Alcohol and drug use are leading causes of morbidity and mortality that frequently go unidentified in medical settings. As part of a multi-phase study to implement electronic health record-integrated ...substance use screening in primary care clinics, we interviewed key clinical stakeholders to identify current substance use screening practices, barriers to screening, and recommendations for its implementation.
Focus groups and individual interviews were conducted with 67 stakeholders, including patients, primary care providers (faculty and resident physicians), nurses, and medical assistants, in two urban academic health systems. Themes were identified using an inductive approach, revised through an iterative process, and mapped to the Knowledge to Action (KTA) framework, which guides the implementation of new clinical practices (Graham et al. in J Contin Educ Health Prof 26(1):13-24, 2006).
Factors affecting implementation based on KTA elements were identified from participant narratives. Identifying the problem: Participants consistently agreed that having knowledge of a patient's substance use is important because of its impacts on health and medical care, that substance use is not properly identified in medical settings currently, and that universal screening is the best approach. Assessing barriers: Patients expressed concerns about consequences of disclosing substance use, confidentiality, and the individual's own reluctance to acknowledge a substance use problem. Barriers identified by providers included individual-level factors such as lack of clinical knowledge and training, as well as systems-level factors including time pressure, resources, lack of space, and difficulty accessing addiction treatment. Adapting to the local context: Most patients and providers stated that the primary care provider should play a key role in substance use screening and interventions. Opinions diverged regarding the optimal approach to delivering screening, although most preferred a patient self-administered approach. Many providers reported that taking effective action once unhealthy substance use is identified is crucial.
Participants expressed support for substance use screening as a valuable part of medical care, and identified individual-level as well as systems-level barriers to its implementation. These findings suggest that screening programs should clearly communicate the goals of screening to patients and proactively counteract stigma, address staff concerns regarding time and workflow, and provide education as well as treatment resources to primary care providers.
Defining AMIA’s artificial intelligence principles Solomonides, Anthony E; Koski, Eileen; Atabaki, Shireen M ...
Journal of the American Medical Informatics Association : JAMIA,
03/2022, Letnik:
29, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Abstract
Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need ...for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including “safe failure”; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence—aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.
The mapping of the human genome has enabled new exploration of how genetic variations contribute to health and disease. This research has been very successful not only in shedding light on how ...genetic variants influence susceptibility to common, chronic diseases but also in playing an instrumental role in the discovery of new biologic pathways and drug targets. Despite the growing body of literature of the value of pharmacogenomic variants to drug efficacy and safety and disease variants for risk, significant challenges remain in translating this information into clinical practice at the point of care, let alone integrating it into electronic health records (EHRs).
Highlights ► This study successfully combined “think-aloud” protocol analysis with “near-live” clinical simulations in a usability evaluation of a new primary care CDS tool. ► These two forms of ...usability evaluation provided complementary observations on problems with the new tool and were used to refine both its usability and workflow integration. ► Their synergistic use provided a robust assessment of how CDS tools would interact in live clinical environments and allowed for enhanced early redesign to augment clinician utilization. ► These findings suggest the importance of using complementary testing methods before releasing CDS for live use.
The development and availability of genomic applications for use in clinical care is accelerating rapidly. the routine use of genomic information, however, is beyond most health-care providers' ...formal training, and the challenges of understanding and interpreting genomic data are compounded by the demands of clinical practice. nearly all physicians, for...
•NAFLD is documented poorly in the EMR. We assess how well we can identify it using an NLP approach versus ICD or text search.•NAFLD can progress to NASH and cirrhosis. We examine our ability to ...measure disease progression within the EMR with NLP.•We look at breakdowns in the knowledge chain between doctors, when NAFLD was identified but not mentioned in future notes.•We identify cases of these breakdowns where the patient developed NASH/cirrhosis without referencing prior NAFLD diagnosis.
Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers.
All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes.
Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days.
NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis.
For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.
The articles in this special issue take advantage of the research and experience of the Electronic Medical Records and Genomics (eMERGE) Network and are designed to provide operational and academic ...leaders with a "getting started" guide for integrating genomic information into the electronic health record (EHR). As noted in the article by Gottesman et al., the eMERGE network has been actively researching issues that shed light on the integration of genomic information into the EHR. However, as the authors in this special issue have indicated, many questions and challenges remain. We have completed mapping of terra incognita and have now arrived at the shores of the undiscovered country.
Clinical prediction rules (CPRs) represent well-validated but underutilized evidence-based medicine tools at the point-of-care. To date, an inability to integrate these rules into an electronic ...health record (EHR) has been a major limitation and we are not aware of a study demonstrating the use of CPR's in an ambulatory EHR setting. The integrated clinical prediction rule (iCPR) trial integrates two CPR's in an EHR and assesses both the usability and the effect on evidence-based practice in the primary care setting.
A multi-disciplinary design team was assembled to develop a prototype iCPR for validated streptococcal pharyngitis and bacterial pneumonia CPRs. The iCPR tool was built as an active Clinical Decision Support (CDS) tool that can be triggered by user action during typical workflow. Using the EHR CDS toolkit, the iCPR risk score calculator was linked to tailored ordered sets, documentation, and patient instructions. The team subsequently conducted two levels of 'real world' usability testing with eight providers per group. Usability data were used to refine and create a production tool. Participating primary care providers (n = 149) were randomized and intervention providers were trained in the use of the new iCPR tool. Rates of iCPR tool triggering in the intervention and control (simulated) groups are monitored and subsequent use of the various components of the iCPR tool among intervention encounters is also tracked. The primary outcome is the difference in antibiotic prescribing rates (strep and pneumonia iCPR's encounters) and chest x-rays (pneumonia iCPR only) between intervention and control providers.
Using iterative usability testing and development paired with provider training, the iCPR CDS tool leverages user-centered design principles to overcome pervasive underutilization of EBM and support evidence-based practice at the point-of-care. The ongoing trial will determine if this collaborative process will lead to higher rates of utilization and EBM guided use of antibiotics and chest x-ray's in primary care.
ClinicalTrials.gov Identifier NCT01386047.
This paper describes an innovative approach to the evaluation of a handheld prescription writing application. Participants (10 physicians) were asked to perform a series of tasks involving entering ...prescriptions into the application from a medication list. The study procedure involved the collection of data consisting of transcripts of the subjects who were asked to “think aloud” while interacting with the prescription writing program to enter medications. All user interactions with the device were video and audio recorded. Analysis of the protocols was conducted in two phases: (1) usability problems were identified from coding of the transcripts and video data, (2) actual errors in entering prescription data were also identified. The results indicated that there were a variety of usability problems, with most related to interface design issues. In examining the relationship between usability problems and errors, it was found that certain types of usability problems were closely associated with the occurrence of specific types of errors in prescription of medications. Implications for identifying and predicting technology-induced error are discussed in the context of improving the safety of health care information systems.
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