Using the Nursing Interventions Classification to identify nursing interventions in free-text nursing documentation in adult psychiatric outpatient care setting

Aims and objectives To identify and describe nursing interventions in patient documentation in adult psychiatric outpatient setting and to explore the potential for using the Nursing Interventions Classification in documentation in this setting. Background Documentation is an important part of nurses' work, and in the psychiatric outpatient care setting, it can be time-consuming. Only very few research reports are available on nursing documentation in this care setting. Methods A qualitative analysis of secondary data consisting of nursing documentation for 79 patients in four outpatient units (years 2016-2017). The data consisted of 1,150 free-text entries describing a contact or an attempted contact with 79 patients, their family members or supporting networks and 17 nursing care summaries. Deductive and inductive content analysis was used. SRQR guideline was used for reporting. Results We identified 71 different nursing interventions, 64 of which are described in the Nursing Interventions Classification. Surveillance and Care Coordination were the most common interventions. The analysis revealed two perspectives which challenge the use of the classification: the problem of overlapping interventions and the difficulty of naming group-based interventions. Conclusion There is an urgent need to improve patient documentation in the adult psychiatric outpatient care setting, and standardised nursing terminologies such as the Nursing Interventions Classification could be a solution to this. However, the problems of overlapping interventions and naming group-based interventions suggest that the classification needs to be further developed before it can fully support the systematic documentation of nursing interventions in the psychiatric outpatient care setting. Relevance to clinical practice This study describes possibilities of using a systematic nursing language to describe the interventions nurses use in the adult psychiatric outpatient setting. It also describes problems in the current free text-based documentation.Peer reviewe

HILT : High-Level Thesaurus Project. Phase IV and Embedding Project Extension : Final Report

Ensuring that Higher Education (HE) and Further Education (FE) users of the JISC IE can find appropriate learning, research and information resources by subject search and browse in an environment where most national and institutional service providers - usually for very good local reasons - use different subject schemes to describe their resources is a major challenge facing the JISC domain (and, indeed, other domains beyond JISC). Encouraging the use of standard terminologies in some services (institutional repositories, for example) is a related challenge. Under the auspices of the HILT project, JISC has been investigating mechanisms to assist the community with this problem through a JISC Shared Infrastructure Service that would help optimise the value obtained from expenditure on content and services by facilitating subject-search-based resource sharing to benefit users in the learning and research communities. The project has been through a number of phases, with work from earlier phases reported, both in published work elsewhere, and in project reports (see the project website: http://hilt.cdlr.strath.ac.uk/). HILT Phase IV had two elements - the core project, whose focus was 'to research, investigate and develop pilot solutions for problems pertaining to cross-searching multi-subject scheme information environments, as well as providing a variety of other terminological searching aids', and a short extension to encompass the pilot embedding of routines to interact with HILT M2M services in the user interfaces of various information services serving the JISC community. Both elements contributed to the developments summarised in this report

Applicability of the nursing interventions classification in the psychiatric outpatient care setting

Standardized nursing terminologies (SNT) have been developed to describe the nursing process systematically. The aim of this research was to study the applicability of the Nursing Interventions Classification (NIC) in the psychiatric outpatient care setting in Finland. The research includes three phases. In the first phase using an integrative literature review we identified nursing interventions in research publications (n=60) and used the NIC to analyze the identified interventions. In the second phase, we used an ethnographically oriented work-place study to identify interventions in the clinical setting. This included observations and interviews and the findings were analyzed together with nurses (n=17). The core interventions were identified using the Delphi method. The panelists consisted of nurses and nurse managers (round one n=54, round two n=26). In the third phase we identified nursing interventions in nursing progress notes (n=1150) and in nursing care summaries (n=17) and mapped these into the NIC. In all we identified 105 different nursing interventions, of which 95% could be mapped into the NIC. The emphasis was in interventions aiming at behavioral change and more specifically interventions that support coping by building on patients’ strengths. In nursing documentation, the most frequent interventions were Surveillance and Care Coordination. The group delivery method was common in all phases. The findings of this study emphasize the need for a systematic terminology to describe nursing interventions for nurses to conceptualize their work, to make the work visible and to ensure the quality of nursing documentation. The broad coverage, descriptiveness of the interventions and the taxonomical structure of the NIC support its applicability. However, the interventions in the classification were found to be overlapping which limits the systematic transfer of information and the possibilities for secondary use of data. Additional limitations are the lack of semantic coherence with the concepts used in research and the difficulty of describing interventions delivered using the group method. This research generated recommendations for the development of the classification. The most central ones include the need to include multiple methods in the research and development and the integration of concepts used in research literature.Hoitotyön interventioiden luokituksen soveltuvuus aikuispsykiatrian avohoitoon Hoitotyön systemaattinen kuvaaminen edellyttää yhteisen kielen ja käsitteistöjen käyttöä. Tässä tutkimuksessa selvitetään hoitotyön interventioiden luokituksen (Nursing Interventions Classification, NIC) soveltuvuutta aikuispsykiatrian avohoitoon. Tutkimus koostuu kolmesta osavaiheesta. Ensimmäisessä vaiheessa integratiivisen kirjallisuuskatsauksen avulla tutkimuksista (n=60) tunnistettiin hoitotyön interventioita ja nämä analysoitiin NIC-luokituksen avulla. Toisessa vaiheessa hyödynnettiin etnografista työntutkimusta. Hoitotyön interventioita tunnistettiin hoitajien työtä havainnoimalla ja hoitajia haastattelemalla. Analysointi tapahtui yhdessä hoitajien (n=17) kanssa. Ydininterventioiden tutkimus tapahtui sähköistä Delfoi-menetelmää hyödyntäen. Panelisteina toimivat sairaanhoitajat ja hoitotyön lähijohtajat (ensimmäisellä kierroksella n=54, toisella kierroksella n=26). Kolmannessa vaiheessa tutkittiin hoitotyön päivittäiskirjauksia (n=1150) ja hoitotyön yhteenvetoja (n=17), joista tunnistetut interventiot yhdistettiin NICluokitukseen. Tutkimuksessa tunnistettiin yhteensä 105 interventioita, joista 95 %:lle löytyi vastine luokituksesta. Keskeisiä interventioita kirjallisuuskatsauksessa, etnografisessa työntutkimuksessa ja ydininterventioiden tutkimuksessa olivat käyttäytymisen muutokseen tähtäävät psykososiaaliset interventiot ja erityisesti voimavaralähtöinen selviytymiskyvyn tukeminen. Hoitotyön kirjauksissa korostuivat seuranta ja hoidon koordinointi. Interventioiden ryhmämuotoinen toteutustapa oli yleinen kaikissa tutkimusvaiheissa. Tutkimuksen tulokset korostavat yhteisten käsitteiden tarvetta hoitotyön interventioille työn käsitteellistämisen, näkyväksi tekemisen ja kirjaamisen laadun näkökulmista. Tutkitun luokituksen soveltuvuutta tukevat sen kattavuus, käsitteiden hyvä tunnistettavuus ja hierarkkinen rakenne. Luokituksen interventiokäsitteet ovat osittain päällekkäisiä heikentäen sen systemaattista käytettävyyttä ja tiedon toisiokäytön mahdollisuuksia. Soveltuvuutta rajoittavat myös luokituksen vähäinen yhteys tutkimuskirjallisuudessa käytettyihin käsitteisiin ja vaikeus kuvata ryhmämuotoisia interventioita. Tutkimus antaa suosituksia luokituksen jatkokehittämiselle. Keskeisimpänä ovat monimenetelmäisyys tutkimuksessa ja kehittämisessä sekä tutkimuskirjallisuuden käsitteistöjen vahvempi integroiminen luokitukseen

Rare Diseases in the United States: Establishing prevalence, the insurance experience, and orphan drug expenditures in Medicare Part D

In the United States, there are an estimated 30 million people living with one or more rare diseases. Each rare disease impacts fewer than 200,000 people. Small patient populations create research and medical challenges. Patients and the healthcare system experience high costs. To adequately address patient needs and prepare our healthcare system, it is critical that we conduct research that contextualizes the U.S. rare disease experience. This dissertation includes three related studies that look at the U.S. rare disease experience. The first paper investigates the availability and data quality of rare disease prevalence estimates and the healthcare infrastructure that could be utilized to establish future estimates. The paper found that prevalence estimates rarely follow best practices in data quality. U.S. healthcare infrastructure is ill-equipped to track rare diseases and produce future prevalence estimates. This could impact our ability to realize and equitably administer healthcare innovations, including precision medicine. The second paper looked at the caregiver experience navigating health insurance using a grounded theory qualitative approach. The paper found that rare disease caregivers feel it is imperative to learn how to navigate insurance, especially since health setbacks are costly and disruptive. Insurance companies are rarely knowledgeable about the disease and interactions are time intensive. Parents are required to meticulously track benefits to balance long-term medical needs and financial stability. The third paper investigated orphan drug expenditures in Medicare Part D from 2013 to 2017. The study found that orphan only drugs represent 8.67% and partial orphan drugs represent 6.74% of total aggregate costs. In 2017, the average cost per beneficiary for orphan drugs was $92,753 and$3,920 for common drugs. Almost half of orphan drug costs are attributed to beneficiaries under age 65. Together, these studies point to the need to invest in our healthcare system and explore programs that can address issues of access for patients. Currently, our system is not equipped to address patient needs and current funding increases are not sustainable. Policy considerations, such as a rare disease national plan, could help ensure rare disease patient needs are addressed in a methodica

Methods to Facilitate the Capture, Use, and Reuse of Structured and Unstructured Clinical Data.

Electronic health records (EHRs) have great potential to improve quality of care and to support clinical and translational research. While EHRs are being increasingly implemented in U.S. hospitals and clinics, their anticipated benefits have been largely unachieved or underachieved. Among many factors, tedious documentation requirements and the lack of effective information retrieval tools to access and reuse data are two key reasons accounting for this deficiency. In this dissertation, I describe my research on developing novel methods to facilitate the capture, use, and reuse of both structured and unstructured clinical data. Specifically, I develop a framework to investigate potential issues in this research topic, with a focus on three significant challenges. The first challenge is structured data entry (SDE), which can be facilitated by four effective strategies based on my systematic review. I further propose a multi-strategy model to guide the development of future SDE applications. In the follow-up study, I focus on workflow integration and evaluate the feasibility of using EHR audit trail logs for clinical workflow analysis. The second challenge is the use of clinical narratives, which can be supported by my innovative information retrieval (IR) technique called “semantically-based query recommendation (SBQR)”. My user experiment shows that SBQR can help improve the perceived performance of a medical IR system, and may work better on search tasks with average difficulty. The third challenge involves reusing EHR data as a reference standard to benchmark the quality of other health-related information. My study assesses the readability of trial descriptions on ClinicalTrials.gov and found that trial descriptions are very hard to read, even harder than clinical notes. My dissertation has several contributions. First, it conducts pioneer studies with innovative methods to improve the capture, use, and reuse of clinical data. Second, my dissertation provides successful examples for investigators who would like to conduct interdisciplinary research in the field of health informatics. Third, the framework of my research can be a great tool to generate future research agenda in clinical documentation and EHRs. I will continue exploring innovative and effective methods to maximize the value of EHRs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135845/1/tzuyu_1.pd

High Throughput Neurological Phenotyping with MetaMap

The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved

Automated clinical coding:What, why, and where we are?

Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html . Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. 1 , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html. Publisher Copyright: © 2022, The Author(s).Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable processof a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Peer reviewe

The threat nets approach to information system security risk analysis

The growing demand for healthcare services is motivating hospitals to strengthen outpatient case management using information systems in order to serve more patients using the available resources. Though the use of information systems in outpatient case management raises patient data security concerns, it was established that the current approaches to information systems risk analysis do not provide logical recipes for quantifying threat impact and determining the cost-effectiveness of risk mitigation controls. Quantifying the likelihood of the threat and determining its potential impact is key in deciding whether to adopt a given information system or not. Therefore, this thesis proposes the Threat Nets Approach organized into 4 service recipes, namely: threat likelihood assessment service, threat impact evaluation service, return on investment assessment service and coordination management. The threat likelihood assessment service offers recipes for determining the likelihood of a threat. The threat impact evaluation service offers techniques of computing the impact of the threat on the organization. The return on investment assessment service offers recipes of determining the cost-effectiveness of threat mitigation controls. To support the application of the approach, a ThreNet tool was developed. The approach was evaluated by experts to ascertain its usability and usefulness. Evaluation of the Threat Nets Approach by the experts shows that it provides complete, usable and useful recipes for the assessment of; threat likelihood, threat impact and cost-effectiveness of threat mitigation controls. The results suggest that the application of Threat Nets approach is effective in quantifying risks to information system