Background: The impact of COVID-19 on gastrointestinal (GI) outcomes in children during the post-acute and chronic phases of the disease is not well understood. Methods: We conducted a retrospective cohort study across twenty-nine healthcare institutions from March 2020 to September 2023, including 413,455 pediatric patients with confirmed SARS-CoV-2 infection and 1,163,478 controls without infection. Infection was confirmed via polymerase chain reaction (PCR), serology, antigen tests, or clinical diagnosis of COVID-19 and related conditions. We examined the incidence of predefined GI symptoms and disorders during the post-acute (28 to 179 days post-infection) and chronic (180 to 729 days post-infection) phases. The adjusted risk ratios (aRRs) were calculated using stratified Poisson regression, with stratification based on propensity scores. Results: Our cohort comprised 1,576,933 patients, with females representing 48.0% of the sample. The analysis revealed that children with SARS-CoV-2 infection had an increased risk of developing at least one GI symptom or disorder in both the post-acute (8.64% vs. 6.85%; aRR 1.25, 95% CI 1.24-1.27) and chronic phases (12.60% vs. 9.47%; aRR 1.28, 95% CI 1.26-1.30) compared to uninfected peers. Specifically, the risk of abdominal pain was higher in COVID-19 positive patients during the post-acute phase (2.54% vs. 2.06%; aRR 1.14, 95% CI 1.11-1.17) and chronic phase (4.57% vs. 3.40%; aRR 1.24, 95% CI 1.22-1.27). Interpretation: Children with a history of SARS-CoV-2 infection are at an increased risk of GI symptoms and disorders during the post-acute and chronic phases of COVID-19. This highlights the need for ongoing monitoring and management of GI outcomes in this population.
As Electronic Health Record (EHR) systems increase in usage, organizations struggle to maintain and categorize clinical documentation so it can be used for clinical care and research. While prior research has often employed natural language processing techniques to categorize free text documents, there are shortcomings relative to computational scalability and the lack of key metadata within notes' text. This study presents a framework that can allow institutions to map their notes to the LOINC document ontology using a Bag of Words approach. After preliminary manual value- set mapping, an automated pipeline that leverages key dimensions of metadata from structured EHR fields aligns the notes with the dimensions of the document ontology. This framework resulted in 73.4% coverage of EHR documents, while also mapping 132 million notes in less than 2 hours; an order of magnitude more efficient than NLP based methods.
Electronic Health Records , Logical Observation Identifiers Names and Codes , Humans , Metadata , Documentation
Patients suffering from ischemic heart disease (IHD) should be monitored closely after being discharged. With recent advances in digital health tools, collecting, using, and sharing patient-generated health data (PGHD) has become more achievable. PGHD can complement the existing clinical data and provide a comprehensive picture of the patient's health status. Despite the potential value of PGHD in healthcare, its implementation currently remains limited due to the clinicians' concern with the reliability and accuracy of the gathered data to support decision-making and concerns regarding the added workload that PGHD might cause to clinical workflow. The main objective of the study was to investigate the clinicians' perspectives towards the use of PGHD for IHD management, focusing on data sharing, interpretation, and efficiency in decision-making. The study consists of semi-structured interviews with seven clinicians. Study results identified four main themes: data generation, data integration, data presentation, data interpretation and utilization in clinical decision-making.
The current research includes a psychometric test of a nursing home (NH) health information technology (HIT) maturity survey and staging model. NHs were assembled based on HIT survey scores from a prior study representing NHs with low (20%), medium (60%), and high (20%) HIT scores. Inclusion criteria were NHs that completed at least two annual surveys over 4 years. NH administrators were excluded who participated in the Delphi panel responsible for instrument recommendations. Recruitment occurred from January to May 2019. Administrators from 121 of 429 facilities completed surveys. NHs were characteristically for-profit, medium bed size, and metropolitan. A covariance matrix demonstrated that all dimensions and domains were significantly correlated, except HIT capabilities and integration in administrative activities. Cronbach's alpha was very good (0.86). Principal component analysis revealed all items loaded intuitively onto four components, explaining 80% variance. The HIT maturity survey and staging model can be used to assess nine dimensions and domains, total HIT maturity, and stage, leading to reliable assumptions about NH HIT. [Research in Gerontological Nursing, 15(2), 93-99.].
Information Technology , Medical Informatics , Humans , Nursing Homes , Psychometrics , Surveys and Questionnaires
A controlled pilot study was performed to evaluate implementation of a medication identification device intended to reduce errors in nursing homes. Naïve observation was used for data collection of medication errors on an intervention unit using the device and a control unit, along with field notes describing observation details. Ten staff were observed administering medications to 70 residents over the study time-frame. Of the 9,099 medication administrations observed (n = 4,588 intervention; n = 4,511 control), 1,068 (12%) errors were identified. The intervention unit had fewer non-time errors versus the control unit, including dose (n = 21 vs. n = 59; p < 0.01), drug (n = 4 vs. n = 21; p <0.01), route (n = 0 vs. n = 4; p < 0.01), and given without order (n = 1 vs. n = 8; p < 0.01). However, time errors were higher on the intervention unit and were often due to late start and interruptions. Non-time errors were due to reliance on memory and nursing judgment. A combination of technology and staff dedicated solely to medication administration likely affected error rate differences. [Journal of Gerontological Nursing, 48(4), 5-11.].
Medication Errors , Nursing Care , Humans , Medication Errors/prevention & control , Nursing Homes , Pilot Projects , Research Design
BACKGROUND: Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification. OBJECTIVE: The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model. METHODS: Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data. RESULTS: Among the 10 feature extractors explored in this study, n-gram, prefix-suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200. CONCLUSION: Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.
Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.
BACKGROUND: Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. OBJECTIVE: This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. METHODS: Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. RESULTS: The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. CONCLUSIONS: This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs.
Antineoplastic Agents , Mobile Applications , Neoplasms , Antineoplastic Agents/adverse effects , Artificial Intelligence , Bayes Theorem , Decision Trees , Humans , Nausea/chemically induced , Neoplasms/drug therapy , Quality of Life , Retrospective Studies , Smartphone , Vomiting/chemically induced , Vomiting/drug therapy
Life history calendars (LHCs) are able to capture large-scale retrospective quantitative data, which can be utilized to learn about transitions of behavior change over time. The Testing and Risk History Calendar (TRHC) is a version of life history calendar (LHC) which correlates critical social, sexual and health variables with the timing of HIV testing. In order to fulfill the need for time-bound data regarding HIV testing and risk of older persons in South Africa, a pilot of the TRHC was performed using a paper fold-out grid format. Though the TRHC study in this format was effective as older persons were able to recall details about their HIV testing and risk contexts, the interview process was tedious as data were collected manually. Development of a tablet application for TRHC study will improve data quality and make data entry and collection more automated. This paper presents the development of the TRHC application prototype in order to collect TRHC data electronically and provides a platform for efficient large-scale life history calendar data collection.
BACKGROUND: Studies have reported that patient-related factors significantly impact the risk of Chemotherapy-Induced Nausea and Vomiting (CINV). The objective of this study was to analyze those risk factors of CINV through a systematic literature review. METHODS: We searched MEDLINE to identify articles that addressed patient-related risk factors of CINV through clinical studies. RESULTS: A total of 49 articles were selected for this study. A total of 28 patient-related risk-factors that significantly impact the risk of CINV were documented. Three factors are demographically related, 17 factors are intrinsic in nature and innate to patient's physiology or influenced by physiology, and eight factors are extrinsic in nature. At least five studies identified seven risk factors with notable summary odds ratio: history of nausea/vomiting (odds ratio: 3.13, 95% CI 2.40-4.07, p < 0.05), female sex (odds ratio: 2.79, 95% CI 2.26-3.44, p < 0.05), expectancy of CINV (odds ratio: 2.61, 95%CI 1.69-4.02, p < 0.05), younger age (odds ratio: 2.59, 95% CI 2.18-3.07, p < 0.05), anxiety (odds ratio: 2.57, 95% CI 1.94-3.40, p < 0.05), history of morning sickness (odds ratio: 1.97, 95% CI 1.46-2.65, p < 0.05), and low alcohol intake (odds ratio: 1.94, 95% CI 1.68-2.24, p < 0.05). CONCLUSIONS: Oncologists can use these factors prior to the initiation of a chemotherapy regimen to identify patients at risk for CINV, in order to focus on more comprehensive antiemetic treatment options for those high-risk patients. This may enable better outcomes and avoid complications.
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Health information technology capabilities in some healthcare sectors, such as nursing homes, are not well understood because measures for information technology uptake have not been fully developed, tested, validated, or measured consistently. The paper provides a report of the development and testing of a new instrument measuring nursing home information technology maturity and stage of maturity. Methods incorporated a four round Delphi panel composed of 31 nursing home experts from across the nation who reported the highest levels of information technology sophistication in a separate national survey. Experts recommended 183 content items for 27 different content areas specifying the measure of information technology maturity. Additionally, experts ranked each of the 183 content items using an IT maturity instrument containing seven stages (stages 0-6) of information technology maturity. The majority of content items (40% (n = 74)) were associated with information technology maturity stage 4, corresponding to facilities with external connectivity capability. Over 11% of the content items were at the highest maturity stage (Stage 5 and 6). Content areas with content items at the highest stage of maturity are reflected in nursing homes that have technology available for residents or their representatives and used extensively in resident care. An instrument to assess nursing home IT maturity and stage of maturity has important implications for understanding health service delivery systems, regulatory efforts, patient safety and quality of care.
Decision Support Systems, Clinical/trends , Information Technology/trends , Nursing Homes/trends , Quality of Health Care/trends , Humans , Patient Care Planning/trends
BACKGROUND: Cancer remains the second major cause of death in the United States over the last decade. Chemotherapy is a core component of nearly every cancer treatment plan. Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy for cancer patients. Several patient-specific factors affect the risk of CINV. However, none of the guidelines consider those factors. Not all of the patients have the similar emetic risk of CINV. Despite the improvements in CINV management, as many as two-thirds of chemotherapy patients still experience some degree of CINV. As a result, physicians use their personal experiences for CINV treatment, which leads to inconsistent managements of CINV. OBJECTIVE: The overall objective of this study is to improve the prevention of CINV using precise, personalized and evidence-based antiemetic treatment before chemotherapy. In CINV prediction, one of the interesting factors is that CINV has two distinct and complex pathophysiologic phases: acute and delayed. In addition, the risk factors and their associations are different for different emetogenic chemotherapies (e.g., low, moderate, and high). There are six contexts considering the combination of phases and emetogenicity levels. This will require the creation of six different models. Instead, our objective was to describe a single framework named "prediction engine" that can perform prediction query without losing the sensitivity to each context. The prediction engine discovers how the patient-related variables and the emetogenecity of chemotherapy are associated with the risk of CINV for each phase. METHODS: This was a single-center retrospective study. The data were collected by retrospective record review from the electronic medical record system used at the University of Missouri Ellis Fischel Cancer Center. An association rule-based dynamic and context-sensitive Prediction Engine has been developed. Physicians receive feedback about CINV risks of patients from the CINV decision support system based on patient-specific factors. RESULTS: The prediction performance of the system outperformed many popular prediction methods and all the results of CINV risk prediction published in the literature. Best prediction performance was achieved using the rule-ranking approach. The accuracy, sensitivity, and specificity were 87.85 %, 87.54 %, and 88.2 %, respectively. CONCLUSIONS: The system used the patient-specific risk factors for making personalized treatment recommendations for CINV. It solved a real clinical problem that will shorten the gap between clinical practices and evidence-based guidelines for CINV management leading to the practice of personalized and precise treatment recommendation, better life quality of patient, and reduced healthcare cost. The approach presented in this article can be applied to any other clinical predictions.
Antiemetics , Antineoplastic Agents , Neoplasms , Antiemetics/therapeutic use , Antineoplastic Agents/adverse effects , Humans , Nausea/chemically induced , Nausea/drug therapy , Nausea/prevention & control , Neoplasms/drug therapy , Retrospective Studies , Vomiting/chemically induced , Vomiting/drug therapy , Vomiting/prevention & control
OBJECTIVES: We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity. MATERIALS AND METHODS: This study had 2 phases. The purpose of phase I was to develop a preliminary nursing home IT maturity model. Phase II involved 3 rounds of questionnaires administered to a Delphi panel of expert nursing home administrators to evaluate the validity of the nursing home IT maturity model proposed in phase I. RESULTS: All participants (n = 31) completed Delphi rounds 1-3. Over the 3 Delphi rounds, the nursing home IT maturity staging model evolved from a preliminary, 5-stage model (stages 1-5) to a 7-stage model (stages 0-6). DISCUSSION: Using innovative IT to improve patient outcomes has become a broad goal across healthcare settings, including nursing homes. Understanding the relationship between IT sophistication and quality performance in nursing homes relies on recognizing the spectrum of nursing home IT maturity that exists and how IT matures over time. Currently, no universally accepted nursing home IT maturity model exists to trend IT adoption and determine the impact of increasing IT maturity on quality. CONCLUSIONS: A 7-stage nursing home IT maturity staging model was successfully developed with input from a nationally representative sample of U.S. based nursing home experts. The model incorporates 7-stages of IT maturity ranging from stage 0 (nonexistent IT solutions or electronic medical record) to stage 6 (use of data by resident or resident representative to generate clinical data and drive self-management).
Information Technology , Medical Informatics , Nursing Homes , Consensus , Delphi Technique , Nursing Homes/organization & administration , Surveys and Questionnaires , United States
BACKGROUND: PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users' knowledge of information retrieval (IR) functions and their usage in query formulation. OBJECTIVE: The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. METHODS: To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. RESULTS: To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. CONCLUSIONS: Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length.
Data is at the core of any clinical and translational research (CTR). In many studies, the electronic data capture (EDC) method has been found to be more efficient than standard paper-based data collection methods in many aspects, including accuracy, integrity, timeliness, and cost-effectiveness. The objective of this article is to present a secure, web-based EDC system for CTR that has been implemented by the Institute for Clinical and Translational Science (iCATS) at the University of Missouri School of Medicine.
Biomedical Research/organization & administration , Data Collection/methods , Internet , Translational Research, Biomedical/organization & administration , Confidentiality , Humans , User-Computer Interface
University of Missouri (MU) Health Care produces a large amount of digitized clinical data that can be used in clinical and translational research for cohort identification, retrospective data analysis, feasibility study, and hypothesis generation. In this article, the implementation of an integrated clinical research data repository is discussed. We developed trustworthy access-management protocol for providing access to both clinically relevant data and protected health information. As of September 2014, the database contains approximately 400,000 patients and 82 million observations; and is growing daily. The system will facilitate the secondary use of electronic health record (EHR) data at MU to promote data-driven clinical and translational research, in turn enabling better healthcare through research.
Academic Medical Centers/organization & administration , Databases as Topic/organization & administration , Electronic Health Records/organization & administration , Medical Informatics/methods , Translational Research, Biomedical/methods , Humans , Missouri
