scholarly journals Cough and cold medicine prescription rates can be significantly reduced by active intervention

2021 ◽  
Author(s):  
Péter Csonka ◽  
Paula Heikkilä ◽  
Sonja Koskela ◽  
Sauli Palmu ◽  
Noora Lajunen ◽  
...  
Keyword(s):  
Real Time ◽  
General Practitioners ◽  
Healthcare Service ◽  
Guideline Implementation ◽  
Intervention Programme ◽  
Record System ◽  
Service Company ◽  
Medicine Prescription ◽  
Prescription Rates ◽  
Electronic Health

AbstractOur aim was to construct and test an intervention programme to eradicate cough and cold medicine (CCM) prescriptions for children treated in a nationwide healthcare service company. The study was carried out in the largest private healthcare service company in Finland with a centralised electronic health record system allowing for real-time, doctor-specific practice monitoring. The step-by-step intervention consisted of company-level dissemination of educational materials to doctors and families, educational staff meetings, continuous monitoring of prescriptions, and targeted feedback. Outreach visits were held in noncompliant units. Finally, those physicians who most often prescribed CCM were directly contacted. During the intervention period (2017–2020), there were more than one million paediatric visits. Prescriptions of CCMs to children were completely eradicated in 41% of units and the total number of CCM prescriptions decreased from 6738 to 744 (89%). During the fourth intervention year, CCMs containing opioid derivatives were prescribed for only 0.2% of children aged < 2 years. The decrease in prescriptions was greatest in general practitioners (5.2 to 1.1%). In paediatricians, the prescription rates decreased from 1.5 to 0.2%. The annual costs of CCMs decreased from €183,996 to €18,899 (89.7%). For the intervention, the developers used 343 h and the attended doctors used 684 h of work time during the 4-year intervention. The costs used for developing, implementing, reporting, evaluating, communicating, and data managing formed approximately 11% of total intervention costs.Conclusion: The study showed that a nationwide systematic intervention to change cough medicine prescription practices is feasible and requires only modest financial investments. What is Known:• Cough and cold medicines (CCM) are not effective or safe, especially for children aged 6 years.• Although the use of CCMs has been declining, caregivers continue to administer CCMs to children, and some physicians still prescribe them even for preschool children. What is New:• A nationwide systematic intervention can significantly and cost effectively change CCM prescription habits of paediatricians, general practitioners, and other specialists.• Electronic health records provide additional tools for operative guideline implementation and real-time quality monitoring, including recommendations of useless or harmful treatments.

scholarly journals Real-time clinician text feeds from electronic health records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James T. H. Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Disease Outbreaks ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

scholarly journals Real-time clinician text feeds from electronic health records

2020 ◽  
Author(s):  
James Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Free Text ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks1, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet 2–4. We describe an approach using real-time aggregation of keywords and phrases of free text from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 2 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can be deployed at multiple organisational scales.

Why Do Physicians Reject the Electronic Health Record: A Theoretical Perspective? (Preprint)

2018 ◽  
Author(s):  
Weam Alfayez ◽  
Arwa Alumran ◽  
Dr Saja A. Al-Rayes
Keyword(s):  
Information Technology ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Health Record ◽  
Record System ◽  
Factors Influencing ◽  
Tam Model ◽  
Electronic Health ◽  
Acceptance Model

BACKGROUND Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, and effective adoption. OBJECTIVE The main aim of this paper is to review the different theories/ models that can help in understanding information technology/system acceptance and use, and to choose the most appropriate theoretical framework that could be applied to understand the factors influencing physicians’ use of the Electronic Health Record system (EHR) at King Fahd Military Medical Complex (KFMMC) in Dhahran city, Saudi Arabia. METHODS The theories/ models were reviewed using scientific databases. The inclusion criteria were if the theories/ models used to explain individual behaviors toward accepting and using of information technology including the once conducted within the healthcare. RESULTS The review showed that there were five theories/ models were used within information technology studies to understand the technology acceptance and used. There were Theory of Reasoned Action, Theory of Planned Behaviour, Innovation Diffusion Theory, Unified theory of acceptance and use of technology, and Technology Acceptance Model. Each has different explanatory power of technology use. The most appropriate theoretical framework to understand the reason behind physician use of the EHR at KFMMC would be the Technology Acceptance Model (TAM). TAM model could explain up to 75% of the variation in the behavioral intention (acceptance), and up to 62% of the variation in the actual use. It is the gold standard for assessing the usage of health technologies and systems. In fact, the TAM model is one of the core models used to explore the physician’s perceptions of the Electronic Health Record system adoption. CONCLUSIONS This review showed that there are different theories available in the literature can be used to justify the reason behind electronic health record acceptance. TAM is one of the effective, simplest models used to understand the factors influencing physicians to use the EHR-system. Further studies need to apply the TAM model to check its ability in explaining the reason behind EHR within different hospitals in Saudi Arabia

scholarly journals Drivers of expectations: Why are Norwegian general practitioners skeptical of a prospective electronic health record?

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822098729
Author(s):  
Morten Hertzum ◽  
Gunnar Ellingsen ◽  
Line Melby
Keyword(s):  
Electronic Health Record ◽  
General Practitioners ◽  
Current System ◽  
Implementation Process ◽  
Health Record ◽  
Early Implementation ◽  
Ehr Adoption ◽  
Electronic Health

While expectations are well-known drivers of electronic health record (EHR) adoption, the drivers of expectations are more elusive. On the basis of interviews with general practitioners (GPs), we investigate how the early implementation process drives their expectations of an EHR that is being implemented in Norway. The GPs’ expectations of the prospective EHR are driven by (a) satisfying experiences with their current system, (b) the transfer of others’ experiences with the prospective EHR, (c) a sense of alignment, or lack thereof, with those in charge of the implementation process, (d) uncertainty about the inclusion of GP needs, and (e) competing technological futures. To manage expectations, starting early is important. Mismanaged expectations produce a need for convincing people to reverse their expectations. This appears to be the situation in Norway, where the GPs are currently skeptical of the prospective EHR.

scholarly journals Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record System

2019 ◽  
Vol 29 (Supp2) ◽  
pp. 441-450 ◽  
Author(s):  
Jesse M. Ehrenfeld ◽  
Keanan Gabriel Gottlieb ◽  
Lauren Brittany Beach ◽  
Shelby E. Monahan ◽  
Daniel Fabbri
Keyword(s):  
Health Care ◽  
Natural Language ◽  
Language Processing ◽  
Health Care Services ◽  
False Positive Rate ◽  
Health Condition ◽  
Record System ◽  
Care Services ◽  
Electronic Health ◽  
Hormone Use

Objective: To create a natural language pro­cessing (NLP) algorithm to identify transgen­der patients in electronic health records.Design: We developed an NLP algorithm to identify patients (keyword + billing codes). Patients were manually reviewed, and their health care services categorized by billing code.Setting: Vanderbilt University Medical CenterParticipants: 234 adult and pediatric trans­gender patientsMain Outcome Measures: Number of transgender patients correctly identified and categorization of health services utilized.Results: We identified 234 transgender pa­tients of whom 50% had a diagnosed men­tal health condition, 14% were living with HIV, and 7% had diabetes. Largely driven by hormone use, nearly half of patients attended the Endocrinology/Diabetes/Me­tabolism clinic. Many patients also attended the Psychiatry, HIV, and/or Obstetrics/Gyne­cology clinics. The false positive rate of our algorithm was 3%.Conclusions: Our novel algorithm correctly identified transgender patients and provided important insights into health care utiliza­tion among this marginalized population. Ethn Dis. 2019;29(Suppl 2): 441-450. doi:10.18865/ed.29.S2.441

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