A network model of activities in primary care consultations.

OBJECTIVE:The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. MATERIALS AND METHODS:This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. RESULTS:Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient's present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. DISCUSSION:Primary care consultations do not appear to follow a classic linear model of defined information seeking activities; rather, they are fragmented, highly interdependent, and can be reactively triggered. CONCLUSION:The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries

Challenges of developing a digital scribe to reduce clinical documentation burden.

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms

Identifying relevant information in medical conversations to summarize a clinician-patient encounter

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries. </jats:p

Physician Practice Variation in Electronic Health Record Documentation.

Adoption of electronic health records (EHRs) was motivated by the expectation that they would improve quality and decrease costs of care. EHRs’ value, however, depends on how they are used, which likely explains the heterogeneous benefits observed in the literature. This dissertation uses mixed methods to explore a critical component of EHR use in primary care: variation in EHR documentation, defined as differences in how users record or remove information. The first chapter delineates a conceptual framework of variation in EHR documentation that includes five different forms of variation and five levels where the forms may materialize. This chapter focuses on potentially harmful variation by detailing how non-patient factors foster variation that interferes with clinical decision support, care coordination, and population health management, jeopardizing the efficient delivery of high-quality healthcare. The second chapter measures variation in one form of variation, completion of documentation, in a national sample of primary care practices. Using data from a major EHR vendor, this chapter finds differences in how variably providers complete fifteen different clinical documentation categories and identifies patient’s problems, the provider’s assessment and diagnosis, the social history, the review of systems, and communication about lab and test results as the most varied. The majority of variation exists across providers in the same practice, suggesting providers are making different decisions about documentation for comparable patients. The final chapter explores the context of this variation with semi-structured interviews, finding that variation in EHR documentation is perceived as a commonplace phenomenon resulting from a flexible EHR design that allows users to develop different documentation styles. Variation reportedly introduced inefficiencies into care delivery and created patient safety and care quality risks from missed or misinterpreted information. Respondents identified additional training, ongoing meetings, and improvements in EHR design as effective strategies to prevent harm. Widespread variation in EHR documentation can interfere with care delivery by obscuring the location and meaning of patient information. In order to realize gains from adopting EHRs, practices, vendors, and policymakers must collaboratively develop better interfaces and clearer guidelines to support their effective use.PHDHealth Services Organization & PolicyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135900/1/grcohen_1.pd

Documentation in Nursing and Midwifery: Australian edition

Documenting patient care and records is an important skill for nurses. This book is designed for students in undergraduate nursing programs, and addresses principles of documentation, legislation associated with documentation, methods and systems of documentation, and related documentation matters, in the Australian context

A roadmap to reduce information inequities in disability with digital health and natural language processing

People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify 3 key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person’s experience of function; (2) underemphasis of the patient’s voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose 3 directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient’s unique experience: (1) analyzing existing information on function in free text documentation; (2) developing new NLP-driven methods to collect information on contextual factors; and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations

Taking Note: A Design Solution for Physician Documentation to Balance the Benefits of Handwritten Notes and Electronic Health Records

Master of Design in Integrative DesignUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136865/1/THo_2017_MDes-Thesis.pd