Background: Chronic conditions comprise a significant amount of healthcare utilization. For example, people with chronic diseases account for 51% of family physician encounters. Therefore, diagnostic algorithms based on comprehensive clinical records could be a rich resource for clinicians, researchers and policy-makers. However, limitations such as misclassification warrant the need for examining the accuracy of these algorithms. Purpose: To investigate and enhance the accuracy of the diagnostic algorithms for five chronic diseases in the Canadian Primary Care Sentinel Surveillance Network. Methods: DESIGN: A validation study using primary chart abstraction. SETTING: A stratified random sample of 350 patient charts from Kingston practice-based research network. OUTCOME MEASURES: Sensitivity and specificity for the diagnostic algorithms. ANALYSIS: A multiple logistic regression model along with the receiver operating characteristic curve was employed to identify the algorithm that maximized accuracy measures. Results: The sensitivities for diagnostic algorithms were 100% (diabetes), 83% (hypertension), 45% (Osteoarthritis), 41% (COPD), and 39% (Depression). The lowest specificity was 97% for depression. A data-driven logistic model and receiver-operating characteristic curve improved sensitivity for identifying hypertension patients from 83% to 88% and for osteoarthritis patients from 45% to 81% with areas under the curve of 92.8% and 89.8% for hypertension and osteoarthritis, respectively. Conclusion: The diagnostic algorithms for diabetes and hypertension demonstrate adequate accuracy, thus allowing their use for research and policy-making purposes. A multivariate logistic model for predicting osteoarthritis diagnosis enhanced sensitivity while maintaining high specificity. This approach can be used towards further refining the diagnostic algorithms for other chronic conditions. / Thesis (Master, Community Health & Epidemiology) -- Queen's University, 2012-07-23 17:58:11.302

Purpose: The purpose of this study is to examine the adoption of e-prescribing by primary care physicians in the Cowichan Valley Community of Practice (COP) who use the same commercial EMR product (Med Access EMR) and to make suggestions on improving adoption. Methods: This study employed a multi-method study design to compare the ideal state of e-prescribing (i.e., the desired e-prescribing features in an EMR) with the possible state (i.e., what the EMR can offer) and the current state of e-prescribing (i.e., what physicians are actually using in practice).The ideal state of e-prescribing was determined using a literature search in MEDLINE, a personal collection, and reference mining.The possible state for e-prescribing was assessed by (1) reviewing the EMR user documentation and (2) reviewing provincial conformance specifications for EMRs (from Physician Information Technology Office (PITO)) and (3) interviewing an EMR vendor representative to confirm features. Based on this review, an e-prescribing assessment tool was then developed and piloted with physicians.The current state of e-prescribing was examined by interviewing physicians using the aforementioned e-prescribing assessment tool and an EMR Adoption Survey. A discussion group then took place to share the study findings and provide feedback on how to improve use of the EMR for prescribing. Results: For the ideal state of e-prescribing, 10 papers were included in the literature search as a part of the search strategy. In total, 104 e-prescribing features were identified in these papers relating to the following categories: patient Information, identification, and data access, current medications/medication history, medication selection, prescribing safety, patient education, monitoring, repeat (renewal) prescribing, computer-user interface, transparency and accountability, security and confidentiality, and interoperability and communication.For the possible state of e-prescribing, the EMR product met 27 of the 33 PITO e-prescribing requirements partially or fully, relating to the following PITO subcategories: generating prescriptions, processing prescriptions, transmitting prescriptions, viewing medications, managing renewals, drug formularies, interaction checking, medication profiles, and reference support. Data pertaining to the current state of e-prescribing adoption were collected from interviews with 12 primary care physicians who represent 17% of the total sample population. On average, the physicians reported using 75% (n=21.7/29) of the e-prescribing features available in the EMR. The e-prescribing features least used were “drug search by class”, “check for patient coverage”, “drug to procedure interaction checking”, and “use of drug monographs”. The average EMR Adoption score for physicians was 3.1 out of 5. A discussion group with six study participants was conducted to validate the findings of the current state and recommendations. Conclusions/ Recommendations: Recruited physicians from the Cowichan Valley COP are using most of the e-prescribing and EMR features available in the Med Access EMR. However, there are several gaps between the ideal, possible, and current state of e-prescribing. These gaps have been addressed through physician-level, policy-related, and technology-related recommendations to (1) help physicians improve use of the EMR for prescribing to achieve the possible state of e-prescribing and (2) guide vendor design and development of e-prescribing features in the EMR to achieve the ideal state of e-prescribing. / Graduate / 0723 / 0566 / 0984 / gurprit@uvic.ca

Introduction The Northwest Territories as a rural and remote region of Canada has higher colorectal cancer rates and lower uptake of colorectal cancer screening compared to the rest of the country. Understanding the complex health system processes involved in screening is necessary to develop informed solutions to improve screening access amongst marginalized populations. A systems approach to describe and understand the health care processes and system-level factors influencing colorectal cancer screening access was undertaken. Methods Semi-structured interviews with health care providers (N=29) involved in colorectal cancer screening in all health authorities within the Northwest Territories (N=8) were performed from September to December 2015. Interview transcripts were analyzed using qualitative content analysis methods within a Collaborative Information Behaviour (CIB) and Continuity of Care framework. Exploratory models of colorectal cancer screening processes were developed and translated into quantitative parameters for simulation modelling. Results Colorectal cancer screening access was defined by patient health care interactions supported by foundational information processes. Eighteen models of colorectal cancer screening access within the territory were identified, with varying complexity in care access seen across communities. Screening access problems included screening initiation, colonoscopy scheduling, screening recall and information silos, and were influenced by multiple contextual factors including a transient health work force, social health determinants, and patient travel. Qualitative models were translated into a system dynamics (SD) design framework for development of further quantitative modeling. Conclusions Colorectal cancer screening access in the Northwest Territories is a complex process comprising patient interactions and information processes linking primary care and hospital care processes, which are influenced by challenging contextual factors in the rural and remote health care environment. In developing screening access solutions the foundational role of information support and the need for system trade-offs in restructuring health system processes are necessary considerations. Optimizing information processes through the utilization of health informatics tools such as standardized referral forms and EMRs may also support health system transformation to improve screening access across the Northwest Territories. Understanding and evaluating system trade-offs may be best achieved using a combination of qualitative and quantitative modeling through future application of SD modeling research.

<p>Care of patients with chronic conditions is complicated and usually includes large number of healthcare providers. Understanding the team structure and networks of healthcare providers help to make informed decisions for health policy makers and design of wellness programs by identifying the influencers in the network. This work presents a novel approach to assess the collaboration of healthcare providers involved in the care of patients with chronic conditions and the impact on patient outcomes. </p> <p>In the first study, we assessed a patient population needs, preventive service utilization, and impact of an onsite clinic as an intervention on preventive service utilization patterns over a three-year period. Classification models were developed to identify groups of patients with similar characteristics and healthcare utilization. Logistic regression models identified patient factors that impacted their utilization of preventive health services in the onsite clinic vs. other providers. Females had higher utilizations compared to males. Type of insurance coverages, and presence of diabetes/hypertension were significant factors that impacted utilization. The first study framework helps to understand the patient population characteristics and role of specific providers (onsite clinic), however, it does not provide information about the teams of healthcare providers involved in the care process. </p> <p>Considering the high prevalence of diabetes in the patient cohort of study 1, in the second study, we followed the patient cohort with diabetes from study 1 and extracted their healthcare providers over a two-year period. A framework based on the social network analysis was presented to assess the healthcare providers’ networks and teams involved in the care of diabetes. The relations between healthcare providers were generated based on the patient sharing relations identified from the claims data. A multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Centrality measures of the social network identified the influencers in the overall network and each community. Mail-order and retail pharmacies were identified as central providers in the overall network and majority of communities. This study presented metrics and approach for assessment of provider collaboration. To study how these collaborative relations impact the patients, in the last study, we presented a framework to assess impacts of healthcare provider collaboration on patient outcomes. </p> <p>We focused on patients with diabetes, hypertension, and hyperlipidemia due to their similar healthcare needs and utilization. Similar to the second study, social network analysis and a multi-scale community detection algorithm were used to identify networks and communities of healthcare providers. We identified providers who were the majority source of care for patients over a three-year period. Regression models using generalized estimating equations were developed to assess the impact of majority source of care provider community-level centrality on patient outcomes. Higher connectedness (higher degree centrality) and higher access (higher closeness centrality) of the majority source of care provider were associated with reduced number of inpatient hospitalization and emergency department visits. </p> <p>This research proposed a framework based on the social network analysis that provides metrics for assessment of care team relations using large-scale health data. These metrics help implementation experts to identify influencers in the network for better design of care intervention programs. The framework is also useful for health services researchers to assess impact of care teams’ relations on patient outcomes. </p> <br> <p> </p>

No description available.

Yes / OBJECTIVES: In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS). DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation). PARTICIPANTS: Adult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2). RESULTS: The risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups. CONCLUSIONS: An externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems. / This research was supported by the Health Foundation. The Health Foundation is an independent charity working to improve the quality of healthcare in the UK. This research was also supported by the National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre (YHPSTRC).

In the light of an ageing society with shrinking economic resources, deinstitutionalization of elderly care is a general trend. As a result, homecare is increasing, and increasingly shared between different health and social care organizations. To provide a holistic overview about the patient care process, i.e. to be patient-centred, shared homecare needs to be integrated. This requires improved support for information sharing and cooperation between different actors, such as care professionals, patients and their relatives. The research objectives of this thesis are therefore to study information and communication needs for patient-centered shared homecare, to explore how integrated information and communication technology (ICT) can support information sharing, and to analyze how current standards for continuity of care and semantic interoperability meet requirements of patient-centered shared homecare. An action research approach, characterized by an iterative cycle, an emphasis on change and close collaboration with practitioners, patients and their relatives, was used. Studying one specific homecare setting closely, intersection points between involved actors and specific needs for information sharing were identified and described as shared information objects. An integration architecture making shared information objects available through integration of existing systems was designed and implemented. Mobile virtual health record (VHR) applications thereby enable a seamless flow of information between involved actors. These applications were tested and validated in the OLD@HOME-project. Moreover, the underlying information model for a shared care plan was mapped against current standards. Some important discrepancies were identified between these results and current standards for continuity of care, stressing the importance of evaluating standardized models against requirements of evolving healthcare contexts. In conclusion, this thesis gives important insights into the needs and requirements of shared homecare, enabling a shift towards patient-centered homecare through mobile access to aggregated information from current feeder systems and documentation at the point of need.

This thesis describes research into the facilitation of mediated communication of health updates and support needs across the social network, on behalf of individuals experiencing acute or chronic health problems. This led to the user-centred design, development and evaluation of a prototype software tool. Investigatory applied research was conducted with the parents of sick newborn infants who were (or had previously been) cared for in a Neonatal Unit, and their social networks of family, friends, colleagues and neighbours. The thesis makes contributions to knowledge within Social Networks, Health Informatics, Adaptive Systems and User Modelling. The user-centred research was conducted using a Grounded Theory approach, progressively focussing on developing themes. An iterative approach was taken to evaluation of the resulting theory. In the Social Networks domain, a novel, intuitive mechanism for capturing the membership and structure of an individual’s personal social network has been defined and developed, grounded in the work of evolutionary anthropologist Robin Dunbar. Use of the highly visual mechanism requires low levels of literacy and computer skills. It is cross-culturally applicable, and makes no prior assumptions about an individual’s relationships. In the domains of Health Informatics, Adaptive Systems and User Modelling, a model has been defined for adaptive information sharing across the personal social network. This model provides a number of new insights about information sharing choices made by an individual experiencing a health crisis (the ego) and their supporters (alters).

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records. Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

When the Swedish National IT Strategy for Health and Social Care was introduced in 2006, intensive work started in implementing Information Systems (IS) in Swedish healthcare organizations. To follow up on the requests for more research with a combined socio-technical focus on challenges, the overall aim of this thesis was to identify social challenges when implementing IS in a Swedish healthcare organization. Furthermore, the aim was to understand the impact of identified social challenges when implementing IS in this context by putting them in an interdisciplinary Applied Health Technology theoretical framework. Institutional ethnography and phenomenological hermeneutics influenced the study design. Study 1 aimed to investigate different meanings of accessibility when implementing Health Information Technology in everyday work practice. The results indicate that accessibility depends on working routines, social structures and patient relationship. When an IT strategy and interaction in everyday work use the same word in different ways there will be consequences. Study 2 sets out to describe experience-based reflections on discharge planning as narrated by nursing staff in primary healthcare, along with their concerns about how the introduction of video conferencing might influence the discharge planning situation. It was found that there is a need for improvement in communication and understanding between nursing staff at the hospital and in primary healthcare. The aim of study 3 was to explore social challenges when implementing IS in everyday work in a nursing context. Power (changing the existing hierarchy, alienation), Professional identity (calling on hold, expert becomes novice, changed routines), and Encounter (ignorant introductions, preconceived notions) were categories presented in the findings. The aim of study 4 was to explore and obtain a deeper understanding of how identified social challenges have an influence on the implementation process of IS, based on healthcare staff’s experiences on micro, meso and macro levels of Swedish Healthcare organizations. It was found that the challenges were related to the steps of putting into practice, making IS a part of everyday work routine and establishing an identity in the implementation process. In the thesis’s discussion, social challenges when implementing IS in Swedish healthcare organizations and how they might be met and dealt with constructively are further reflected upon in relation to the interdisciplinary theoretical framework and as possible consequences of the modernity-era. This thesis contributes to the starting up of a discussion of how ingrained professional characteristics are important to feel secure of being part of an established profession. If the characteristics are questioned, the whole professional performance is threatened. One consequence of this insight is the reinforcement of the realization that a basic understanding of IS and IS implementation processes in healthcare organizations needs to be integrated in to the construction of professional identity of nurses already from the start in nursing education.