IU Indianapolis ScholarWorks :: Browsing by Subject "Electronic Health Records"

Browsing by Subject "Electronic Health Records"

Now showing 1 - 10 of 25

Adoption of Health Information Technology Among US Nursing Facilities
(Elsevier, 2018-12-19) Vest, Joshua R.; Jung, Hye-Young; Wiley, Kevin; Kooreman, Harold; Pettit, Lorren; Unruh, Mark A.; Health Policy and Management, School of Public Health
Objectives: Nursing facilities have lagged behind in the adoption interoperable health information technology (i.e. technologies that allow the sharing and use of electronic patient information between different information systems). The objective of this study was to estimate the nationwide prevalence of electronic health record (EHR) adoption among nursing facilities and to identify the factors associated with adoption. Design: Cross-sectional survey. Setting & participants: We surveyed members of the Society for Post-Acute & Long-Term Care Medicine (AMDA) about their organizations’ health information technology usage and characteristics. Measurements: Using questions adopted from existing instruments, the survey measured nursing home’s EHR adoption, the ability to send, receive, search and integrate electronic information, as well as barriers to usage. Additionally, we linked survey responses to public use secondary data sources to construct measurements for eight determinants known to be associated with organizational adoption: innovativeness, functional differentiation, role specialization, administrative intensity, professionalism, complexity, technical knowledge resources and slack resources. A series of regression models estimated the association between potential determinants and technology adoption. Results: 84% of nursing facilities reported using an EHR. After controlling for all other factors, respondents who characterized their organization as more innovative had more than 6 times the odds (adjusted odds ratio = 6.39; 95%CI = 2.69, 15.21) of adopting an EHR. Organization innovativeness was also associated with an increased odds of being able to send, integrate, and search for electronic information. The most commonly identified barrier to sharing clinical information among nursing facilities with an EHR was a reported absence of interoperability (57%). Conclusions/Implications: An organizational culture that fosters innovation and awareness campaigns by professional societies may facilitate further adoption and effective use of technology. This will be increasingly important as policymakers continue to emphasize the use of EHRs and interoperability to improve the quality of care in nursing facilities.
Advancing cognitive engineering methods to support user interface design for electronic health records
(Elsevier, 2014-04) Thyvalikakath, Thankam P.; Dziabiak, Michael P.; Johnson, Raymond; Torres-Urquidy, Miguel Humberto; Acharya, Amit; Yabes, Jonathan; Schleyer, Titus K.; Department of Cariology, Operative Dentistry and Dental Public Health, IU School of Dentistry
Background Despite many decades of research on the effective development of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow, especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practitioners in the United States. A critical reason for slow adoption is the poor usability of clinical systems, which makes it difficult for providers to navigate through the information and obtain an integrated view of patient data. Objective In this study, we documented the cognitive processes and information management strategies used by dentists during a typical patient examination. The results will inform the design of a novel electronic dental record interface. Methods We conducted a cognitive task analysis (CTA) study to observe ten general dentists (five general dentists and five general dental faculty members, each with more than two years of clinical experience) examining three simulated patient cases using a think-aloud protocol. Results Dentists first reviewed the patient’s demographics, chief complaint, medical history and dental history to determine the general status of the patient. Subsequently, they proceeded to examine the patient’s intraoral status using radiographs, intraoral images, hard tissue and periodontal tissue information. The results also identified dentists’ patterns of navigation through patient’s information and additional information needs during a typical clinician-patient encounter. Conclusion This study reinforced the significance of applying cognitive engineering methods to inform the design of a clinical system. Second, applying CTA to a scenario closely simulating an actual patient encounter helped with capturing participants’ knowledge states and decision-making when diagnosing and treating a patient. The resultant knowledge of dentists’ patterns of information retrieval and review will significantly contribute to designing flexible and task-appropriate information presentation in electronic dental records.
Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records
(2018) Patel, Jay; Mowery, Danielle; Krishnan, Anand; Thyvalikakath, Thankam
Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data.
Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic
(Oxford University Press, 2020-06-01) Holmgren, A. Jay; Apathy, Nate C.; Adler-Milstein, Julia; Health Policy and Management, School of Public Health
We sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region level. Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 hospital referral regions in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data. Public health agencies’ inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.
Better patient identification could help fight the coronavirus
(Nature Research, 2020-06-01) Moscovitch, Ben; Halamka, John D.; Grannis, Shaun; BioHealth Informatics, School of Informatics and Computing
Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions
(2019-07) Binkheder, Samar Hussein; Jones, Josette; Li, Lang; Quinney, Sara Kay; Wu, Huanmei; Zhang, Chi
Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement.
Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria
(The Korean Society of Medical Informatics, 2019-04) Purkayastha, Saptarshi; Allam, Roshini; Maity, Pallavi; Gichoya, Judy W.; BioHealth Informatics, School of Informatics and Computing
Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems. Methods: We performed qualitative content analysis with a directed approach on recently published literature (2012-2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system. Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially. Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regards to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.
Computer-facilitated review of electronic medical records reliably identifies emergency department interventions in older adults
(Society for Academic Emergency Medicine, 2013-06) Biese, Kevin J.; Forbach, Cory R.; Medlin, Richard P.; Platts- Mills, Timothy F.; Scholer, Matthew J.; McCall, Brenda; Shofer, Frances S.; LaMantia, Michael; Hobgood, Cherri; Kizer, J. S.; Busby-Whitehead, Jan; Cairns, Charles B.; Emergency Medicine, School of Medicine
OBJECTIVES: An estimated 14% to 25% of all scientific studies in peer-reviewed emergency medicine (EM) journals are medical records reviews. The majority of the chart reviews in these studies are performed manually, a process that is both time-consuming and error-prone. Computer-based text search engines have the potential to enhance chart reviews of electronic emergency department (ED) medical records. The authors compared the efficiency and accuracy of a computer-facilitated medical record review of ED clinical records of geriatric patients with a traditional manual review of the same data and describe the process by which this computer-facilitated review was completed. METHODS: Clinical data from consecutive ED patients age 65 years or older were collected retrospectively by manual and computer-facilitated medical record review. The frequency of three significant ED interventions in older adults was determined using each method. Performance characteristics of each search method, including sensitivity and positive predictive value, were determined, and the overall sensitivities of the two search methods were compared using McNemar's test. RESULTS: For 665 patient visits, there were 49 (7.4%) Foley catheters placed, 36 (5.4%) sedative medications administered, and 15 (2.3%) patients who received positive pressure ventilation. The computer-facilitated review identified more of the targeted procedures (99 of 100, 99%), compared to manual review (74 of 100 procedures, 74%; p < 0.0001). CONCLUSIONS: A practical, non-resource-intensive, computer-facilitated free-text medical record review was completed and was more efficient and accurate than manually reviewing ED records.
Data Analytics and Modeling for Appointment No-show in Community Health Centers
(SAGE, 2018) Mohammadi, Iman; Wu, Huanmei; Turkcan, Ayten; Toscos, Tammy; Doebbeling, Bradley N.; BioHealth Informatics, School of Informatics and Computing
Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.
Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory
(Oxford University Press, 2014-03-01) Rosenman, Marc; He, Jinghua; Martin, Joel; Nutakki, Kavitha; Eckert, George; Lane, Kathleen; Gradus-Pizlo, Irmina; Hui, Siu L.; Department of Pediatrics, IU School of Medicine
Background and objective Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical ‘phenotypes’ accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one ‘gold standard’ chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across different definitions. Materials and methods We used retrospective queries of hospitalizations (2002–2011) in the Indiana Network for Patient Care with any CHF ICD-9 diagnoses, a primary diagnosis, an echocardiogram performed, a B-natriuretic peptide (BNP) drawn, or BNP >500 pg/mL. We used a hybrid between proportional sampling by Venn zone and over-sampling non-overlapping zones. The acute CHF (presence/absence) outcome was based on expert chart review using a priori criteria. Results Among 79 091 hospitalizations, we reviewed 908. A query for any ICD-9 code for CHF had PPV 42.8% (SE 1.5%) for acute CHF and sensitivity 94.3% (1.3%). Primary diagnosis of 428 and BNP >500 pg/mL had PPV 90.4% (SE 2.4%) and sensitivity 28.8% (1.1%). PPV was <10% when there was no echocardiogram, no BNP, and no primary diagnosis. ‘False positive’ hospitalizations were for other heart disease, lung disease, or other reasons. Conclusions This novel method successfully allowed flexible application and validation of queries for patients hospitalized with acute CHF.

Browsing by Subject "Electronic Health Records"

Results Per Page

Sort Options