding of quality of life (Cingi et al., ; Lakshminarayana et al., ; Sundberg et al., ), increasing patient compliance (Cingi et al., ; Gernart et al., ; Kitsiou et al., ; Wolf et al., ), and improving access to and satisfaction with health care (Dickson et al., ; Lakshminarayana et al., ).…”