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Pediatric primary care provider personal health records (PHRs) enterprise utilization and practice
Dissertation   Open access

Pediatric primary care provider personal health records (PHRs) enterprise utilization and practice

Kisha Hortman Hawthorne
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2018
DOI:
https://doi.org/10.17918/xsp1-wv19
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Abstract

Information science Health services administration Medical records--Data processing Primary care (Medicine) Information Technology Pediatrics
Background Personal Health Records (PHRs) empirical research has overwhelmingly focused on patient and family adoption across care setting, severity of illness, dealing with privacy concerns and overall facilitators and barriers to adoption. PHRs Technology Acceptance is defined as three types of PHR usage.1 These usage types are behavioral intent to use, actual use and proficient use. Providers are the focus of this research, which are less researched for PHR barriers and facilitators, acceptance and adoption. Pediatric primary care providers (PCP) are key in terms of the health and well-being of their patients and families, and results in, the often close relationship developed with parents and children during their formative years and the heavily surrogate population that exists in the pediatric population. Wolcott, et al. determines that a supportive provider relationship has been associated with increased patient compliance, decreased pain, and shortened recovery periods.2 Objectives The objectives of this dissertation study were to investigate the existing literature regarding personal health care records and provider use, utilization and practice. Another objective was to evaluate a newly created PHR Organizational Pediatric PCP PHR Acceptance survey and model (POP3) to identify pathways and mechanisms responsible for pediatric PCP PHR behavioral intent to use and actual use. Finally, a targeted network intervention was investigated to determine what, if any, impact it had on increasing pediatric PCP PHR actual use and utilization and practice. Methods The POP3 survey and model was offered to pediatric providers practicing at 30 urban and suburban primary care practices affiliated with an academic medical center. The POP3 survey and model was based on existing technology acceptance and adoption theory. The survey scored external variables impact on both predictor and outcome variables based on a Likert scale. Demographic variables included age, gender, years of healthcare experience including residency and provider type. Predictor variables were individual, technology, patient and family experience, network and organizational level. The median scores were utilized to analyze any pathways and impact on perceived ease of use (PEOU), perceived benefit of use (PBOU), behavioral intent to use (BITU), and actual use (AU). A network intervention was used to further validate impacts of network level interventions on actual and proficient use. Results The POP3 survey response rate was 45%, with 94 providers attempting to complete or completing the survey. Proficient use (UP) had the highest self-reported median score of all outcome variables. Age and years of experience had a statistically significant impact on BITU. The POP3 model resulted in positive impacts of the four of five external variables on both PEOU and PBOU. The highest results were for PHR patient family experience on PEOU and individual PHR technology use, ([beta]= 0.6, P< 0.001), on PBOU. PEOU has an impact, ([beta]= 0.5, P< 0.001), on PBOU which has been demonstrated in past empirical studies. BITU ([beta]= 0.6, P< 0.001), of all predictor variables had the highest impact of all, meaning if the provider intended to use the PHR, there was a higher impact or potential increase on actual use (AU) in comparison to other predictor variables such as PEOU, PBOU and BITU. The self-scheduling network intervention had a 2.0% absolute and 4.0% relative increase in PHR actual provider use in comparison with the control group. For providers deemed as proficient users, a 38% increase in PHR actual use in the intervention group. Finally, PCP providers identified both patient family and provider PHR interventions that could increase PHR use. Conclusion The POP3 model and the impact of a network intervention were both positively validated by this dissertation research. Individual and network level data were integrated to analysis provider PHR use. Additionally, both objective and subjective data are analyzed in this study. This study allows for comparison of both survey and PHR log data to determine provider PHR actual and proficient use. Future research opportunities include updated the POP3 survey and model to include top interventions identified by participants. Further testing of these new interventions, after policy development is completed and implements by care network and organizational leadership. The research should also be extended to include other ambulatory specialty care network locations and providers as well as including other PCP network office roles such as nurses and patient access staff. A further evaluation of proficient usage post intervention should be conducted with the new interventions identified. Finally, a review and analysis of the implications and effects of both patients and families and providers interventions on provider PHR use.

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