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Background: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline., Objective: To develop a speech processing algorithm using routine patient-nurse verbal communication and evaluate its performance when combined with electronic health record (EHR) data in detecting early signs of cognitive decline., Method: We analyzed 125 audio-recorded patient-nurse verbal communication for 47 patients from a major home healthcare agency in New York City. Out of 47 patients, 19 experienced symptoms associated with the onset of cognitive decline. A natural language processing algorithm was developed to extract domain-specific linguistic and interaction features from these recordings. The algorithm's performance was compared against EHR-based screening methods. Both standalone and combined data approaches were assessed using F1-score and area under the curve (AUC) metrics., Results: The initial model using only patient-nurse verbal communication achieved an F1-score of 85 and an AUC of 86.47. The model based on EHR data achieved an F1-score of 75.56 and an AUC of 79. Combining patient-nurse verbal communication with EHR data yielded the highest performance, with an F1-score of 88.89 and an AUC of 90.23. Key linguistic indicators of cognitive decline included reduced linguistic diversity, grammatical challenges, repetition, and altered speech patterns. Incorporating audio data significantly enhanced the risk prediction models for hospitalization and emergency department visits., Discussion: Routine verbal communication between patients and nurses contains critical linguistic and interactional indicators for identifying cognitive impairment. Integrating audio-recorded patient-nurse communication with EHR data provides a more comprehensive and accurate method for early detection of cognitive decline, potentially improving patient outcomes through timely interventions. This combined approach could revolutionize cognitive impairment screening in home healthcare settings., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In a previous study, sepsis was noted as a diagnosis on the home health record only 4% of the time for 165,000 sepsis survivors transitioning from hospital to home health care in America. If sepsis and other conditions are not clearly documented in the transitional care record this can lead to unpreparedness, missed, care, and poor patient outcomes. Our implementation science study discovered a source of this problem regarding the sepsis documentation in 16 hospitals referring to five home care agencies. Together, researchers, hospital, and home care personnel developed and implemented two information technology solutions to address this deficit in seven hospitals. The automated method was more readily adopted and effective in improving information transfer between hospital and home health care.

The use of healthcare information technology (HIT) is vital for storing and exchanging health information during patient transitions, playing a significant role in care coordination for sepsis survivors. The critical role of HIT was evident during the pre-implementation phase of a study to implement an evidence-based protocol supporting the timely transition of sepsis survivors to home health and outpatient care. Through 61 semi-structured interviews involving 91 stakeholders, over half of the 33 identified themes were related to HIT. Notably, electronic health record (EHR) alert systems led to over-capture and alarm fatigue. Efficient information transfer during HHC referral highlighted the need for improved EHR access. The study underscores HIT's importance and potential while emphasizing the need for collaborative policy and interface development to promote effective transitions in care.

Objective: Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model., Methods: During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool., Results: The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%)., Conclusion: Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: To identify the risk factors home healthcare (HHC) clinicians associate with patient deterioration and understand how clinicians respond to and document these risk factors., Methods: We interviewed multidisciplinary HHC clinicians from January to March of 2021. Risk factors were mapped to standardized terminologies (eg, Omaha System). We used directed content analysis to identify risk factors for deterioration. We used inductive thematic analysis to understand HHC clinicians' response to risk factors and documentation of risk factors., Results: Fifteen HHC clinicians identified a total of 79 risk factors that were mapped to standardized terminologies. HHC clinicians most frequently responded to risk factors by communicating with the prescribing provider (86.7% of clinicians) or following up with patients and caregivers (86.7%). HHC clinicians stated that a majority of risk factors can be found in clinical notes (ie, care coordination (53.3%) or visit (46.7%))., Discussion: Clinicians acknowledged that social factors play a role in deterioration risk; but these factors are infrequently studied in HHC. While a majority of risk factors were represented in the Omaha System, additional terminologies are needed to comprehensively capture risk. Since most risk factors are documented in clinical notes, methods such as natural language processing are needed to extract them., Conclusion: This study engaged clinicians to understand risk for deterioration during HHC. The results of our study support the development of an early warning system by providing a comprehensive list of risk factors grounded in clinician expertize and mapped to standardized terminologies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Clinicians' perspectives on the electronic health records (EHR) in home healthcare (HHC) are understudied. To explore this topic, qualitative interviews were conducted with 15 HHC clinicians in the Northeastern USA. Thematic analysis was conducted to identify key themes emerging from the interviews. While some EHR benefits were recognized, overall satisfaction with the EHR was low. The results suggest EHR limitations are tied to poor usability, restrictions, and redundancy in documentation leading to increased documentation workload. Clinicians have recommendations to mitigate these limitations via additional EHR functions and better patient risk detection. Future stakeholders should consider the results of this study when developing and updating the EHR in HHC.

Background: Electronic health record (EHR) usability issues represent an emerging threat to the wellbeing of nurses and patients; however, few large studies have examined these relationships., Objective: To examine associations between EHR usability and nurse job (burnout, job dissatisfaction, and intention to leave) and surgical patient (inpatient mortality and 30-day readmission) outcomes., Methods: A cross-sectional analysis of linked American Hospital Association, state patient discharge, and nurse survey data was conducted. The sample included 343 hospitals, 1,281,848 surgical patients, and 12,004 nurses. Logistic regression models were used to assess relationships between EHR usability and outcomes, before and after accounting for EHR adoption level (comprehensive vs. basic or less) and other confounders., Results: In fully adjusted models, nurses who worked in hospitals with poorer EHR usability had significantly higher odds of burnout [odds ratio (OR), 1.41; 95% confidence interval (CI), 1.21-1.64], job dissatisfaction (OR, 1.61; 95% CI, 1.37-1.90) and intention to leave (OR, 1.31; 95% CI, 1.09-1.58) compared with nurses working in hospitals with better usability. Surgical patients treated in hospitals with poorer EHR usability had significantly higher odds of inpatient mortality (OR, 1.21; 95% CI, 1.09-1.35) and 30-day readmission (OR, 1.06; 95% CI, 1.01-1.12) compared with patients in hospitals with better usability. Comprehensive EHR adoption was associated with higher odds of nurse burnout (OR, 1.14; 95% CI, 1.01-1.28)., Conclusion: Employing EHR systems with suboptimal usability was associated with higher odds of adverse nurse job outcomes and surgical patient mortality and readmission. EHR usability may be more important to nurse job and patient outcomes than comprehensive EHR adoption., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background:  The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES:  Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems., Methods:  This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time., Results:  Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research., Conclusion:  We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users., Competing Interests: T.S.C. reports grants from ACRIN, NIH, ACR, and RSNA, as well as royalties from the Osler Institute for lectures in 2013, outside the submitted work. D.S.H. reports grants and nonfinancial support from Roche Diagnostics, outside the submitted work. R.A.H. reports grants from Johnson & Johnson, Merck, and Pfizer, outside the submitted work. Q.L. reports grants from NIH, during the conduct of the study; grants from Pfizer and Bayer, outside the submitted work., (Thieme. All rights reserved.)

Objective: Patient transitions into home health care (HHC) often occur without the transfer of information needed for critical clinical decisions and the plan of care. Owing to a lack of universally implemented standards, there is wide variation in information transfer. We sought to characterize missing information at HHC admission., Materials and Methods: We conducted a mixed methods study with 3 diverse HHC agencies. Focus groups with nurses at each agency identified what information supports patient care decisions at admission. Thirty-six in-home admissions with associated documentation review determined the available information. To inform information standards development for the HHC admission process, we compared the types of information desired and available to an international standard for transitions in care information, the Continuity of Care Document (CCD) enhanced with Office of the National Coordinator for Healthcare Information Technology summary terms (CCD/S)., Results: Three-quarters of the items from the focus groups mapped to the CCD/S. Regarding available information at admission, no observation included all CCD/S data items. While medication information was needed and often available for 4 important decisions, concepts related to patient medication self-management appeared in neither the CCD/S nor the admission documentation., Discussion: The CCD/S mostly met HHC nurses' information needs and is recommended to begin to fill the current information gap. Electronic health record recommendations include use of a data standard: the CCD or the proposed, more parsimonious U.S. Core Data for Interoperability., Conclusions: Referral source and HHC agency adoption of data standards is recommended to support structured, consistent data and information sharing., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar terms for patient fall history from homecare visit notes (N = 1 149 586) extracted from a large US homecare agency. Several experiments with parameters of word embedding models were conducted to identify the most time-effective and high-quality model. Models with larger word window width sizes (n = 10) that present users with about 50 top potentially similar terms for each (true) term validated by the user were most effective. NimbleMiner can assist in building a thorough vocabulary of fall history terms in about 2 hours. For domains like nursing, this approach could offer a valuable tool for rapid lexicon enrichment and discovery.

Background: Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components., Objective: This manuscript describes the general system architecture and user Interface and presents results of a case study aimed at classifying fall-related information (including fall history, fall prevention interventions, and fall risk) in homecare visit notes., Methods: We extracted a corpus of homecare visit notes (n = 1,149,586) for 89,459 patients from a large US-based homecare agency. We used a gold standard testing dataset of 750 notes annotated by two human reviewers to compare the NimbleMiner's ability to classify documents regarding whether they contain fall-related information with a previously developed rule-based NLP system., Results: NimbleMiner outperformed the rule-based system in almost all domains. The overall F- score was 85.8% compared to 81% by the rule based-system with the best performance for identifying general fall history (F = 89% vs. F = 85.1% rule-based), followed by fall risk (F = 87% vs. F = 78.7% rule-based), fall prevention interventions (F = 88.1% vs. F = 78.2% rule-based) and fall within 2 days of the note date (F = 83.1% vs. F = 80.6% rule-based). The rule-based system achieved slightly better performance for fall within 2 weeks of the note date (F = 81.9% vs. F = 84% rule-based)., Discussion & Conclusions: NimbleMiner outperformed other systems aimed at fall information classification, including our previously developed rule-based approach. These promising results indicate that clinical text mining can be implemented without the need for large labeled datasets necessary for other types of machine learning. This is critical for domains with little NLP developments, like nursing or allied health professions., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Despite evidence suggesting higher quality and safer care in hospitals with comprehensive electronic health record (EHR) systems, factors related to advanced system usability remain largely unknown, particularly among nurses. Little empirical research has examined sociotechnical factors, such as the work environment, that may shape the relationship between advanced EHR adoption and quality of care., Objective: The objective of this study was to examine the independent and joint effects of comprehensive EHR adoption and the hospital work environment on nurse reports of EHR usability and nurse-reported quality of care and safety., Methods: This study was a secondary analysis of nurse and hospital survey data. Unadjusted and adjusted logistic regression models were used to assess the relationship between EHR adoption level, work environment, and a set of EHR usability and quality/safety outcomes. The sample included 12,377 nurses working in 353 hospitals., Results: In fully adjusted models, comprehensive EHR adoption was associated with lower odds of nurses reporting poor usability outcomes, such as dissatisfaction with the system (odds ratio [OR]: 0.75; 95% confidence interval [CI]: 0.61-0.92). The work environment was associated with all usability outcomes with nurses in better environments being less likely to report negatively. Comprehensive EHRs (OR: 0.83; 95% CI: 0.71-0.96) and better work environments (OR: 0.47; 95% CI: 0.42-0.52) were associated with lower odds of nurses reporting fair/poor quality of care, while poor patient safety grade was associated with the work environment (OR: 0.50; 95% CI: 0.46-0.54), but not EHR adoption level., Conclusion: Our findings suggest that adoption of a comprehensive EHR is associated with more positive usability ratings and higher quality of care. We also found that-independent of EHR adoption level-the hospital work environment plays a significant role in how nurses evaluate EHR usability and whether EHRs have their intended effects on improving quality and safety of care., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

Home care nurses have multiple goals at the patient admission visit. Electronic health records support some of these goals, including high-quality documentation, but nurses may not complete the electronic documentation at the point of care. To characterize admission nurses' practices at the point of care and lay the foundation for design recommendations, this study investigates admission nurses' documentation strategies with respect to entering electronic data and how nursing goals affect them. We conducted 10 observations of home care agency admissions with five admission nurses in rural Pennsylvania. We collected screenshots and recorded the admission process. We asked the nurses questions outside the point of care. We coded the nurses' strategies at the data-entry screen level. Using thematic analysis, we investigated the influence of nursing goals on documentation strategies. Subject matter experts reviewed our findings. Several goals affect nurses' documentation strategies: ensure data accuracy, reduce time in the patient's home, and prevent infection. Home care admission nurses distribute the electronic documentation temporally due to their goals. Nurses developed memory aids to support completion of the documentation after leaving the patients' homes. Design and training should support the distributed manner in which home care nurses document patient encounters.

Objective: Build and validate a clinical decision support (CDS) algorithm for discharge decisions regarding referral for post-acute care (PAC) and to what site of care. Materials and Methods: Case studies derived from EHR data were judged by 171 interdisciplinary experts and prediction models were generated. Results: A two-step algorithm emerged with area under the curve (AUC) in validation of 91.5% (yes/no refer) and AUC 89.7% (where to refer). Discussion: CDS for discharge planning (DP) decisions may remove subjectivity, and variation in decision-making. CDS could automate the assessment process and alert clinicians of high need patients earlier in the hospital stay. Conclusion: Our team successfully built and validated a two-step algorithm to support discharge referral decision-making from EHR data. Getting patients the care and support they need may decrease readmissions and other adverse events. Further work is underway to test the effects of the CDS on patient outcomes in two hospitals.

Background: Eliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges., Objectives: Describe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts' referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline., Methods: De-identified case studies, developed from electronic health records (EHR), contained a comprehensive description of 1,496 acute care inpatients. In teams of three, physicians, nurses, social workers, and physical therapists reviewed case studies and assessed the need for post-acute care referrals; Delphi rounds followed when team members did not agree. Generalized estimating equations (GEEs) compared experts' decisions by discipline, region of the country and to the decisions made by study hospital clinicians, adjusting for the repeated observations from each expert and case. Frequencies determined the most common case characteristics chosen as important by the experts., Results: The experts recommended referral for 80% of the cases; the actual discharge disposition of the patients showed referrals for 67%. Experts from the Northeast and Midwest referred 5% more cases than experts from the West. Physicians and nurses referred patients at similar rates while both referred more often than social workers. Differences by discipline were seen in the factors identified as important to the decision., Conclusion: The method for eliciting expert knowledge enabled national dispersed expert clinicians to anonymously review case summaries and make decisions about post-acute care referrals. Having time and a comprehensive case summary may have assisted experts to identify more patients in need of post-acute care than the hospital clinicians. The methodology produced the data needed to develop an expert decision support system for discharge planning.

Heart failure is a complex condition with a significant impact on patients' lives. A few studies have identified risk factors associated with rehospitalization among telehomecare patients with heart failure using logistic regression or survival analysis models. To date, there are no published studies that have used data mining techniques to detect associations with rehospitalizations among telehomecare patients with heart failure. This study is a secondary analysis of the home healthcare electronic medical record called the Outcome and Assessment Information Set-C for 552 telemonitored heart failure patients. Bivariate analyses using SAS and a decision tree technique using Waikato Environment for Knowledge Analysis were used. From the decision tree technique, the presence of skin issues was identified as the top predictor of rehospitalization that could be identified during the start of care assessment, followed by patient's living situation, patient's overall health status, severe pain experiences, frequency of activity-limiting pain, and total number of anticipated therapy visits combined. Examining risk factors for rehospitalization from the Outcome and Assessment Information Set-C database using a decision tree approach among a cohort of telehomecare patients provided a broad understanding of the characteristics of patients who are appropriate for the use of telehomecare or who need additional supports.

The re-hospitalization rate of homecare patients within 60 days of hospital discharge is 30%. Enhanced care planning based on better information may reduce this rate. However, very little is known about the homecare admission and care planning processes. The research team collected data during observations of three nursing visits to admit homecare patients in Camden NJ, and conducted thematic content analysis on these data. Human factors methods helped to identify nurse decision-making related to selection of the plan of care problems, non-nursing resources, and the nursing visit pattern. They identified how the electronic health record (EHR) assisted the nurse in visit pattern frequency decisions. Major themes that emerged included reduced efficiency due to use of redundant intra-team communication methods to augment EHR documentation, redundant documentation, and workarounds and reorganization of clinical workflow.

We assessed the Health Information Technology (HIT) Reference-based Evaluation Framework (HITREF) comprehensiveness in two HIT evaluations in settings different from that in which the HITREF was developed. Clinician satisfaction themes that emerged from clinician interviews in the home care and the hospital studies were compared to the framework components. Across both studies, respondents commented on 12 of the 20 HITREF components within 5 of the 6 HITREF concepts. No new components emerged that were missing from the HITREF providing evidence that the HITREF is a comprehensive framework. HITREF use in a range of HIT evaluations by researchers new to the HITREF demonstrates that it can be used as intended. Therefore, we continue to recommend the HITREF as a comprehensive, research-based HIT evaluation framework to increase the capacity of informatics evaluators' use of best practice and evidence-based practice to support the credibility of their findings for fulfilling the purpose of program evaluation.

Electronic health records (EHRs), intended to improve the clinical process, are understudied in home care. The researchers assessed clinician satisfaction, informed by workflow and patient outcomes, to identify EHR adoption challenges. The mixed methods study setting was a Philadelphia agency with 137 clinicians. Adoption challenges included: (a) hardware problems coupled with lack of field support; (b) inadequate training; and (c) mismatch of EHR usability/functionality and workflow resulting in decreased efficiency. Adoption facilitators were support for team communication and improved clinical data timeliness. Opportunities for improved adoption included sharing with front-line clinicians EHR data related to patient care and health outcomes.

We conducted three evaluation studies in community and hospital settings to examine point-of-care documentation system adoption among interdisciplinary care team clinicians. In the community settings, quantitative methods included documentation time-to-completion and a clinician satisfaction survey. Qualitative methods included observations and follow-up interviews. Qualitative data and quantitative data were merged comparing findings along themes. In the hospitals, qualitative scenario testing results indicated clinician system adoption was universal, though not always timely. At all sites, mismatch between system functionality and workflow was a barrier to clinician system access during patient care and reduced clinician efficiency. Clinicians at all settings were satisfied with their ability to access other clinicians' notes, without increased interdisciplinary team communication. Clinicians did not identify any systems impact on patient outcomes. To facilitate adoption, clinicians should see the value of using the system as intended by receiving system data feedback that shows improvement of patient care and patient safety.

Administrators play a major role in choosing and managing the use of the electronic health record (EHR). The documentation policies and EHR changes enacted or approved by administrators affect the ability to use clinical data for research. This article illustrates the challenges that can be avoided through awareness of the consequences of customization, variations in documentation policies and quality, and user interface features. Solutions are posed that assist administrators in avoiding these challenges and promoting data harmonization for research and quality improvement.

Data from homecare electronic health records were used to explore the association of patient characteristics with re-hospitalizations of patients with heart failure (HF) during a 60-day period of telemonitoring following hospital discharge. Data from 403 Medicare patients with HF who had used telehealth from 2008 to 2010 were analysed. There were 112 all-cause (29%) and 73 cardiac-related (19%) re-hospitalizations within 60 days of the start of telemonitoring. In adjusted analyses, the patients' number of medications and type of cardiac medications were significantly (P < 0.05) associated with an increased risk of re-hospitalization. After stratifying the sample by illness severity, age and gender, other significant (P < 0.05) predictors associated with an increased risk of all-cause and cardiac re-hospitalization were psychiatric co-morbidity, pulmonary and obesity co-morbidities within gender, beta blocker prescription in females and primary HF diagnosis in the oldest age stratum. The study's findings may assist homecare agencies seeking to allocate resources without compromising patient care.

We conducted three health care evaluation studies in community and hospital settings to examine adoption of point-of-care documentation systems among interdisciplinary care team clinicians. Both community studies used a mixed methods design to assess actual system usage and clinician satisfaction. In the hospitals, scenario testing was used. Results indicated clinician adoption of the systems was universal, although not always timely with: (1) a mismatch between system functionality and workflow which was a barrier to clinician system access during patient care and reduced clinician efficiency; (2) no increase in interdisciplinary team communication; and (3) no impact on patient outcomes identified by clinicians. To facilitate adoption, clinicians should see the value of using the system as intended by receiving patient care and patient safety feedback that uses system data.

This multimethod study measured the impact of an electronic health record (EHR) on clinician satisfaction with clinical process. Subjects were 39 clinicians at a Program of All-inclusive Care for Elders (PACE) site in Philadelphia utilizing an EHR. Methods included the evidence-based evaluation framework, Health Information Technology Research-Based Evaluation Framework, which guided assessment of clinician satisfaction with surveys, observations, follow-up interviews, and actual EHR use at two points in time. Mixed-methods analysis of findings provided context for interpretation and improved validity. The study found that clinicians were satisfied with the EHR; however, satisfaction declined between time periods. Use of EHR was universal and wide and was differentiated by clinical role. Between time periods, EHR use increased in volume, with increased timeliness and decreased efficiency. As the first EHR evaluation at a PACE site from the perspective of clinicians who use the system, this study provides insights into EHR use in the care of older people in community-based healthcare settings.

A new survey instrument was developed and validated to measure clinician (nurse) satisfaction with electronic health record impact on clinical process. The Health Information Technology Reference-Based Evaluation Framework guided the selection of evaluation dimensions for the survey. Survey questions were gathered from existing health information technology satisfaction surveys that reflected individual evaluation concepts, such as efficiency or benefits. Decisions about data-gathering methods (e.g., item selection) were made based on reviews of literature and surveys of clinician satisfaction with health information technology and expert input. Preliminary instrument validation was accomplished using qualitative and statistical analysis of five repeated sets of responses from clinicians at the pilot site and field administrations repeated twice at electronic health record implementation and paper-based comparison sites and by analyzing convergent evidence from observations and interviews. Reliability was assessed on one sample: 30 graduate nursing students at the single pilot site. Validity was assessed on three separate samples: (1) graduate nursing students (n = 30), (2) field test at a site with electronic health record (n = 39 participants), and (3) field test at a paper-based site (n = 17). The implementation and comparison sites are Program of All-Inclusive Care for the Elderly that provide managed day care for frail elderly. Survey responses were assessed for test-retest reliability, internal consistency, and content and construct validity. The instrument design enables its administration before and after electronic health record implementation. Work to date suggests the instrument is reliable and valid; it is offered to electronic health record evaluators for further testing and application.

Meaningful use has become ubiquitous in the vocabulary of health information technology. It suggests that better healthcare does not result from the adoption of technology and electronic health records, but by increasing interoperability and informing clinical decisions at the point of care. Although the initial application of meaningful use was limited to eligible professionals and hospitals, it incorporates complex processes and workflow that involve all nurses, other healthcare practitioners, and settings. The healthcare community will become more integrated, and interdisciplinary practitioners will provide enhanced patient-centered care if electronic health records adopt the priorities of meaningful use. Standardized terminologies are a necessary component of such electronic health records. The Omaha System is an exemplar of a standardized terminology that enables meaningful use of clinical data to support and improve patient-centered clinical practice, education, and research. It is user-friendly, generates data that can be shared with patients and their families, and enables healthcare providers to analyze and exchange patient-centered coded data. Use of the Omaha System is increasing steadily in diverse practice, education, and research settings nationally and internationally.

Objectives Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows. Materials and Methods We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1–15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F 1, and area under the receiver operating curve (AUC). Results The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F 1 = 0.69). Discussion and Conclusion This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions. [ABSTRACT FROM AUTHOR]

• Patients with HF who have poor self-management can be identified from narrative notes using NLP. • Younger age, gender (male), smoking, and comorbid conditions are associated with poor self-management. • Knowing which patient factors are associated with poor self-management may assist HHC clinicians in developing individualized care plans. Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management. To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management. An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management. There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976–0.987, p <.001), longer length of stay in HHC (OR 1.036, 95% CI 1.029– 1.043, p <.001), diagnosis of diabetes (OR 1.47, 95% CI 1.3–1.67, p <.001) and depression (OR 1.36, 95% CI 1.09–1.68, p <.01), impaired decision-making (OR 1.64, 95% CI 1.37–1.95, p <.001), smoking (OR 1.7, 95% CI 1.4–2.04, p <.001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1–1.42, p <.01) were associated with poor self-management. Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes. [ABSTRACT FROM AUTHOR]

During home health care (HHC) admissions, nurses provide input into decisions regarding the skilled nursing visit frequency and episode duration. This important clinical decision can impact patient outcomes including hospitalization. Episode duration has recently gained greater importance due to the Centers for Medicare and Medicaid Services (CMS) decrease in reimbursable episode length from 60 to 30 days. We examined admissions nurses' visit pattern decision-making and whether it is influenced by documentation available before and during the first home visit, agency standards, other disciplines being scheduled, and electronic health record (EHR) use. This observational mixed-methods study included admission document analysis, structured interviews, and a think-aloud protocol with 18 nurses from 3 diverse HHC agencies (6 at each) admitting 2 patients each (36 patients). Findings show that prior to entering the home, nurses had an information deficit; they either did not predict the patient's visit frequency and episode duration or stated them based on experience with similar patients. Following patient interaction in the home, nurses were able to make this decision. Completion of documentation using the EHR did not appear to influence visit pattern decisions. Patient condition and insurance restrictions were influential on both frequency and duration. Given the information deficit at admission, and the delay in visit pattern decision making, we offer health information technology recommendations on electronic communication of structured information, and EHR documentation and decision support. [ABSTRACT FROM AUTHOR]

Illustrate patterns of patient problem information received and documented across the home health care (HHC) admission process and offer practice, policy, and health information technology recommendations to improve information transfer. Observational field study. Three diverse HHC agencies using different commercial point-of-care electronic health records (EHRs). Six nurses per agency each admitted 2 patients (36 total). Researchers observed the admission process and photographed documents and EHR screens across 3 phases: referral, assessment, and plan of care (POC). To create a standardized data set, we mapped terms within medical diagnoses, signs, symptoms, and Problems to 5 of the 42 Omaha System Problem Classification Scheme problem terms. This created 180 problem pattern cases (5 problem patterns per patient). Each pattern of problem information being present or absent was observed. In 52 cases (28.9%), a problem did not appear. In 36 cases (20%), the problem appeared in all 3 phases. In 46 cases (25.6%), the problem appeared in referral and/or assessment phases and not on the POC. Conversely, in 37 cases (20.5%), the problem appeared in referral or assessment phases and on the POC. In 9 cases (5%), the problem only appeared on the POC. Within the EHRs, there were no rationale fields to clarify including Problems or not and no problem status fields to identify active, resolved, or potential ones. Diagnosis or problem information transferred from the referral source or gathered during an in-home assessment did not appear in the POC. Because of the EHR structure, clinicians could not identify inactive problem or problem priority. Documentation or mapping of a structured problem list using a standardized interprofessional terminology such as the Omaha System coupled with identification of rationale could support the documentation of problem status and priority and reduce information loss. [ABSTRACT FROM AUTHOR]

In a quasi-experimental study, decision support software was installed in three hospitals to study the ability to scale (spread) its use from one hospital on paper to three hospitals as software, and to examine the effect on 30- and 60-day readmissions. The Discharge Decision Support System (D2S2) software analyzes data collected by nurses on admission with a proprietary risk assessment tool, identifies patients in need of post-acute care, and alerts discharge planners. On six intervention units, with a concurrent comparison group of 76 units, we examined the implementation experience and compared readmission outcomes before and after implementation. The software implementation finished one month ahead of schedule, and the software performed reliably. High-risk patients admitted in the experimental phase after implementation of D2S2 decision support had significantly fewer 30-day readmissions (a decrease from 22.2% to 9.4%). When high- and low-risk patients were analyzed together, D2S2 achieved a 33% relative reduction in 30-day readmissions (13.1 to 8.8%) and sustained a 37% relative reduction at 60 days. The software, available commercially through RightCare Solutions, was adopted by the health system and remains in use after 22 months. The D2S2 risk assessment tool can be installed easily in existing EHR systems. Future research will focus on how the tool influences discharge decision-making and how its accuracy can be improved in specific settings. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The author highlights examples of the latest research led by nurse scientists using health information technology (HIT) to improve patient safety, quality and efficiency of patient care. Topics discussed include opportunities for gerontological nurse scientists interested in merging care of older adults and HIT, how the effective use of electronic health records (EHRs) can improve outcomes and the challenges faced by EHR research such as the inability to conduct randomized clinical trials.

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Background Methods Results Conclusions Clinical Relevance The healthcare industry increasingly values high‐quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real‐time artificial intelligence (AI)‐based CDS tool.We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.This study discusses the creation of a time‐series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF.This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day‐to‐day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations. [ABSTRACT FROM AUTHOR]

Characterize the work that home health care (HHC) admission nurses complete as part of the medication reconciliation tasks, explore the impact of shared electronic medication data (interoperability) from the referral source on medication reconciliation, and highlight opportunities to enhance medication reconciliation with respect to transition in care to HHC agencies. Observational field study. Three diverse Pennsylvania HHC agencies; each used different electronic health record systems with different interoperability characteristics. Six nurses per site admitted 2 patients each (36 patients total). Researchers observed the admission process in the patient home and at the HHC agency. The nurses' tasks related to medication reconciliation were characterized by (1) number and change types (ie, medications dropped or added; changes to dose, frequency/administration time, or tablet types) made to the referrer medication list during and after the home visit, and (2) reasons that the nurse called the health provider (doctor, pharmacy) to resolve medication-related issues. Differences between interoperable and non-interoperable observations were explored. Polypharmacy (on average, study patients were taking more than 12 medications) and high-risk medications (on average, more than 8 per patient) were pervasive. For 91% of patients, the number of medications decreased between pre- and post-reconciliation medication lists; 41% of the medications required changes. Nurses using interoperable systems needed to make fewer changes than nurses using non-interoperable systems. In two-thirds of observations, the nurse called a provider. Changes to the referrer medication list and calls to providers highlighted the nurses' effort to complete the medication reconciliation. Interoperability appeared to reduce the number of changes required, but did not eliminate changes or calls to providers. We highlight opportunities to enhance medication reconciliation with respect to transition in care to HHC agencies. [ABSTRACT FROM AUTHOR]

Abstract Objectives To evaluate the quality of communication between hospitals and home health care (HHC) clinicians and patient preparedness to receive HHC in a statewide sample of HHC nurses and staff. Design A web-based 48-question cross-sectional survey of HHC nurses and staff in Colorado to describe the quality of communication after hospital discharge and patient preparedness to receive HHC from the perspective of HHC nurses and staff. Questions were on a Likert scale, with optional free-text questions. Setting and participants Between January and June 2017, we sent a web-based survey to individuals from the 56 HHC agencies in the Home Care Association of Colorado that indicated willingness to participate. Results We received responses from 50 of 122 individuals (41% individual response rate) representing 14 of 56 HHC agencies (25% agency response rate). Half of the respondents were HHC nurses, the remainder were managers, administrators, or quality assurance clinicians. Among respondents, 60% (n = 30) reported receiving insufficient information to guide patient management in HHC and 44% (n = 22) reported encountering problems related to inadequate patient information. Additional tests recommended by hospital clinicians was the communication domain most frequently identified as insufficient (58%). More than half of respondents (52%) indicated that patient preparation to receive HHC was inadequate, with patient expectations frequently including extended-hours caregiving, housekeeping, and transportation, which are beyond the scope of HHC. Respondents with electronic health record (EHR) access for referring providers were less likely to encounter problems related to a lack of information (27% vs 57% without EHR access, P =.04). Respondents with EHR access were also more likely to have sufficient information about medications and contact isolation. Conclusions/Implications Communication between hospitals and HHC is suboptimal, and patients are often not prepared to receive HHC. Providing EHR access for HHC clinicians is a promising solution to improve the quality of communication. [ABSTRACT FROM AUTHOR]

Highlights: [•] Poor usability of interface limits ability to capitalize on available information. [•] Design flaws in NIS can be addressed with human–computer interaction literature. [•] Scenario-based testing is feasible and informative to redesign of NIS interfaces. [•] Study methods provide feedback to inform work practice and system use. [ABSTRACT FROM AUTHOR]

Background Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care. Objective This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes. Methods and design First, two human annotators manually reviewed a purposeful training sample (n = 360) and random test sample (n = 1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data. Results The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system’s accuracy = 92.7%), with best results for wound treatment (F-measure = 95.7%) and poorest results for wound size (F-measure = 81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis. Conclusions The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application’s information coverage to other relevant wound factors and validation of the model with external data. [ABSTRACT FROM AUTHOR]