Information technologies that facilitate care coordination: provider and patient perspectives

Health information technology is a core infrastructure for the chronic care model, integrated care, and other organized care delivery models. From the provider perspective, health information exchange (HIE) helps aggregate and share information about a patient or population from several sources. HIE technologies include direct messages, transfer of care, and event notification services. From the patient perspective, personal health records, secure messaging, text messages, and other mHealth applications may coordinate patients and providers. Patient-reported outcomes and social media technologies enable patients to share health information with many stakeholders, including providers, caregivers, and other patients. An information architecture that integrates personal health record and mHealth applications, with HIEs that combine the electronic health records of multiple healthcare systems will create a rich, dynamic ecosystem for patient collaboration

Physicians' perceptions of an electronic health record-based clinical trial alert approach to subject recruitment: A survey

<p>Abstract</p> <p>Background</p> <p>Physician participation in clinical research recruitment efforts is critical to many studies' success, but it is often limited. Use of an Electronic Health Record (EHR)-based, point-of-care Clinical Trial Alert (CTA) approach has led to significant increases in physician-generated recruitment and holds promise for wider benefit. However, little is known about physicians' decision-making regarding recruitment in EHR-equipped settings or the use of such EHR-based approaches. We sought to assess physicians' perceptions about recruitment in general and using the CTA approach in particular.</p> <p>Methods</p> <p>We developed and delivered a Web-based survey consisting of 15 multiple-choice and free-text questions. Participants included the 114 physician subjects (10 endocrinologists and 104 general internists) who were exposed to CTAs during our preceding 4-month intervention study. Response data were descriptively analyzed, and key findings were compared between groups using appropriate statistical tests.</p> <p>Results</p> <p>Sixty-nine physicians (61%) responded during the 10-week survey period. Respondents and non-respondents did not differ significantly. Twenty-seven percent of respondents felt very comfortable recruiting patients to trials in general, and 77% appreciated being reminded about a trial via a CTA. Only 11% percent felt the CTA was difficult to use, and 27% felt it was more than somewhat intrusive. Among those who ignored all CTAs, 37% cited a lack of time, 28% knowledge of the patient's ineligibility, and 13% limited knowledge about the trial as their most common reason. Thirty-eight percent wanted more information about the trial presented in the CTA, and 73% were interested in seeing CTAs for future trials. Comments and suggestions were submitted by 33% of respondents and included suggestions for improvement of the CTA approach.</p> <p>Conclusion</p> <p>Most physicians were comfortable recruiting patients for clinical trials at the point-of-care, found the EHR-based CTA approach useful and would like to see it used in the future. These findings provide insight into the perceived utility of this EHR-based approach to subject recruitment, suggest ways it might be improved, and add to the limited body of knowledge regarding physicians' attitudes toward clinical trial recruitment in EHR-equipped settings.</p

Foundations for Studying Clinical Workflow: Development of a Composite Inter-Observer Reliability Assessment for Workflow Time Studies

The ability to understand and measure the complexity of clinical workflow provides hospital managers and researchers with the necessary knowledge to assess some of the most critical issues in healthcare. Given the protagonist role of workflow time studies on influencing decision makers, major efforts are being conducted to address existing methodological inconsistencies of the technique. Among major concerns, the lack of a standardized methodology to ensure the reliability of human observers stands as a priority. In this paper, we highlight the limitations of the current Inter-Observer Reliability Assessments, and propose a novel composite score to systematically conduct them. The composite score is composed of a) the overall agreement based on Kappa that evaluates the naming agreement on virtually created one-seconds tasks, providing a global assessment of the agreement over time, b) a naming agreement based on Kappa, requiring an observation pairing approach based on time-overlap, c) a duration agreement based on the concordance correlation coefficient, that provides means to evaluate the correlation concerning tasks duration, d) a timing agreement, based on descriptive statistics of the gaps between timestamps of same-task classes, and e) a sequence agreement based on the Needleman-Wunsch sequence alignment algorithm. We hereby provide a first step towards standardized reliability reporting in workflow time studies. This new composite IORA protocol is intended to empower workflow researchers with a standardized and comprehensive method for validating observers' reliability and, in turn, the validity of their data and results

What can we learn about SARS-CoV-2 prevalence from testing and hospital data?

Measuring the prevalence of active SARS-CoV-2 infections is difficult because tests are conducted on a small and non-random segment of the population. But people admitted to the hospital for non-COVID reasons are tested at very high rates, even though they do not appear to be at elevated risk of infection. This sub-population may provide valuable evidence on prevalence in the general population. We estimate upper and lower bounds on the prevalence of the virus in the general population and the population of non-COVID hospital patients under weak assumptions on who gets tested, using Indiana data on hospital inpatient records linked to SARS-CoV-2 virological tests. The non-COVID hospital population is tested fifty times as often as the general population. By mid-June, we estimate that prevalence was between 0.01 and 4.1 percent in the general population and between 0.6 to 2.6 percent in the non-COVID hospital population. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. The combination of clinical testing data and hospital records may contain much more information about the state of the epidemic than has been previously appreciated. The bounds we calculate for Indiana could be constructed at relatively low cost in many other states

SUCCESSFUL LANGUAGE LEARNING STRATEGIES USED BY SUCCESSFUL YEAR 5 ENGLISH AS A SECOND LANGUAGE (ESL) LEARNERS

Research on language learning strategies in Malaysia has been carried out extensively since mid 1990s. However, these studies have not covered the language learning strategies among native pupils in suburban primary school in Mukah, Sarawak. The main objective of the study was to identify the language learning strategies used by English as Second Language (ESL) learners. Data was collected using a survey questionnaire with 20 outstanding Year 5 ESL Iban learners in one of the suburban schools in Mukah, Sarawak. The instrument used in this study include a Language Strategy Use Questionnaire adapted from Language Strategy Use Inventory by Cohen, Oxford and Chi (2002). The adapted version of Language Strategy Use Questionnaire consists of 60 statements concerning the four major English language skills, namely listening, speaking and reading as well as acquisition of vocabulary and grammar. Data was analyzed through mean, frequency, percentage and standard deviation. The findings revealed that these learners were moderate users of listening, reading, writing, grammar and vocabulary strategies and low users of speaking strategies. There were variations in responses with regard to the use of language learning strategies among primary school learners. The pedagogical implications of the findings are also discussed.

Research IT maturity models for academic health centers: Early development and initial evaluation

This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools

Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare data age. This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be better together

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and population

Diagnosis-Specific Readmission Risk Prediction Using Electronic Health Data: a Retrospective Cohort Study

Background: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. Methods: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis. The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. Results: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). Conclusions: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged