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      Health Information Technology and Doctor Shopping: A Systematic Review

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          Abstract

          Doctor shopping is the practice of visiting multiple physicians to obtain multiple prescriptions. Health information technology (HIT) allows healthcare providers and patients to leverage records or shared information to improve effective care. Our research objective was to determine how HIT is being leveraged to control for doctor shopping. We analyzed articles that covered a 10-year time period from four databases and reported using preferred reporting items for systematic reviews and meta-analysis (PRISMA). We compared intervention, study design, and bias, in addition to showing intervention interactions with facilitators, barriers, and medical outcomes. From 42 articles published from six countries, we identified seven interventions, five facilitator themes with two individual observations, three barrier themes with six individual observations, and two medical outcome themes with four individual observations. Multiple HIT mechanisms exist to control for doctor shopping. Some are associated with a decrease in overdose mortality, but access is not universal or compulsory, and data sharing is sporadic. Because shoppers travel hundreds of miles in pursuit of prescription drugs, data sharing should be an imperative. Research supports leveraging HIT to control doctor shopping, yet without robust data sharing agreements, the efforts of the system are limited to the efforts of the entity with the least number of barriers to their goal. Shoppers will seek out and exploit that organization that does not require participation or checking of prescription drug monitoring programs (PDMP), and the research shows that they will drive great distances to exploit this weakest link.

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          Patient empowerment, patient participation and patient-centeredness in hospital care: A concept analysis based on a literature review.

          The concepts of patient empowerment, patient participation and patient-centeredness have been introduced as part of the trend towards a more participatory health care and have largely been used interchangeably. Although these concepts have been discussed for a number of years, their exact meaning in hospital care remains somewhat unclear. This absence of theoretical and conceptual clarity has led to (1) poor understanding and communication among researchers, health practitioners and policy makers and (2) problems in measurement and comparison between studies across different hospitals.
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            Association Between Prescription Drug Monitoring Programs and Nonfatal and Fatal Drug Overdoses

            Prescription drug monitoring programs (PDMPs) are a key component of the president's Prescription Drug Abuse Prevention Plan to prevent opioid overdoses in the United States.
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              Prescription drug monitoring programs, nonmedical use of prescription drugs, and heroin use: Evidence from the National Survey of Drug Use and Health.

              In the United States, nonmedical prescription opioid use is a major public health concern. Various policy initiatives have been undertaken to tackle this crisis, including state prescription drug monitoring programs (PDMPs). This study uses the 2004-2014 National Survey of Drug Use and Health (NSDUH) and exploits state-level variation in the timing of PDMP implementation and PDMP characteristics to investigate whether PDMPs are associated with a reduction in prescription opioid misuse or whether they have the unintended consequence of increasing heroin use. In addition, the study examines the impact of PDMPs on the availability of opioids from various sources. The study finds no effect of PDMP status on various measures of nonmedical prescription opioid use (abuse, dependence, and initiation), but finds evidence of a reduction in the number of days of opioid misuse in the past year. The study also finds that implementation of PDMP was not associated with an increase in heroin use or initiation, but was associated with an increase in number of days of heroin use in the past year. Findings also suggest that PDMPs were associated with a significant decline in doctor shopping among individuals without increasing reliance on illegal sources (e.g., drug dealers, stealing, etc.) or social sources (friends or relatives) as a means of obtaining opioids. The President's FY2017 budget proposed the allocation of $1.1 billion in an effort to reduce prescription drug misuse, and highlighted the use of PDMPs as a policy tool. This study documents evidence that PDMPs might be having measurable impact.
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                Author and article information

                Journal
                Healthcare (Basel)
                Healthcare (Basel)
                healthcare
                Healthcare
                MDPI
                2227-9032
                28 August 2020
                September 2020
                : 8
                : 3
                : 306
                Affiliations
                School of Health Administration, Texas State University, San Marcos, TX 78666, USA; b_k118@ 123456txstate.edu (B.K.); skb133@ 123456txstate.edu (S.B.); g_g251@ 123456txstate.edu (G.G.); kpc16@ 123456txstate.edu (K.C.)
                Author notes
                Author information
                https://orcid.org/0000-0002-7636-1086
                https://orcid.org/0000-0001-9089-1369
                Article
                healthcare-08-00306
                10.3390/healthcare8030306
                7551569
                32872211
                f28d5e4d-985a-43ce-b87a-84689d022ab1
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 July 2020
                : 26 August 2020
                Categories
                Review

                prescription drug monitoring program,pharma cloud,doctor shopping,pdmp

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