Navigation in the electronic health record

Background The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. Results The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. Conclusions The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

/pdf/digital-health-data-quality-issues-systematic-review-3tv08n1q.pdf

Digital Health Data Quality Issues: Systematic Review

Objective To identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated. Design A mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts. Setting Three urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics. Participants Observation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies. Methods 330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks. Results The model predicted that roughly 44% of administrative tasks carried out by staff in general practice are ‘mostly’ or ‘completely’ automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation. Conclusions There is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.

https://bmjopen.bmj.com/content/bmjopen/10/6/e032412.full.pdf

Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice

The process of documentation is one of the most important parts of electronic health records (EHR). It is time-consuming, and up until now, available documentation procedures have not been able to overcome this type of EHR limitations. Thus, entering information into EHR still has remained a challenge. In this study, by applying the trigram language model, we presented a method to predict the next words while typing free texts. It is hypothesized that using this system may save typing time of free text. The words prediction model introduced in this research was trained and tested on the free texts regarding to colonoscopy, transesophageal echocardiogram, and anterior-cervical-decompression. Required time of typing for each of the above-mentioned reports calculated and compared with manual typing of the same words. It is revealed that 33.36% reduction in typing time and 73.53% reduction in keystroke. The designed system reduced the time of typing free text which might be an approach for EHRs improvement in terms of documentation.

Words prediction based on N-gram model for free-text entry in electronic health records

Background:Aspirin impacts risk for important outcomes such as cancer, cardiovascular disease, and gastrointestinal bleeding. However, ascertaining exposure to medications available both by prescription and over-the-counter such as aspirin for research and quality improvement purposes is a challenge

Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data.

Electronic Health Records (EHRs) have increased the utility and portability of health information by storing it in structured formats. However, EHRs separate this structured data from the rich, free-text descriptions of clinical notes. The ultimate objective of our research is to develop an interactive progress note that unifies entry, access, and retrieval of structured and unstructured health information. In this study we present the design and subsequent testing with eight clinicians of a core element of this envisioned note: free-text order entry. Clinicians saw this new order-entry paradigm as a way to save time and preserve data quality by reducing double-documentation. However, they wanted the prototype to recognize more diverse types of shorthand and apply default values to fields that remain fairly constant across orders, such as number of refills and pickup location. Future work will test more complex orders, such as cascading orders, with a broader range of clinicians.

Validating free-text order entry for a note-centric EHR.

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Validating free-text order entry for a note-centric EHR.