Interoperability Progress and Remaining Data Quality Barriers of Certified Health Information Technologies

The Consolidated Clinical Document Architecture (C-CDA) is the primary standard for clinical document exchange in the United States. While document exchange is prevalent today, prior research has documented challenges to high quality, effective interoperability using this standard. Many electronic health records (EHRs) have recently been certified to a new version of the C-CDA standard as part of federal programs for EHR adoption. This renewed certification generated example documents from 52 health information technologies that have been made publicly available. This research applies automated tooling and manual inspection to evaluate conformance and data quality of these testing artifacts. It catalogs interoperability progress as well as remaining barriers to effective data exchange. Its findings underscore the importance of programs that evaluate data quality beyond schematron conformance to enable the high quality and safe exchange of clinical data.

The full article can be downloaded below.

ScriptNumerate: A Data-to-Advice Pipeline using Compound Digital Objects to Increase the Interoperability of Computable Biomedical Knowledge

Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice. We asked whether a shared format could be used by both processes. To address this question, we developed a data-to-advice pipeline called ScriptNumerate. ScriptNumerate analyzes historical e-prescription data and communicates its results in a compound digital object format. ScriptNumerate then uses these same compound digital objects to compute its advice about whether new e-prescriptions have common, rare, or unprecedented instructions. ScriptNumerate demonstrates that data-to-advice pipelines are feasible. In the future, data-to-advice pipelines similar to ScriptNumerate may help support Learning Health Systems.

The full article can be downloaded below.  

MOBILE HEALTH (M-HEALTH) AND ELECTRONIC HEALTH (E-HEALTH) SERVICES: A STUDY IN COST-EFFECTIVENESS OF TELEMEDICINE, ELECTRONIC, AND MOBILE HEALTH SYSTEMS

A systematic review of cost-utility and cost-effectiveness research works of telemedicine, electronic health (ehealth), and mobile health (m-health) systems in the literature is presented. Academic databases and systems such as PubMed, Scopus, ISI Web of Science, and IEEE Xplore were searched, using different combinations of terms such as ‘‘cost-utility’’ OR ‘‘cost utility’’ AND ‘‘telemedicine,’’ ‘‘cost-effectiveness’’ OR ‘‘cost effectiveness’’ AND ‘‘mobile health,’’ etc. In the articles searched, there were no limitations in the publication date. The search identified 35 relevant works. Many of the articles were reviews of different studies. Seventy-nine percent concerned the cost effectiveness of telemedicine systems in different specialties such as teleophthalmology, telecardiology, teledermatology, etc. More articles were found between 2000 and 2017. Cost-utility studies were done only for telemedicine systems. There are few cost utility and cost-effectiveness studies for e-health and m-health systems in the literature. Some cost-effectiveness studies demonstrate that telemedicine can reduce the costs, but not all. Among the main limitations of the economic evaluations of telemedicine systems are the lack of randomized control trials, small sample sizes, and the absence of quality data and appropriate measures.

The full article can be downloaded below.  

Impediments to the Implementation of Healthcare Information Technology: A Systematic Literature Review 

The healthcare industry has seen a splurge in information technology investments largely due to the incentives offered by the government for its adoption as well as the penalties imposed under the HITECH Act of 2009. This has resulted in extensive research on Healthcare Information Technology (HIT) in recent years. In this study, we follow a systematic literature review across diverse disciplines ranging from management, information systems, and healthcare, and find that successful implementation of HIT follows three inter-related stages - adoption stage, integration stage, and sustenance stage. Given the uniqueness of healthcare industry with respect to knowledge-intensity and power hierarchy within job positions, we ascertain impediments that impact HIT implementation. Major impediments we identified include limited user buy-in, lack of risk assessment and safety measure during the adoption stage, physician resistance, spillover effect, standardized training, negative viewpoint in the integration stage, and lack of interoperability in the sustenance stage. Identifying and classifying impediments through a systematic literature review is the first step towards operationalizing these impediments and creating effective interventions to minimize their effect on HIT performance.

The full article can be downloaded below.  

Assessing EHR use during hospital mornin​g rounds: A multi-faceted study

The majority of U.S hospitals have implemented electronic health records (EHRs). While the benefits of EHRs have been widely touted, little is known about their effects on inpatient care, including how well they meet workflow needs and support care.

The objective was to assess the extent to which EHRs support care team workflow during hospital morning rounds.

We applied a mixed-method approach including observations of care teams during morning rounds, semi-structured interviews and an electronic survey of hospital inpatient clinicians. Structured field notes taken during observations were used to identify workflow patterns for analysis. We applied a grounded theory approach to extract emerging themes from interview transcripts and used SPSS Statistics 24 to analyze survey responses.  This took place at medical units at a major teaching hospital in New England.

Data triangulation across the three analyses yielded four main findings: (1) a high degree of variance in the ways care teams use EHRs during morning rounds. (2) Pervasive use of workarounds at critical points of care (3) EHRs are not used for information sharing and frequently impede intra-care team communication. (4) System design and hospital room settings do not adequately support care team workflow.

Gaps between EHR design and the functionality needed in the complex inpatient environment result in lack of standardized workflows, extensive use of workarounds and team communication issues. These issues pose a threat to patient safety and quality of care. Possible solutions need to include improvements in EHR design, care team training and changes to the hospital room setting.

The full article can be downloaded below.

Hidden FDA Reports Detail Harm Caused By Scores Of Medical Devices

The Food and Drug Administration has let medical device companies file reports of injuries and malfunctions outside a widely scrutinized public database, which leave doctors and medical sleuths in the dark.

The full Kaiser Health News article can be viewed at this link.  

How Banner Health Network is managing interoperability with 30-plus EHRs

Banner Health Network, with 5,000 affiliated physicians and 15 hospitals in the Phoenix area, is large and complex. Its accountable care and population health management efforts require lots of high quality data and a keen focus on closing gaps in care. But that's something of a challenge when those physicians are using more than 30 different electronic health records.

"Thirty-two, I think is the number," said David Coe, vice president of data management, analytics and population health technology at Banner Health. "So you could imagine doing point to point interfaces to each one of those individual EHR systems."

So BHN has recently tried a new approach to interoperability, partnering with Atlanta-based Holon for its CollaborNet platform, which Coe said connects physicians across the organization with necessary data to improve care coordination and pop health, enabling the jointly owned Banner|Aetna organization to make good on the the promise of value-based care, officials said.

The full Healthcare IT News article can be viewed at this link.  

Implementation best practices: Dealing with the complexity of AI

Artificial intelligence is just as complex as it sounds. Successful deployment of the various technologies that are necessary for AI to work requires planning and strategy..

To help chief information officers and other IT professionals better understand these best practices for implementing AI at their health systems, hospitals, group practices and other provider organizations, we spoke with four experts in AI technologies who offered their advice for effective rollouts.

Best Practices

• Identifying use cases - By identifying use-cases and successes, you can help categorize vendors who have a proven track record of success while reducing the financial risk your healthcare organization takes on by purchasing an AI tool.
• The expected value - When it comes to implementing AI, there is no substitute for sound business principles.  CIOs should apply the same rigor in the adoption of AI that they apply in the adoption of any other new technology.  As CIOs pursue a portfolio of initiatives, it’s critical to work with partners who can introduce solutions to a variety of areas in the hospital or network.
• The operational state - A key must-have when implementing an AI system is a clear vision of an organization's operational state and business goals.
• Focus on outcomes - Set goals and make sure you have ways to benchmark the success of the AI solution – know how long it will take to see an outcome.

The full Healthcare IT News article can be viewed at this link.  

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks​

Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation.

This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak.

We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation.

The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak.

AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

The full article can be downloaded below.  

CyberPDF: Smart and Secure Coordinate-based Automated Health PDF Data Batch Extraction

Data extraction from files is a prevalent activity in today’s electronic health record systems which can be laborious. When document analysis is repetitive (e.g., processing a series of files with the same layout and extraction requirements), relying on data-entry staff to manually perform such tasks is costly and highly insecure. Particularly analyzing a large list of PDF files (as a widely used format) to extract specific data and migrate them to other destinations for later use is both tedious and frustrating to do manually. This paper addresses a very practical requirement of batch extracting data from PDF files in health data document analysis and beyond. Specifically, we propose a Coordinate Based Information Extraction System (CBIES) to instrument a smart and automatic PDF batch data extraction tool, releasing health organizations from duplicate efforts and reducing labor costs. The proposed technique enables users to query a representative PDF document and extract the same data from a series of files in the batch analysis manner swiftly. Furthermore, since security and privacy considerations are essential part of any health record systems, it is included in our approach. Based on CBIES, we implement a prototype tool for PDF batch data extraction technique named, CyberPDF. The tool exhibits great efficiency, security and accuracy in multi-file data processing.

The full article can be downloaded below.