A Process Improvement Toolkit to Guide the Attainment of Meaningful Use Stage 2 Requirements

Healthcare is evolving. Reimbursement is transitioning to a model based on quality and patient outcomes. To remain relevant and survive this transition, providers of care must adapt and implement new models of care delivery that account for these changes. This toolkit was created as a deliverable of a Doctor of Nursing Practice dissertation that explored a successful primary care delivery model of a Patient-Centered Medical Home that utilized an interdisciplinary team approach that included nurses. Through this model high quality care was delivered to achieve desired outcomes, specifically, successful attestation for Stage 2 of the Meaningful Use Incentive Program during the first quarter of 2014. This toolkit was created as a result of the exploration of this model in order to inform others regarding structures and processes that can be integrated to meet the requirements of Stage 2 Meaningful Use. To do so, this toolkit describes the structure utilized by practice of interest, including the roles of vital staff members. Processes that result in meeting Meaningful Use objectives are also described, many in the form of decision trees. The toolkit also includes an example of what an investment in this model would entail along with guidelines for model replication. This toolkit provides a framework for success in meeting Meaningful Use Stage 2 requirements

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Obese Active Duty Military Members: Improving Screening, Diagnosis and Access to Weight Management Support in Primary Care

Background: In 2015, 61% of active duty (AD) U.S. Air Force (USAF) members were categorized as being either overweight or obese. Although the USAF Behavioral Health Optimization Program (BHOP) has a weight management program, it remains underutilized by USAF primary care managers (PCMs). This process improvement project aimed to improve weight screening, diagnosis, patient counseling documentation, and scheduling obese members with the BHOP weight management program. Methods: Conducted at a single USAF primary care clinic, the intervention included: an evidence-based weight management education session; provision of a stakeholder- informed screening and service access protocol; and employed a champion and a reminder system. The following data were collected pre-implementation, during project implementation, and post-implementation for each relevant obese AD appointment: height, weight, and BMI; obesity diagnosis; documented counseling, and scheduled BHOP appointment. Data were compared across time points using repeated measures logistic regression. Findings: 1,631 AD appointments were analyzed. Family practice obesity and weight management counseling documentation was statistically significantly lower between pre and post intervention time periods. The BHOP appointment rate increased (2.9% to 5.9%). Flight medicine height, weight, and BMI documentation initially improved (79.2% to 87.6%), as did counseling documentation (14.3% to 25.9%), and BHOP appointments (0% to 12.5%). However, obesity diagnosis decreased (13.3% to 7.4%). The walk-in care team experienced a non-significant decline (90.9% to 77.4%) in height, weight, and BMI documentation. insufficient data prevented analyzing diagnosis, counseling, and appointing changes. Implications: Although this project targeted a significant military health issue and employed evidence-based implementation strategies, little relevant improvement was seen. Process improvement studies noted that genuine change(s) in practice are difficult in any setting, which is likely further complicated when attempted in military care settings due to additional duty requirements. Research among civilian PCMs suggested that providers struggle with weight management counseling, either because they are uncomfortable with the topic, or they perceived that patients were unwilling to engage in behavior change. Military healthcare providers may well need additional supports to assist AD personnel in maintaining appropriate weight, as this is essential to maintaining the military mission as well as for the long-term health of the military members.Doctor of Nursing Practic

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled Developing a Clinical Genomic Informatics Research Agenda . The meeting\u27s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting\u27s goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them

eHealth in hypertension and cardiovascular disease:Opportunities and challenges

In this thesis we investigate different aspects of eHealth for hypertension and cardiovascular disease, with a focus on remote monitoring programs for chronic care. We use the Dutch HartWacht program for patients with hypertension, cardiac arrhythmias and heart failure as an example that has been implemented in routine clinical care. We first focus on hypertension and identify areas that are attractive for future implementation of eHealth because of poor hypertension control. In the following chapters we present economical, legal and technical challenges that accompany eHealth implementation, in each chapter followed by potential solutions and opportunities. We identify success factors for cost-effective eHealth, provide a roadmap for GDPR-compliant solutions, present a novel technique for heartbeat detection through a bracelet and describe a protocol for efficient data handling in remote monitoring programs. In the second part of this thesis, we zoom in on the patients participating in eHealth programs. We evaluate the impact on quality of life of patients participating in the HartWacht program for cardiac arrhythmias and demonstrate equivalence compared to usual care. We then describe the feasibility of the HartWacht program for patients with hypertension in reducing blood pressure and present rationale, design and cohort profile of the Effectiveness of home-Monitoring of blood pressure in PAtients with difficult to Treat HYpertension (EMPATHY) trial. We conclude with an evaluation of the impact of the COVID-19 pandemic on the uptake of eHealth in primary care in the Netherlands

J Registry Manag

Electronic health records (EHRs) are increasingly being used to support public health surveillance, including in cancer, where many population-based registries can now accept electronic case reporting. Using EHRs to supplement cancer registry data provides the opportunity to examine in more detail emerging issues in cancer control, such as the increasing incidence rates of early onset colorectal cancer (CRC). The purpose of this study was to evaluate the feasibility of a public health organization partnering with a health system to examine risk factors for early-onset CRC in a community cancer setting, and to further understand challenges with using EHRs to address emerging topics in cancer control. We conducted a mixed-methods evaluation using key informant interviews with public health practitioners, researchers, and registry staff to generate insights on how using EHRs and partnering with health systems can improve chronic disease surveillance and cancer control. A data quality assessment of variables representing risk factors for CRC and other clinical characteristics was conducted on all CRC patients diagnosed in 2016 at the participating cancer center. The quantitative assessment of the EHR data revealed that, while most chronic health conditions were well documented, around 25% of CRC patients were missing information on body mass index, alcohol, and tobacco use. Key informants offered ideas and ways to overcome challenges with using EHR data to support chronic disease surveillance. Their recommendations included the following activities: engaging EHR vendors in the development of standards, taking leadership roles on workgroups to address emerging technological issues, participating in pilot studies and task forces, and negotiating with EHR vendors so that clinical decision support tools built to support public health initiatives are freely available to all users of those EHRs. Although using EHR data to support public health efforts is not without its challenges, it soon could be an important part of chronic disease surveillance and cancer control.20212022-06-24T00:00:00ZCC999999/ImCDC/Intramural CDC HHSUnited States/U38 OT000225/OT/OSTLTS CDC HHSUnited States/34170890PMC92316381167

Dynamic Risk Models for Characterising Chronic Diseases' Behaviour Using Process Mining Techniques

[ES] Los modelos de riesgo en el ámbito de la salud son métodos estadísticos que brindan advertencias tempranas sobre el riesgo de una persona de sufrir un episodio adverso en el futuro. Por lo general, utilizan la información almacenada de forma rutinaria en los sistemas de información hospitalaria para ofrecer una probabilidad individual de desarrollar un resultado negativo futuro en un período determinado. Concretamente, en el campo de las enfermedades crónicas que comparten factores de riesgo comunes, los modelos de riesgo se basan en el análisis de esos factores de riesgo -tensión arterial elevada, glucemia elevada, lípidos sanguíneos anormales, sobrepeso y obesidad- y sus medidas biométricas asociadas. Estas medidas se recopilan durante la práctica clínica de manera periódica y, se incorporan a los modelos de riesgo para apoyar a los médicos en la toma de decisiones. Para crear modelos de riesgo que incluyan la variable temporal, se podrían utilizar técnicas basadas en datos (Data-Driven), de forma que se tuviera en cuenta el historial de los pacientes almacenado en los registros médicos electrónicos, extrayendo conocimiento de los datos en bruto. Sin embargo, en el ámbito de la salud, los resultados de la minería de datos suelen ser percibidos por los expertos en salud como cajas negras y, en consecuencia, no confían en sus decisiones. El paradigma Interactivo permite a los expertos comprender los resultados, para que los profesionales puedan corregir esos modelos de acuerdo con su conocimiento y experiencia, proporcionando modelos perceptivos y cognitivos. En este contexto, la minería de procesos es una técnica de minería de datos que permite la implementación del paradigma Interactivo, ofreciendo una comprensión clara del proceso de atención y proporcionando modelos comprensibles para el ser humano. Las condiciones crónicas generalmente se describen mediante imágenes estáticas de variables, como factores genéticos, fisiológicos, ambientales y de comportamiento. Sin embargo, la perspectiva dinámica, temporal y de comportamiento no se consideran comúnmente en los modelos de riesgo. Eso significa que el último estado de riesgo se convierte en el estado real del paciente. No obstante, la condición de los pacientes podría verse influenciada por sus condiciones dinámicas pasadas. El objetivo de esta tesis es proporcionar una visión novedosa del riesgo asociado a un paciente, basada en tecnologías Data-Driven que ofrezcan una visión dinámica de su evolución con respecto a su condición crónica. Técnicamente, supone abordar los modelos de riesgo incorporando la perspectiva dinámica y comportamental de los pacientes gracias a la información incluida en la Historia Clínica Electrónica. Los resultados obtenidos a lo largo de esta tesis muestran cómo las tecnologías de minería de procesos pueden aportar una visión dinámica e interactiva de los modelos de riesgo de enfermedades crónicas. Estos resultados pueden ayudar a los profesionales de la salud en la práctica diaria para una mejor comprensión del estado de salud de los pacientes y una mejor clasificación de su estado de riesgo.[CA] Els models de risc en l'àmbit de la salut són mètodes estadístics que brinden advertències primerenques sobre el risc d'una persona de patir un episodi advers en el futur. Generalment, utilitzen la informació emmagatzemada de forma rutinària en els sistemes d'informació hospitalària per a oferir una probabilitat individual de desenrotllar un resultat negatiu futur en un període determinat. Concretament, en el camp de les malalties cròniques que compartixen factors de risc comú, els models de risc es basen en l'anàlisi d'eixos factors de risc -tensió arterial elevada, glucèmia elevada, lípids sanguinis anormals, sobrecàrrega i obesitat- i les seues mesures biomètriques associades. Estes mesures es recopilen durant la pràctica clínica ben sovint de manera periòdica i, en conseqüència, s'incorporen als models de risc i recolzen la presa de decisions dels metges. Per a crear estos models de risc que incloguen la variable temporal es podrien utilitzar tècniques basades en dades (Data-Driven) , de manera que es tinguera en compte l'historial dels pacients disponible en els registres mèdics electrònics, extraient coneixement de les dades en brut. No obstant això, en l'àmbit de la salut, els resultats de la mineria de dades solen ser percebuts pels experts en salut com a caixes negres i, en conseqüència, no confien en les decisions dels algoritmes. El paradigma Interactiu permet als experts comprendre els resultats, perquè els professionals puguen corregir eixos models d'acord amb el seu coneixement i experiència, proporcionant models perceptius i cognitius. En este context, la mineria de processos és una tècnica de mineria de dades que permet la implementació del paradigma Interactiu, oferint una comprensió clara del procés d'atenció i proporcionant models comprensibles per al ser humà. Les condicions cròniques generalment es descriuen per mitjà d'imatges estàtiques de variables, com a factors genètics, fisiològics, ambientals i de comportament. No obstant això, la perspectiva dinàmica, temporal i de comportament no es consideren comunament en els models de risc. Això significa que l'últim estat de risc es convertix en l'estat real del pacient. No obstant això, la condició dels pacients podria veure's influenciada per les seues condicions dinàmiques passades. L'objectiu d'esta tesi és proporcionar una visió nova del risc, associat a un pacient, basada en tecnologies Data-Driven que oferisquen una visió dinàmica de l'evo\-lució dels pacients respecte a la seua condició crònica. Tècnicament, suposa abordar els models de risc incorporant la perspectiva dinàmica i el comportament dels pacients als models de risc gràcies a la informació inclosa en la Història Clínica Electrònica. Els resultats obtinguts al llarg d'esta tesi mostren com les tecnologies de mineria de processos poden aportar una visió dinàmica i interactiva dels models de risc de malalties cròniques. Estos resultats poden ajudar els professionals de la salut en la pràctica diària per a una millor comprensió de l'estat de salut dels pacients i una millor classificació del seu estat de risc.[EN] Risk models in the healthcare domain are statistical methods that provide early warnings about a person's risk for an adverse episode in the future. They usually use the information routinely stored in Hospital Information Systems to offer an individual probability for developing a future negative outcome in a given period. Concretely, in the field of chronic diseases that share common risk factors, risk models are based on the analysis of those risk factors -raised blood pressure, raised glucose levels, abnormal blood lipids, and overweight and obesity- and their associated biometric measures. These measures are collected during clinical practice frequently in a periodic manner, and accordingly, they are incorporated into the risk models to support clinicians' decision-making. Data-Driven techniques could be used to create these temporal-aware risk models, considering the patients' history included in Electronic Health Records, and extracting knowledge from raw data. However, in the healthcare domain, Data Mining results are usually perceived by the health experts as black-boxes, and in consequence, they do not trust in the algorithms' decisions. The Interactive paradigm allows experts to understand the results, in that sense, professionals can correct those models according to their knowledge and experience, providing perceptual and cognitive models. In this context, Process Mining is a Data Mining technique that enables the implementation of the Interactive paradigm, offering a clear care process understanding and providing human-understandable models. Chronic conditions are usually described by static pictures of variables, such as genetic, physiological, environmental, and behavioural factors. Nevertheless, the dynamic, temporal, and behavioural perspectives are not commonly considered in the risk models. That means the last status of the risk becomes the actual status of the patient. However, the patients' condition could be influenced by their past dynamic circumstances. The objective of this thesis is to provide a novel risk vision based on Data-Driven technologies offering a dynamic view of the patients' evolution regarding their chro\-nic condition. Technically, it supposes to approach risk models incorporating the dynamic and behavioural perspective of patients to the risk models thanks to the information included in the Electronic Health Records. The results obtained throughout this thesis show how Process Mining technologies can bring a dynamic and interactive view of chronic disease risk models. These results can support health professionals in daily practice for a better understanding of the patients' health condition and a better classification of their risk status.Valero Ramón, Z. (2022). Dynamic Risk Models for Characterising Chronic Diseases' Behaviour Using Process Mining Techniques [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181652TESI

Decision Support for Tailored Biopsychosocial Rehabilitation : In Non-specific Low Back Pain

Alaselkäkipu on maailman yleisin toimintakyvyn haittaa aiheuttava oire. Suurin osa alaselkäkivusta on niin sanottua epäspesifiä, eikä sille ole osoitettavissa aukottomasti patoanatomista taustaa. Kivun ja toimintakyvyn haitan kokemukseen ja kroonistumiseen liittyy laajasti erilaisia biopsykososiaalisia tekijöitä, joista osaan voidaan vaikuttaa kohdentamalla interventioita oikea-aikaisesti oikealle potilaalle, ja täten vähentää kivun pitkittymisen riskiä. Kipuun liittyviä biopsykososiaalisia tekijöitä ja niiden välisiä yhteyksiä voidaan ymmärtää paremmin maailman terveysjärjestö WHO:n kansainvälisen toimintakyvyn, toimintarajoitteiden ja terveyden luokituksen (ICF-viitekehys) avulla, joka kuvaa toimintakykyä laaja- alaisena biopsykososiaalisena kokonaisuutena. Tämän artikkeliväitöskirjan päätavoitteena oli kehittää menetelmiä tukemaan yksilöllisen biopsykososiaalisen kuntoutuksen suunnittelua ja toteutusta selkäkipupotilailla. Alatavoitteina oli tuottaa ajankohtaista tietoa tunnistetuista alaselkäkivun kroonistumisen riskitekijöistä, sekä löytää uusia menetelmiä biopsykososiaalisten tekijöiden tunnistamiseen ja näiden tekijöiden avulla sopivan intervention valintaan yksilöllisesti. Alaselkäkivun kroonistumisen riskitekijöistä tehtiin systemaattinen kirjallisuuskatsaus, jossa tutkittiin 25 tutkimuksen tuloksia. Tutkimusten tuli arvioida mahdollista riskitekijää ennen kivun kroonistumisen alkamista (3kk), jotta riskitekijää voitiin pitää ennakoivana tekijänä kroonistumiselle. Biopsykososiaalisten tekijöiden tunnistamiseen kehitettiin sovellus tekoälyalgoritmista, jonka tarkoituksena on tunnistaa toimintakykyyn liittyvää tietoa potilaskertomusteksteistä ICF-viitekehyksen mukaisesti. Sovelluksen tuloksia verrattiin alan asiantuntijan tekemään tunnistamiseen. Selkäpotilaan prosesseja perusterveydenhuollossa ja työterveydessä kehitettiin paremmin tunnistamaan kroonistumisen riskitekijöitä sekä valitsemaan sopivat interventiot yksilöllisesti. Prosessin kehittämisessä oli mukana moniammatillinen työryhmä perusterveydenhuollosta, työterveydestä sekä erikoissairaanhoidosta. Uusien menetelmien kehityksen tueksi kerättiin 93 kroonisen alaselkäkipuisen potilaan aineisto. Aineisto sisälsi vapaata tekstiä potilaskertomusteksteistä sekä numeerista dataa esitietolomakkeiden muodossa. Systemaattisen kirjallisuuskatsauksen mukaan yhteensä 45 erilaista riskitekijää on tunnistettavissa selkäkivun kroonistumisen riskitekijäksi. Riskitekijät jaoteltiin demografisiin ja sairaushistoriaan liittyviin tekijöihin, biomekaanisiin tekijöihin, oireiden ominaisuuksiin liittyviin tekijöihin, psykologisiin ja psykososiaalisiin tekijöihin, sekä elintapatekijöihin. Tunnistetut riskitekijät olivat yhdistettävissä ICF- viitekehyksen toimintakyvyn kuvauksiin, lukuun ottamatta demografisia ja sairaushistoriaan liittyviä tekijöitä. Kehitetty tekoälyalgoritmin sovellus tunnisti toimintakykytietoa potilaskertomusteksteistä 83.1 % herkkyydellä ja 99.84 % tarkkuudella verrattuna alan asiantuntijan tekemään tunnistukseen. Selkäpotilaan prosessin kehityksen tuotoksena syntyi vuokaavio, jonka avulla oikeat ammattilaiset ohjautuvat mukaan prosessiin potilaan tarpeiden mukaisesti, tietävät omat tehtävänsä, sekä pystyvät hyödyntämään paremmin moniammatillista ja monisektorista yhteisöä yksilöllisesti potilaan hyväksi. Tämä artikkeliväitöskirja luo uusia tutkimusmahdollisuuksia sekä selkäpotilaiden että toimintakykytiedon hyödyntämisen alueilla. Kirjallisuuskatsauksen tulokset auttavat kliinikoita paremmin ymmärtämään selkäkivun biospykososiaalista kokonaisuutta ja tutkijoita laajentamaan interventiotutkimusasetelmiaan. Tulevaisuudessa kuntoutusprosessista voidaan tehdä soveltuvuustutkimusta ennen laajempaa interventiota, ja tekoälyalgoritmin sovelluksen hyödyntämistä muille potilasryhmille ja kielille suunnitellaan.Low back pain is globally the most burdensome symptom causing disability. It is most commonly defined as non-specific, which means no pathoanatomical cause can be demonstrated as the cause. Different biopsychosocial factors are widely related to the experience and prolongation of pain and disability. Some of these factors can be affected by targeting timely interventions and decreasing the risk for pain chronicity. Pain related biopsychosocial factors and their connections can be understood more profoundly with the help of the International Classification of Functioning, Disability, and Health (ICF) framework developed by the World Health Organization (WHO), which describes disability from a wide biopsychosocial perspective. The main aim of this dissertation was to develop methods to support the decision-making in the tailored biopsychosocial rehabilitation of patients with non- specific LBP. The secondary aims were to produce a topical summary of the known biopsychosocial risk factors for low back pain chronicity, and to find methods to recognize those factors as well as support the assessment and execution of tailored interventions targeted to the individually recognized factors. A systematic literature review was compiled from the results of 25 different studies on the risk factors associated with low back pain chronicity. The studies had to evaluate the possible risk factor before the chronic phase of pain (3 months) in order to be regarded as a preceding factor for pain. To help the recognition of biopsychosocial factors at the individual level, an artificial intelligence algorithm application was developed that identifies disability information from electronic health records in accordance with the ICF framework. The results of the application were compared to the findings of a domain expert. The processes of patients with low back pain in primary and occupational health care were developed to more comprehensively assess possible risk factors and better tailor interventions to the individuals. A multidisciplinary team was formed from primary, occupational, and special health care professionals for the process design. For the purposes of developing new methods, a patient population of 93 patients with chronic low back pain were gathered. The data comprised free text from electronic health records and quantitative information from medical history forms. According to the systematic review, 45 different factors were identified as being associated with low back pain chronification. The factors were divided into demographical and medical history related factors, biomechanical factors, symptom related factors, psychological and psychosocial factors, and lifestyle factors. The factors were interrelated with the description of disability in the ICF framework, with the exception of the demographic and medical history related factors. The applied artificial intelligence algorithm was able to recognize disability information from the electronic health records with a sensitivity of 83.1% and specificity of 99.84% compared to the results of the domain expert. The rehabilitation process design was presented in a logic model that guides the needed professionals into the process according to the patients’ needs, clearly states the activities of the professionals, and comprehensively exploits a multidisciplinary community over sector boundaries. The findings of this dissertation open new research possibilities in the areas of low back pain and the exploitation of disability information. The results of the systematic review will help clinicians to better understand the biopsychosocial entity of low back pain more competently and researchers to extend their intervention study designs. In future, a feasibility study on the rehabilitation process should be executed before a larger intervention. The benefits of the artificial intelligence algorithm application are planned to be expanded to other patient groups and languages

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain