Dissertations and Theses (Open Access)
Graduation Date
Fall 2022
Degree Name
Doctor of Philosophy (PhD)
School Name
McWilliams School of Biomedical Informatics at UTHealth Houston
Advisory Committee
Tiffany Champagne-Langabeer, PHD, MBA, RDN
Abstract
Per the American Medical Informatics Association (AMIA) and the International Medical Informatics Association (IMIA), there is a lack of frameworks to guide research on the unintended consequences of health information technology (HIT) policy. To address this need, this research focused on the mandated use of the Texas prescription drug monitoring program (PDMP). The research’s aims were to determine the effects on, and experiences of prescribers and pharmacists as they complied with mandated PDMP use through (1) a systematic literature review, (2) semistructured interviews of prescribers and pharmacists analyzed using a grounded theory methodology and, by combining the results of the first two aims, (3) the proposal of a conceptual model of the unintended consequences of mandated PDMP use. This research is a step towards building theory on the unintended consequences of HIT policy in general, and guide future research in this area. Aim 1’s systematic literature review included 65 peer-reviewed articles published between 2016 - 2021. It revealed that PDMP use, whether voluntary or mandated, was associated with decreases in the number of opioid prescriptions overall. However, the review was not clear on whether clinicians found PDMP data helpful, if PDMP use resulted in less risky prescriptions overall, nor whether PDMP users wrote less risky vi prescriptions. It also revealed potential barriers to patients obtaining clinically appropriate controlled substance prescriptions. For Aim 2, 30 semi-structured interviews of clinicians in the State of Texas were conducted between April - June 2022; they were analyzed using a grounded theory methodology. Interviewees worked in a variety of settings including emergency medicine, neurology, pediatrics, and psychiatry. Interviews revealed that clinicians used the PDMP because of a mix of fear of liability, a desire to deliver good patient care, and concern about the opioid epidemic. Interviewees also talked about how the PDMP was useful for communication with patients and with other providers, and how - since clinicians could access the same PDMP data irrespective of healthcare setting - the PDMP served as a unifying medical record for patients. However, interviews also divulged increased mistrust between prescribers and pharmacists as each had corresponding responsibility for identifying potential instances of polysubstance misuse and diversion. These Aim 1 and 2 findings were combined to yield a conceptual model in which pharmacists, prescribers, and patients enjoyed better communication, but also experienced greater mistrust, leading to potential friction in the interactions between pharmacists and prescribers, and patients experiencing barriers to obtaining clinically appropriate controlled substance prescriptions. This conceptual model pointed to well meaning HIT policy: (1) potentially resulting in both helpful and harmful unintended consequences, (2) impacting different clinicians in different ways and to different extents - suggesting a need to parse out the effect of HIT policy on different clinician groups, and vii (3) affecting the way that clinicians interact with each other and with patients - pointing out the importance of ascertaining the effect of HIT policy on stakeholders and their interactions. The research also revealed the need for more action and research in the following areas: (1) How can HIT use be facilitated? (2) How does HIT affect clinical decision-making? (3) How should HIT be used?
Recommended Citation
Smith, Nina, "Proposing a Conceptual Model of the Unintended Consequences of Health Information Technology Policy" (2022). Dissertations and Theses (Open Access). 100.
https://digitalcommons.library.tmc.edu/uthshis_dissertations/100
Keywords
Health information technology (HIT), Prescription Drug Monitoring Programs (PDMP), Health policy, Unintended consequences, Opioid prescribing, Grounded theory, Clinical decision-making, Pharmacists and prescribers, Texas PMP AWARxE, Opioid epidemic