Development and implementation of a mobile device-based pediatric electronic decision support tool as part of a national practice standardization project

Abstract Objective Implementing evidence-based practices requires a multi-faceted approach. Electronic clinical decision support (ECDS) tools may encourage evidence-based practice adoption. However, data regarding the role of mobile ECDS tools in pediatrics is scant. Our objective is to describe the development, distribution, and usage patterns of a smartphone-based ECDS tool within a national practice standardization project. Materials and Methods We developed a smartphone-based ECDS tool for use in the American Academy of Pediatrics, Value in Inpatient Pediatrics Network project entitled “Reducing Excessive Variation in the Infant Sepsis Evaluation (REVISE).” The mobile application (app), PedsGuide, was developed using evidence-based recommendations created by an interdisciplinary panel. App workflow and content were aligned with clinical benchmarks; app interface was adjusted after usability heuristic review. Usage patterns were measured using Google Analytics. Results Overall, 3805 users across the United States downloaded PedsGuide from December 1, 2016, to July 31, 2017, leading to 14 256 use sessions (average 3.75 sessions per user). Users engaged in 60 442 screen views, including 37 424 (61.8%) screen views that displayed content related to the REVISE clinical practice benchmarks, including hospital admission appropriateness (26.8%), length of hospitalization (14.6%), and diagnostic testing recommendations (17.0%). Median user touch depth was 5 [IQR 5]. Discussion We observed rapid dissemination and in-depth engagement with PedsGuide, demonstrating feasibility for using smartphone-based ECDS tools within national practice improvement projects. Conclusions ECDS tools may prove valuable in future national practice standardization initiatives. Work should next focus on developing robust analytics to determine ECDS tools’ impact on medical decision making, clinical practice, and health outcomes.

[1]  S. Sloman,et al.  Base-rate respect: From ecological rationality to dual processes. , 2007, The Behavioral and brain sciences.

[2]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[3]  Steen Andreassen,et al.  Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. , 2006, The Journal of antimicrobial chemotherapy.

[4]  Benjamin Littenberg,et al.  The Effect of the Vermont Diabetes Information System on Inpatient and Emergency Department Use: Results from a Randomized Trial , 2010 .

[5]  M. Gharbi,et al.  Effect of adding a mobile health intervention to a multimodal antimicrobial stewardship programme across three teaching hospitals: an interrupted time series study , 2017, Journal of Antimicrobial Chemotherapy.

[6]  L. Moja,et al.  Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. , 2014, American journal of public health.

[7]  Kevin B. Johnson,et al.  Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial , 2014, Int. J. Medical Informatics.

[8]  Samir S. Shah,et al.  Association of clinical practice guidelines with emergency department management of febrile infants ≤56 days of age. , 2015, Journal of hospital medicine.

[9]  R. Wasserman,et al.  Does Clinical Presentation Explain Practice Variability in the Treatment of Febrile Infants? , 2006, Pediatrics.

[10]  F. Balamuth,et al.  Lumbar Puncture for All Febrile Infants 29-56 Days Old: A Retrospective Cohort Reassessment Study. , 2017, The Journal of pediatrics.

[11]  Christoph U. Lehmann,et al.  Eliminating Health Care Disparities With Mandatory Clinical Decision Support: The Venous Thromboembolism (VTE) Example , 2015, Medical care.

[12]  David W. Bates,et al.  Standard practices for computerized clinical decision support in community hospitals: a national survey , 2012, J. Am. Medical Informatics Assoc..

[13]  R. Gupta,et al.  Initial Experience of the American Society of Regional Anesthesia and Pain Medicine Coags Regional Smartphone Application: A Novel Report of Global Distribution and Clinical Usage of an Electronic Decision Support Tool to Enhance Guideline Use , 2015, Regional Anesthesia & Pain Medicine.

[14]  Aziz Sheikh,et al.  Organizational issues in the implementation and adoption of health information technology innovations: An interpretative review , 2013, Int. J. Medical Informatics.

[15]  M. Garber,et al.  A Multicenter Collaborative to Reduce Unnecessary Care in Inpatient Bronchiolitis , 2016, Pediatrics.

[16]  P. Heidenreich,et al.  Clinical Reminders Attached to Echocardiography Reports of Patients With Reduced Left Ventricular Ejection Fraction Increase Use of &bgr;-Blockers: A Randomized Trial , 2007, Circulation.

[17]  A. Wey,et al.  Implementation of an Electronic Clinical Decision Support Tool for Pediatric Appendicitis Within a Hospital Network , 2018, Pediatric emergency care.

[18]  Jakob Nielsen,et al.  The usability engineering life cycle , 1992, Computer.

[19]  Laura D. Scherer,et al.  Blocks, Ovals, or People? Icon Type Affects Risk Perceptions and Recall of Pictographs , 2014, Medical decision making : an international journal of the Society for Medical Decision Making.

[20]  R. McCulloh,et al.  Facing the ongoing challenge of the febrile young infant , 2017, Critical Care.

[21]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[22]  Katherine M. Atkinson,et al.  Barriers and facilitators to the use of an immunization application: a qualitative study supplemented with Google Analytics data , 2017, Journal of public health.

[23]  Louis M Bell,et al.  Electronic Health Record–Based Decision Support to Improve Asthma Care: A Cluster-Randomized Trial , 2010, Pediatrics.

[24]  G. Pollara,et al.  Attitudes and Behaviours to Antimicrobial Prescribing following Introduction of a Smartphone App , 2016, PloS one.

[25]  U. Shaikh,et al.  Impact of Electronic Health Record Clinical Decision Support on the Management of Pediatric Obesity , 2015, American journal of medical quality : the official journal of the American College of Medical Quality.

[26]  K. Parikh,et al.  A Multicenter Collaborative to Improve Care of Community Acquired Pneumonia in Hospitalized Children , 2017, Pediatrics.

[27]  G. Putzer,et al.  The effects of innovation factors on smartphone adoption among nurses in community hospitals. , 2010, Perspectives in health information management.

[28]  The use of smartphones in hospitals. , 2012, Clinical nurse specialist CNS.

[29]  Bruce G. Link,et al.  Association between an Internet-Based Measure of Area Racism and Black Mortality , 2015, PloS one.

[30]  M. Siegrist,et al.  Does Iconicity in Pictographs Matter? The Influence of Iconicity and Numeracy on Information Processing, Decision Making, and Liking in an Eye‐Tracking Study , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[31]  A. Tricco,et al.  Diagnosis and management of febrile infants (0-3 months). , 2012, Evidence report/technology assessment.

[32]  Gavin J Putzer,et al.  Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. , 2012, Perspectives in health information management.

[33]  M. Marks,et al.  Decreasing unnecessary utilization in acute bronchiolitis care: results from the value in inpatient pediatrics network. , 2013, Journal of hospital medicine.

[34]  C. Anandan,et al.  The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview , 2011, PLoS medicine.

[35]  Samir S. Shah,et al.  Variation in Care of the Febrile Young Infant <90 Days in US Pediatric Emergency Departments , 2014, Pediatrics.

[36]  Dean F Sittig,et al.  Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. , 2009, Journal of healthcare information management : JHIM.

[37]  Ferdinand T. Velasco,et al.  Improving Outcomes with Clinical Decision Support: An Implementer's Guide , 2012 .

[38]  Janette R. Hill,et al.  Real-time use of the iPad by third-year medical students for clinical decision support and learning: a mixed methods study , 2014, Journal of community hospital internal medicine perspectives.

[39]  V. Master,et al.  Numeracy among trainees: are we preparing physicians for evidence-based medicine? , 2014, Journal of surgical education.

[40]  Gunilla C. Nilsson,et al.  The Use of the Personal Digital Assistant (PDA) Among Personnel and Students in Health Care: A Review , 2008, Journal of medical Internet research.

[41]  Development of a Web-Based Decision Support Tool to Operationalize and Optimize Management of Hyperbilirubinemia in Preterm Infants. , 2016, Clinics in perinatology.

[42]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[43]  Benjamin Littenberg,et al.  The effect of the Vermont Diabetes Information System on inpatient and emergency room use: results from a randomized trial. , 2010, Health outcomes research in medicine.