Kenrick Cato

What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions

Background Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the body of evidence related to economic evaluations of mHealth interventions is assessed and summarized. Methods Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed. Results Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62–100, STD 14.18) and the top quartile reported 91.3–100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case. Conclusions Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research.

Developing Clinical Decision Support within a Commercial Electronic Health Record System to Improve Antimicrobial Prescribing in the Neonatal ICU

OBJECTIVE To develop and implement a clinical decision support (CDS) tool to improve antibiotic prescribing in neonatal intensive care units (NICUs) and to evaluate user acceptance of the CDS tool. METHODS Following sociotechnical analysis of NICU prescribing processes, a CDS tool for empiric and targeted antimicrobial therapy for healthcare-associated infections (HAIs) was developed and incorporated into a commercial electronic health record (EHR) in two NICUs. User logs were reviewed and NICU prescribers were surveyed for their perceptions of the CDS tool. RESULTS The CDS tool aggregated selected laboratory results, including culture results, to make treatment recommendations for common clinical scenarios. From July 2010 to May 2012, 1,303 CDS activations for 452 patients occurred representing 22% of patients prescribed antibiotics during this period. While NICU clinicians viewed two culture results per tool activation, prescribing recommendations were viewed during only 15% of activations. Most (63%) survey respondents were aware of the CDS tool, but fewer (37%) used it during their most recent NICU rotation. Respondents considered the most useful features to be summarized culture results (43%) and antibiotic recommendations (48%). DISCUSSION During the study period, the CDS tool functionality was hindered by EHR upgrades, implementation of a new laboratory information system, and changes to antimicrobial testing methodologies. Loss of functionality may have reduced viewing antibiotic recommendations. In contrast, viewing culture results was frequently performed, likely because this feature was perceived as useful and functionality was preserved. CONCLUSION To improve CDS tool visibility and usefulness, we recommend early user and information technology team involvement which would facilitate use and mitigate implementation challenges.

Response to a Mobile Health Decision Support System for Screening and Management of Tobacco Use

PURPOSE/OBJECTIVES To describe the predictors of nurse actions in response to a mobile health decision-support system (mHealth DSS) for guideline-based screening and management of tobacco use. DESIGN Observational design focused on an experimental arm of a randomized, controlled trial. SETTING Acute and ambulatory care settings in the New York City metropolitan area. SAMPLE 14,115 patient encounters in which 185 RNs enrolled in advanced practice nurse (APN) training were prompted by an mHealth DSS to screen for tobacco use and select guideline-based treatment recommendations. METHODS Data were entered and stored during nurse documentation in the mHealth DSS and subsequently stored in the study database where they were retrieved for analysis using descriptive statistics and logistic regressions. MAIN RESEARCH VARIABLES Predictor variables included patient gender, patient race or ethnicity, patient payer source, APN specialty, and predominant payer source in clinical site. Dependent variables included the number of patient encounters in which the nurse screened for tobacco use, provided smoking cessation teaching and counseling, or referred patients for smoking cessation for patients who indicated a willingness to quit. FINDINGS Screening was more likely to occur in encounters where patients were female, African American, and received care from a nurse in the adult nurse practitioner specialty or in a clinical site in which the predominant payer source was Medicare, Medicaid, or State Childrens Health Insurance Program. In encounters where the patient payer source was other, nurses were less likely to provide tobacco cessation teaching and counseling. CONCLUSIONS mHealth DSS has the potential to affect nurse provision of guideline-based care. However, patient, nurse, and setting factors influence nurse actions in response to an mHealth DSS for tobacco cessation. IMPLICATIONS FOR NURSING The combination of a reminder to screen and integration of guideline-based recommendations into the mHealth DSS may reduce racial or ethnic disparities to screening, as well as clinician barriers related to time, training, and familiarity with resources.

Data elements and validation methods used for electronic surveillance of health care-associated infections: A systematic review

BACKGROUND We describe the primary data sources, data elements, and validation methods currently used in electronic surveillance systems (ESS) for identification and surveillance of health care-associated infections (HAIs), and compares these data elements and validation methods with recommended standards. METHODS Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a PubMed and manual search was conducted to identify research articles describing ESS for identification and surveillance of HAIs published January 1, 2009-August 31, 2014. Selected articles were evaluated to determine what data elements and validation methods were included. RESULTS Among the 509 articles identified in the original literature search, 30 met the inclusion criteria. Whereas the majority of studies (83%) used recommended data sources and validated the numerator (80%), only 10% of studies performed external and internal validation. In addition, there was variation in the ESS data formats used. CONCLUSIONS Our findings suggest that the majority of ESS for HAI surveillance use standard definitions, but the lack of widespread internal data, denominator, and external validation in these systems reduces the reliability of their findings. Additionally, advanced programming skills are required to create, implement, and maintain these systems and to reduce the variability in data formats.

Did I Tell You That? Ethical Issues Related to Using Computational Methods to Discover Non-Disclosed Patient Characteristics

Widespread availability of electronic health records coupled with sophisticated statistical methods offer great potential for a variety of applications for health and disease surveillance, developing predictive models and advancing decision support for clinicians. However, use of “big data” mining and discovery techniques has also raised ethical issues such as how to balance privacy and autonomy with the wider public benefits of data sharing. Furthermore, electronic data are being increasingly used to identify individual characteristics, which can be useful for clinical prediction and management, but were not previously disclosed to a clinician. This process in computer parlance is called electronic phenotyping, and has a number of ethical implications. Using the Belmont Report’s principles of respect for persons, beneficence, and justice as a framework, we examined the ethical issues posed by electronic phenotyping. Ethical issues identified include the ability of the patient to consent for the use of their information, the ability to suppress pediatric information, ensuring that the potential benefits justify the risks of harm to patients, and acknowledging that the clinician’s biases or stereotypes, conscious or unintended, may become a factor in the therapeutic interaction. We illustrate these issues with two vignettes, using the person characteristic of gender minority status (i.e., transgender identity) and health history characteristic of substance abuse. Data mining has the potential to uncover patient characteristics previously obscured, which can provide clinicians with beneficial clinical information. Hence, ethical guidelines must be updated to ensure that electronic phenotyping supports the principles of respect for persons, beneficence, and justice.