Adapting an Environmental Scan for 'Insights Reporting': Learnings from an Online Brain Cancer Peer Support Platform.

When developing a digital health solution, product owners, healthcare professionals, researchers, IT teams, and consumers require timely, accurate contextual information to inform solution development. Insights Reporting can rapidly draw together information from literature, end users and existing technology to inform the development process. This was the case when creating an online brain cancer peer support platform where solution development was conducted in parallel with contextual information synthesis. This paper discusses the novel adaptation of an environmental scan methodology using codesign and multiple layers of qualitative rigor, to create Insights Reporting. This seven-step process can be completed in two months and results in salient points of knowledge that can rapidly inform the design of a solution, creating a shared understanding of a digital health phenomenon. Project members noted that Insights Reporting surfaces previously inaccessible knowledge, catalyzes decision-making and allows all stakeholders to influence the report agenda, affirming principles of digital health equity.

Retrospective analysis of the impact of electronic medical record alerts on low value care in a pediatric hospital.

OBJECTIVES: Hospital costs continue to rise unsustainably. Up to 20% of care is wasteful including low value care (LVC). This study aimed to understand whether electronic medical record (EMR) alerts are effective at reducing pediatric LVC and measure the impact on hospital costs. MATERIALS AND METHODS: Using EMR data over a 76-month period, we evaluated changes in 4 LVC practices following the implementation of EMR alerts, using time series analysis to control for underlying time-based trends, in a large pediatric hospital in Australia. The main outcome measure was the change in rate of each LVC practice. Balancing measures included the rate of alert adherence as a proxy measure for risk of alert fatigue. Hospital costs were calculated by the volume of LVC avoided multiplied by the unit costs. Costs of the intervention were calculated from clinician and analyst time required. RESULTS: All 4 LVC practices showed a statistically significant reduction following alert implementation. Two LVC practices (blood tests) showed an abrupt change, associated with high rates of alert adherence. The other 2 LVC practices (bronchodilator use in bronchiolitis and electrocardiogram ordering for sleeping bradycardia) showed an accelerated rate of improvement compared to baseline trends with lower rates of alert adherence. Hospital savings were $325 to $180 000 per alert. DISCUSSION AND CONCLUSION: EMR alerts are effective in reducing pediatric LVC practices and offer a cost-saving opportunity to the hospital. Further efforts to leverage EMR alerts in pediatric settings to reduce LVC are likely to support future sustainable healthcare delivery.

Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes.

As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, DRG codes are valuable for cost monitoring and resource allocation. However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs and DRG-based case mix index (CMI) at early inpatient admission using routine clinical text to estimate hospital cost in an acute setting. We examined a deep learning-based natural language processing (NLP) model to automatically predict per-episode DRGs and corresponding cost-reflecting weights on two cohorts (paid under Medicare Severity (MS) DRG or All Patient Refined (APR) DRG), without human coding efforts. It achieved macro-averaged area under the receiver operating characteristic curve (AUC) scores of 0·871 (SD 0·011) on MS-DRG and 0·884 (0·003) on APR-DRG in fivefold cross-validation experiments on the first day of ICU admission. When extended to simulated patient populations to estimate average cost-reflecting weights, the model increased its accuracy over time and obtained absolute CMI error of 2·40 (1·07%) and 12·79% (2·31%), respectively on the first day. As the model could adapt to variations in admission time, cohort size, and requires no extra manual coding efforts, it shows potential to help estimating costs for active patients to support better operational decision-making in hospitals.

Quality assessment of real-world data repositories across the data life cycle: A literature review.

OBJECTIVE: Data quality (DQ) must be consistently defined in context. The attributes, metadata, and context of longitudinal real-world data (RWD) have not been formalized for quality improvement across the data production and curation life cycle. We sought to complete a literature review on DQ assessment frameworks, indicators and tools for research, public health, service, and quality improvement across the data life cycle. MATERIALS AND METHODS: The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases from health, physical and social sciences were used: Cinahl, Embase, Scopus, ProQuest, Emcare, PsycINFO, Compendex, and Inspec. Embase was used instead of PubMed (an interface to search MEDLINE) because it includes all MeSH (Medical Subject Headings) terms used and journals in MEDLINE as well as additional unique journals and conference abstracts. A combined data life cycle and quality framework guided the search of published and gray literature for DQ frameworks, indicators, and tools. At least 2 authors independently identified articles for inclusion and extracted and categorized DQ concepts and constructs. All authors discussed findings iteratively until consensus was reached. RESULTS: The 120 included articles yielded concepts related to contextual (data source, custodian, and user) and technical (interoperability) factors across the data life cycle. Contextual DQ subcategories included relevance, usability, accessibility, timeliness, and trust. Well-tested computable DQ indicators and assessment tools were also found. CONCLUSIONS: A DQ assessment framework that covers intrinsic, technical, and contextual categories across the data life cycle enables assessment and management of RWD repositories to ensure fitness for purpose. Balancing security, privacy, and FAIR principles requires trust and reciprocity, transparent governance, and organizational cultures that value good documentation.

Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis.

BACKGROUND: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record-based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. OBJECTIVE: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. METHODS: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. RESULTS: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. CONCLUSIONS: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system.

Mapping the Patient's Journey in Healthcare through Process Mining.

Nowadays, assessing and improving customer experience has become a priority, and has emerged as a key differentiator for business and organizations worldwide. A customer journey (CJ) is a strategic tool, a map of the steps customers follow when engaging with a company or organization to obtain a product or service. The increase of the need to obtain knowledge about customers' perceptions and feelings when interacting with participants, touchpoints, and channels through different stages of the customer life cycle. This study aims to describe the application of process mining techniques in healthcare as a tool to asses customer journeys. The appropriateness of the approach presented is illustrated through a case study of a key healthcare process. Results depict how a healthcare process can be mapped through the CJ components, and its analysis can serve to understand and improve the patient's experience.

The Online Availability of Multilingual Health Promotion Materials Produced by Local Health Departments: an Information Assessment.

OBJECTIVES: Local Health Departments (LHDs) are a key source of health promotion information. For ethnically and culturally diverse communities, it becomes important to provide minorities with language appropriate health information. This project sought to assess the availability of multilingual health promotion materials on LHD websites in Washington State (WA), USA. METHODS: We performed a cross-sectional study of all 34 LHD websites in WA. We collected and classified health promotion documents available to the public, specifically, whether translated versions were available. We also assessed the extent of document sharing between LHDs. RESULTS: We identified 1,624 documents across 34 LHDs. Topics most frequently covered were communicable diseases and emergency preparedness. Fewer than 10% of documents were available in non-English languages. We found little evidence of document sharing between LHDs; only 5% of all documents were shared between LHDs. CONCLUSIONS: WA LHDs provide a variety of health promotion materials for the public, but few multilingual materials are available online. New technologies for facilitating document sharing and machine translation may improve the present landscape.

Availability of structured and unstructured clinical data for comparative effectiveness research and quality improvement: a multisite assessment.

INTRODUCTION: A key attribute of a learning health care system is the ability to collect and analyze routinely collected clinical data in order to quickly generate new clinical evidence, and to monitor the quality of the care provided. To achieve this vision, clinical data must be easy to extract and stored in computer readable formats. We conducted this study across multiple organizations to assess the availability of such data specifically for comparative effectiveness research (CER) and quality improvement (QI) on surgical procedures. SETTING: This study was conducted in the context of the data needed for the already established Surgical Care and Outcomes Assessment Program (SCOAP), a clinician-led, performance benchmarking, and QI registry for surgical and interventional procedures in Washington State. METHODS: We selected six hospitals, managed by two Health Information Technology (HIT) groups, and assessed the ease of automated extraction of the data required to complete the SCOAP data collection forms. Each data element was classified as easy, moderate, or complex to extract. RESULTS: Overall, a significant proportion of the data required to automatically complete the SCOAP forms was not stored in structured computer-readable formats, with more than 75 percent of all data elements being classified as moderately complex or complex to extract. The distribution differed significantly between the health care systems studied. CONCLUSIONS: Although highly desirable, a learning health care system does not automatically emerge from the implementation of electronic health records (EHRs). Innovative methods to improve the structured capture of clinical data are needed to facilitate the use of routinely collected clinical data for patient phenotyping.

Local health department translation processes: potential of machine translation technologies to help meet needs.

Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

Using crowdsourcing technology for testing multilingual public health promotion materials.

BACKGROUND: Effective communication of public health messages is a key strategy for health promotion by public health agencies. Creating effective health promotion materials requires careful message design and feedback from representatives of target populations. This is particularly true when the target audiences are hard to reach as limited English proficiency groups. Traditional methods of soliciting feedback--such as focus groups and convenience sample interviews--are expensive and time consuming. As a result, adequate feedback from target populations is often insufficient due to the time and resource constraints characteristic to public health. OBJECTIVE: To describe a pilot study investigating the use of crowdsourcing technology as a method to gather rapid and relevant feedback on the design of health promotion messages for oral health. Our goal was to better describe the demographics of participants responding to a crowdsourcing survey and to test whether crowdsourcing could be used to gather feedback from English-speaking and Spanish-speaking participants in a short period of time and at relatively low costs. METHODS: We developed health promotion materials on pediatric dental health issues in four different formats and in two languages (English and Spanish). We then designed an online survey to elicit feedback on format preferences and made it available in both languages via the Amazon Mechanical Turk crowdsourcing platform. RESULTS: We surveyed 236 native English-speaking and 163 native Spanish-speaking participants in less than 12 days, at a cost of US $374. Overall, Spanish-speaking participants originated from a wider distribution of countries than the overall Latino population in the United States. Most participants were in the 18- to 29-year age range and had some college or graduate education. Participants provided valuable input for the health promotion material design. CONCLUSIONS: Our results indicate that crowdsourcing can be an effective method for recruiting and gaining feedback from English-speaking and Spanish-speaking people. Compared with traditional methods, crowdsourcing has the potential to reach more diverse populations than convenience sampling, while substantially reducing the time and cost of gathering participant feedback. More widespread adoption of this method could streamline the development of effective health promotion materials in multiple languages.