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      The Use and Effects of Electronic Health Tools for Patient Self-Monitoring and Reporting of Outcomes Following Medication Use: Systematic Review

      review-article
      , BSc, MSc 1 , , MPharm, PhD 2 , , BSc (Hons), PharmD 2 , 3 , , BSc, MPH 2 , , BSc, PharmD, ACPR 4 , , BSc, PharmD 5 , , BA, MHE 6 , , BSc, MSc, PhD 4 , , BSc, MSc, PharmD 1 , 2 ,
      (Reviewer), (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      eHealth, mHealth, electronic health record, telemedicine, self-report, patient portals, patient-centered care, drug monitoring, adverse effects

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          Abstract

          Background

          Electronic health (eHealth) tools are becoming increasingly popular for helping patients’ self-manage chronic conditions. Little research, however, has examined the effect of patients using eHealth tools to self-report their medication management and use. Similarly, there is little evidence showing how eHealth tools might prompt patients and health care providers to make appropriate changes to medication use.

          Objective

          The objective of this systematic review was to determine the impact of patients’ use of eHealth tools on self-reporting adverse effects and symptoms that promote changes to medication use. Related secondary outcomes were also evaluated.

          Methods

          MEDLINE, EMBASE, and CINAHL were searched from January 1, 2000, to April 25, 2018. Reference lists of relevant systematic reviews and included articles from the literature search were also screened to identify relevant studies. Title, abstract, and full-text review as well as data extraction and risk of bias assessment were performed independently by 2 reviewers. Due to high heterogeneity, results were not meta-analyzed and instead presented as a narrative synthesis.

          Results

          A total of 14 studies, including 13 randomized controlled trials (RCTs) and 1 open-label intervention, were included, from which 11 unique eHealth tools were identified. In addition, 14 RCTs found statistically significant increases in positive medication changes as a result of using eHealth tools, as did the single open-label study. Moreover, 8 RCTs found improvement in patient symptoms following eHealth tool use, especially in adolescent asthma patients. Furthermore, 3 RCTs showed that eHealth tools might improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of eHealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behavior, health service utilization, adverse effects, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy.

          Conclusions

          Evidence suggests that the use of eHealth tools may improve patient symptoms and lead to medication changes. Patients generally found eHealth tools useful in improving communication with health care providers. Moreover, health-related outcomes among frequent eHealth tool users improved in comparison with individuals who did not use eHealth tools frequently. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how eHealth tools can be used to effectively manage use of medications to improve medication management and patient outcomes.

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          Most cited references37

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          Concurrent and predictive validity of a self-reported measure of medication adherence.

          Adherence to the medical regimen continues to rank as a major clinical problem in the management of patients with essential hypertension, as in other conditions treated with drugs and life-style modification. This article reviews the psychometric properties and tests the concurrent and predictive validity of a structured four-item self-reported adherence measure (alpha reliability = 0.61), which can be easily integrated into the medical visit. Items in the scale address barriers to medication-taking and permit the health care provider to reinforce positive adherence behaviors. Data on patient adherence to the medical regimen were collected at the end of a formalized 18-month educational program. Blood pressure measurements were recorded throughout a 3-year follow-up period. Results showed the scale to demonstrate both concurrent and predictive validity with regard to blood pressure control at 2 years and 5 years, respectively. Seventy-five percent of the patients who scored high on the four-item scale at year 2 had their blood pressure under adequate control at year 5, compared with 47% under control at year 5 for those patients scoring low (P less than 0.01).
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            Development and testing of a short form of the patient activation measure.

            The Patient Activation Measure (PAM) is a 22-item measure that assesses patient knowledge, skill, and confidence for self-management. The measure was developed using Rasch analyses and is an interval level, unidimensional, Guttman-like measure. The current analysis is aimed at reducing the number of items in the measure while maintaining adequate precision. We relied on an iterative use of Rasch analysis to identify items that could be eliminated without loss of significant precision and reliability. With each item deletion, the item scale locations were recalibrated and the person reliability evaluated to check if and how much of a decline in precision of measurement resulted from the deletion of the item. The data used in the analysis were the same data used in the development of the original 22-item measure. These data were collected in 2003 via a telephone survey of 1,515 randomly selected adults. Principal Findings. The analysis yielded a 13-item measure that has psychometric properties similar to the original 22-item version. The scores for the 13-item measure range in value from 38.6 to 53.0 (on a theoretical 0-100 point scale). The range of values is essentially unchanged from the original 22-item version. Subgroup analysis suggests that there is a slight loss of precision with some subgroups. The results of the analysis indicate that the shortened 13-item version is both reliable and valid.
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              Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption.

              Recently there has been a remarkable upsurge in activity surrounding the adoption of personal health record (PHR) systems for patients and consumers. The biomedical literature does not yet adequately describe the potential capabilities and utility of PHR systems. In addition, the lack of a proven business case for widespread deployment hinders PHR adoption. In a 2005 working symposium, the American Medical Informatics Association's College of Medical Informatics discussed the issues surrounding personal health record systems and developed recommendations for PHR-promoting activities. Personal health record systems are more than just static repositories for patient data; they combine data, knowledge, and software tools, which help patients to become active participants in their own care. When PHRs are integrated with electronic health record systems, they provide greater benefits than would stand-alone systems for consumers. This paper summarizes the College Symposium discussions on PHR systems and provides definitions, system characteristics, technical architectures, benefits, barriers to adoption, and strategies for increasing adoption.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                December 2018
                18 December 2018
                : 20
                : 12
                : e294
                Affiliations
                [1 ] Department of Family Medicine McMaster University Hamilton, ON Canada
                [2 ] Leslie Dan Faculty of Pharmacy University of Toronto Toronto, ON Canada
                [3 ] School of Pharmacy University of Waterloo Waterloo, ON Canada
                [4 ] Department of Health Research Methods, Evidence, and Impact McMaster University Hamilton, ON Canada
                [5 ] Pharmacy Department Sunnybrook Health Sciences Toronto, ON Canada
                [6 ] Michael G. DeGroote National Pain Centre McMaster University Hamilton, ON Canada
                Author notes
                Corresponding Author: Lisa Dolovich lisa.dolovich@ 123456utoronto.ca
                Author information
                http://orcid.org/0000-0002-7638-8190
                http://orcid.org/0000-0002-4073-4346
                http://orcid.org/0000-0002-6111-7304
                http://orcid.org/0000-0002-4864-6359
                http://orcid.org/0000-0002-5821-0516
                http://orcid.org/0000-0002-3634-3019
                http://orcid.org/0000-0001-9929-2485
                http://orcid.org/0000-0003-0355-9734
                http://orcid.org/0000-0002-0061-6783
                Article
                v20i12e294
                10.2196/jmir.9284
                6315271
                30563822
                142fe151-b061-45a2-9945-9c68cbdefca2
                ©Karla Lancaster, Aseel Abuzour, Manmeet Khaira, Annalise Mathers, April Chan, Vivian Bui, Annie Lok, Lehana Thabane, Lisa Dolovich. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.12.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 2 November 2017
                : 15 March 2018
                : 18 June 2018
                : 14 September 2018
                Categories
                Review
                Review

                Medicine
                ehealth,mhealth,electronic health record,telemedicine,self-report,patient portals,patient-centered care,drug monitoring,adverse effects

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