Accounting for quality improvement during the conduct of embedded pragmatic clinical trials within healthcare systems: NIH Collaboratory case studies

Embedded pragmatic clinical trials (ePCTs) and quality improvement (QI) activities often occur simultaneously within healthcare systems (HCSs). Embedded PCTs within HCSs are conducted to test interventions and provide evidence that may impact public health, health system operations, and quality of care. They are larger and more broadly generalizable than QI initiatives, and may generate what is considered high-quality evidence for potential use in care and clinical practice guidelines. QI initiatives often co-occur with ePCTs and address the same high-impact health questions, and this co-occurrence may dilute or confound the ability to detect change as a result of the ePCT intervention. During the design, pilot, and conduct phases of the large-scale NIH Collaboratory Demonstration ePCTs, many QI initiatives occurred at the same time within the HCSs. Although the challenges varied across the projects, some common, generalizable strategies and solutions emerged, and we share these as case studies. KEY LESSONS: Study teams often need to monitor, adapt, and respond to QI during design and the course of the trial. Routine collaboration between ePCT researchers and health systems stakeholders throughout the trial can help ensure research and QI are optimally aligned to support high-quality patient-centered care

Development of a complex intervention for early integration of palliative home care into standard care for end-stage COPD patients : a phase 0-I study

Background : Research suggests that palliative home care should be integrated early into standard care for end-stage COPD patients. Patients also express the wish to be cared for and to die at home. However, a practice model for early integration of palliative home care (PHC) into standard care for end-stage COPD has not been fully developed. Aim : To develop an intervention for early integration of PHC into standard care for end-stage COPD patients. Methods : We conducted a Phase 0-I study according to the Medical Research Council Framework for the development of complex interventions. Phase 0 aimed to identify the inclusion criteria and key components of the intervention by way of an explorative literature search of interventions, expert consultations, and seven focus groups with general practitioners and community nurses on perceived barriers to and facilitators of early integrated PHC for COPD. In Phase 1, the intervention, its inclusion criteria and its components were developed and further refined by an expert panel and two expert opinions. Results : Phase 0 resulted in identification of inclusion criteria and components from existing interventions, and barriers to and facilitators of early integration of PHC for end-stage COPD. Based on these findings, a nurse-led intervention was developed in Phase I consisting of training for PHC nurses in symptom recognition and physical therapy exercises for end-stage COPD, regular visits by PHC nurses at the patients' homes, two information leaflets on selfmanagement, a semi-structured protocol and follow-up plan to record the outcomes of the home visits, and integration of care by enabling collaboration and communication between home and hospital-based professional caregivers. Conclusion : This Phase 0-I trial succeeded in developing a complex intervention for early integration of PHC for end-stage COPD. The use of three methods in Phase 0 gave reliable data on which to base inclusion criteria and components of the intervention. The preliminary effectiveness, feasibility and acceptability of the intervention will be subsequently tested in a Phase II study

User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals

[EN] Objective:Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX). Methods:We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment. Results:The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7 +/- 14.1 and 65 +/- 26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale. Conclusions:The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018¿2020 grant number 825750) and the CANCERLESS project (H2020-SC1-2020-Single-Stage-RTD grant number 965351), both funded by the European Union¿s Horizon 2020 research and innovation programme. Also, it was partially supported by the ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017¿ 2020, grant number PID2019-104978RB-I00)Blanes-Selva, V.; Asensio-Cuesta, S.; Doñate-Martínez, A.; Pereira Mesquita, F.; Garcia-Gomez, JM. (2023). User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals. Digital Health. 9:1-13. https://doi.org/10.1177/20552076221150735113

Advance Care Planning in Cancer Patient - Caregiver DYADS

For many, a cancer diagnosis signals death\u27s inevitability and elicits much existential concern. In the quest for life prolongation, many are offered or seek life-sustaining treatments, fail to appreciate a declining trajectory and lack the opportunity to seek information or plan meaningfully for their future. Advance care planning (ACP) provides an avenue for patients and their caregivers to plan for future care. ACP is defined ‘as a process that supports adults at any age or stage of health in understanding and sharing their personal values, life goals and preferences regarding future medical care’ and is a key quality indicator in cancer care. An increased emphasis is now placed on exploring values and beliefs to ensure alignment with the choices made relating to treatment decisions and end-of-life desirables. The uptake of ACP in cancer remains poor due to patient, caregiver, practitioner, and operational factors. For the clinician, the challenge remains as to how best to maintain hope, despite provoking and honest conversations. Increasingly, novel interventions are being developed to promote uptake in ACP. This includes the vignette technique (VT), whereby patients and/or caregivers are exposed to future scenarios in written or video material. My studies were the first to explore the use of video vignettes to explore values conversations between patient-caregiver dyads. These studies described older participants as more likely to identify with ACP and values conversations, the importance of ACP in improving patient-caregiver concordance in communication and that cancer patients concurrently postured vulnerability and resilience, despite conflicting emotions and experiences. I highlight that ACP requires contextualisation of individual situations and values and should focus on achieving meaningful outcomes beyond completing documents. Future research will focus on improving and measuring concordance in communication as an outcome for ACP and techniques to enhance ACP engagement in younger cancer patients

Implementing the battery-operated hand-held fan as an evidence-based, non-pharmacological intervention for chronic breathlessness in patients with chronic obstructive pulmonary disease (COPD): a qualitative study of the views of specialist respiratory clinicians

INTRODUCTION: The battery-operated hand-held fan ('fan') is an inexpensive and portable non-pharmacological intervention for chronic breathlessness. Evidence from randomised controlled trials suggests the fan reduces breathlessness intensity and improves physical activity in patients with a range of advanced chronic conditions. Qualitative data from these trials suggests the fan may also reduce anxiety and improve daily functioning for many patients. This study aimed to explore barriers and facilitators to the fan's implementation in specialist respiratory care as a non-pharmacological intervention for chronic breathlessness in patients with chronic obstructive pulmonary disease (COPD). METHODS: A qualitative approach was taken, using focus groups. Participants were clinicians from any discipline working in specialist respiratory care at two hospitals. Questions asked about current fan-related practice and perceptions regarding benefits, harms and mechanisms, and factors influencing its implementation. Analysis used a mixed inductive/deductive approach. RESULTS: Forty-nine participants from nursing (n = 30), medical (n = 13) and allied health (n = 6) disciplines participated across 9 focus groups. The most influential facilitator was a belief that the fan's benefits outweighed disadvantages. Clinicians' beliefs about the fan's mechanisms determined which patient sub-groups they targeted, for example anxious or palliative/end-stage patients. Barriers to implementation included a lack of clarity about whose role it was to implement the fan, what advice to provide patients, and limited access to fans in hospitals. Few clinicians implemented the fan for acute-on-chronic breathlessness or in combination with other interventions. CONCLUSION: Implementation of the fan in specialist respiratory care may require service- and clinician-level interventions to ensure it is routinely recommended as a first-line intervention for chronic breathlessness in patients for whom this symptom is of concern, regardless of COPD stage

Interventions to Prevent Potentially Avoidable Hospitalizations:A Mixed Methods Systematic Review

BACKGROUND: The demand for healthcare is increasing due to an aging population, more people living with chronic diseases and medical comorbidities. To manage this demand, political institutions call for action to reduce the potentially avoidable hospitalizations. Quantitative and qualitative aspects should be considered to understand how and why interventions work, and for whom. The aim of this mixed methods systematic review was to identify and synthesize evidence on interventions targeting avoidable hospitalizations from the perspectives of the citizens and the healthcare professionals to improve the preventive healthcare services. METHODS AND RESULTS: A mixed methods systematic review was conducted following the JBI methodology using a convergent integrated approach to synthesis. The review protocol was registered in PROSPERO, reg. no. CRD42020134652. A systematic search was undertaken in six databases. In total, 45 articles matched the eligibility criteria, and 25 of these (five qualitative studies and 20 quantitative studies) were found to be of acceptable methodological quality. From the 25 articles, 99 meaning units were extracted. The combined evidence revealed four categories, which were synthesized into two integrated findings: (1) Addressing individual needs through care continuity and coordination prevent avoidable hospitalizations and (2) Recognizing preventive care as an integrated part of the healthcare work to prevent avoidable hospitalizations. CONCLUSIONS: The syntheses highlight the importance of addressing individual needs through continuous and coordinated care practices to prevent avoidable hospitalizations. Engaging healthcare professionals in preventive care work and considering implications for patient safety may be given higher priority. Healthcare administers and policy-makers could support the delivery of preventive care through targeted educational material aimed at healthcare professionals and simple web-based IT platforms for information-sharing across healthcare settings. The findings are an important resource in the development and implementation of interventions to prevent avoidable hospitalizations, and may serve to improve patient safety and quality in preventive healthcare services. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=134652, identifier: CRD42020134652

Clinical Decision Support Systems for Palliative Care Referral: Design and Evaluation of Frailty and Mortality Predictive Models

[ES] Los Cuidados Paliativos (PC) son cuidados médicos especializados cuyo objetivo esmejorar la calidad de vida de los pacientes con enfermedades graves. Históricamente,se han aplicado a los pacientes en fase terminal, especialmente a los que tienen undiagnóstico oncológico. Sin embargo, los resultados de las investigaciones actualessugieren que la PC afecta positivamente a la calidad de vida de los pacientes condiferentes enfermedades. La tendencia actual sobre la PC es incluir a pacientes nooncológicos con afecciones como la EPOC, la insuficiencia de funciones orgánicas ola demencia. Sin embargo, la identificación de los pacientes con esas necesidades escompleja, por lo que se requieren herramientas alternativas basadas en datos clínicos. La creciente demanda de PC puede beneficiarse de una herramienta de cribadopara identificar a los pacientes con necesidades de PC durante el ingreso hospitalario.Se han propuesto varias herramientas, como la Pregunta Sorpresa (SQ) o la creaciónde diferentes índices y puntuaciones, con distintos grados de éxito. Recientemente,el uso de algoritmos de inteligencia artificial, en concreto de Machine Learning (ML), ha surgido como una solución potencial dada su capacidad de aprendizaje a partirde las Historias Clínicas Electrónicas (EHR) y con la expectativa de proporcionarpredicciones precisas para el ingreso en programas de PC. Esta tesis se centra en la creación de herramientas digitales basadas en ML para la identificación de pacientes con necesidades de cuidados paliativos en el momento del ingreso hospitalario. Hemos utilizado la mortalidad y la fragilidad como los dos criterios clínicos para la toma de decisiones, siendo la corta supervivencia y el aumento de la fragilidad, nuestros objetivos para hacer predicciones. También nos hemos centrado en la implementación de estas herramientas en entornos clínicos y en el estudio de su usabilidad y aceptación en los flujos de trabajo clínicos. Para lograr estos objetivos, en primer lugar, estudiamos y comparamos algoritmos de ML para la supervivencia a un año en pacientes adultos durante el ingreso hospitalario. Para ello, definimos una variable binaria a predecir, equivalente a la SQ y definimos el conjunto de variables predictivas basadas en la literatura. Comparamos modelos basados en Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) y Multilayer Perceptron (MLP), atendiendo a su rendimiento, especialmente al Área bajo la curva ROC (AUC ROC). Además, obtuvimos información sobre la importancia de las variables para los modelos basados en árboles utilizando el criterio GINI. En segundo lugar, estudiamos la medición de la fragilidad de la calidad de vida(QoL) en los candidatos a la intervención en PC. Para este segundo estudio, redujimosla franja de edad de la población a pacientes ancianos (≥ 65 años) como grupo objetivo. A continuación, creamos tres modelos diferentes: 1) la adaptación del modelo demortalidad a un año para pacientes ancianos, 2) un modelo de regresión para estimarel número de días desde el ingreso hasta la muerte para complementar los resultadosdel primer modelo, y finalmente, 3) un modelo predictivo del estado de fragilidad aun año. Estos modelos se compartieron con la comunidad académica a través de unaaplicación web b que permite la entrada de datos y muestra la predicción de los tresmodelos y unos gráficos con la importancia de las variables. En tercer lugar, propusimos una versión del modelo de mortalidad a un año enforma de calculadora online. Esta versión se diseñó para maximizar el acceso de losprofesionales minimizando los requisitos de datos y haciendo que el software respondiera a las plataformas tecnológicas actuales. Así pues, se eliminaron las variablesadministrativas específicas de la fuente de datos y se trabajó en un proceso para minimizar las variables de entrada requeridas, manteniendo al mismo tiempo un ROCAUC elevado del modelo. Como resultado, e[CA] Les Cures Pal·liatives (PC) són cures mèdiques especialitzades l'objectiu de les qualsés millorar la qualitat de vida dels pacients amb malalties greus. Històricament, s'hanaplicat als pacients en fase terminal, especialment als quals tenen un diagnòstic oncològic. No obstant això, els resultats de les investigacions actuals suggereixen que lesPC afecten positivament a la qualitat de vida dels pacients amb diferents malalties. Latendència actual sobre les PC és incloure a pacients no oncològics amb afeccions comla malaltia pulmonar obstructiva crònica, la insuficiència de funcions orgàniques o lademència. No obstant això, la identificació dels pacients amb aqueixes necessitats éscomplexa, per la qual cosa es requereixen eines alternatives basades en dades clíniques. La creixent demanda de PC pot beneficiar-se d'una eina de garbellat per a identificar als pacients amb necessitats de PC durant l'ingrés hospitalari. S'han proposatdiverses eines, com la Pregunta Sorpresa (SQ) o la creació de diferents índexs i puntuacions, amb diferents graus d'èxit. Recentment, l'ús d'algorismes d'intel·ligènciaartificial, en concret de Machine Learning (ML), ha sorgit com una potencial soluciódonada la seua capacitat d'aprenentatge a partir de les Històries Clíniques Electròniques (EHR) i amb l'expectativa de proporcionar prediccions precises per a l'ingrés enprogrames de PC. Aquesta tesi se centra en la creació d'eines digitals basades en MLper a la identificació de pacients amb necessitats de cures pal·liatives durant l'ingréshospitalari. Hem utilitzat mortalitat i fragilitat com els dos criteris clínics per a lapresa de decisions, sent la curta supervivència i la major fragilitat els nostres objectiusa predir. Després, ens hem centrat en la seua implementació en entorns clínics i hemestudiat la seua usabilitat i acceptació en els fluxos de treball clínics.Aquesta tesi se centra en la creació d'eines digitals basades en ML per a la identificació de pacients amb necessitats de cures pal·liatives en el moment de l'ingrés hospitalari. Hem utilitzat la mortalitat i la fragilitat com els dos criteris clínics per ala presa de decisions, sent la curta supervivència i l'augment de la fragilitat, els nostresobjectius per a fer prediccions. També ens hem centrat en la implementació d'aquesteseines en entorns clínics i en l'estudi de la seua usabilitat i acceptació en els fluxos detreball clínics. Per a aconseguir aquests objectius, en primer lloc, estudiem i comparem algorismesde ML per a la supervivència a un any en pacients adults durant l'ingrés hospitalari.Per a això, definim una variable binària a predir, equivalent a la SQ i definim el conjuntde variables predictives basades en la literatura. Comparem models basats en Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) i Multilayer Perceptron (MLP), atenent el seu rendiment,especialment a l'Àrea sota la corba ROC (AUC ROC). A més, vam obtindre informaciósobre la importància de les variables per als models basats en arbres utilitzant el criteri GINI. En segon lloc, estudiem el mesurament de la fragilitat de la qualitat de vida (QoL)en els candidats a la intervenció en PC. Per a aquest segon estudi, vam reduir lafranja d'edat de la població a pacients ancians (≥ 65 anys) com a grup objectiu. Acontinuació, creem tres models diferents: 1) l'adaptació del model de mortalitat a unany per a pacients ancians, 2) un model de regressió per a estimar el nombre de dies desde l'ingrés fins a la mort per a complementar els resultats del primer model, i finalment,3) un model predictiu de l'estat de fragilitat a un any. Aquests models es van compartiramb la comunitat acadèmica a través d'una aplicació web c que permet l'entrada dedades i mostra la predicció dels tres models i uns gràfics amb la importància de lesvariables. En tercer lloc, vam proposar una versió del model de mortalitat a un any en formade calculadora en línia. Aquesta versió es va di[EN] Palliative Care (PC) is specialized medical care that aims to improve patients' quality of life with serious illnesses. Historically, it has been applied to terminally ill patients, especially those with oncologic diagnoses. However, current research results suggest that PC positively affects the quality of life of patients with different conditions. The current trend on PC is to include non-oncological patients with conditions such as Chronic Obstructive Pulmonary Disease (COPD), organ function failure or dementia. However, the identification of patients with those needs is complex, and therefore alternative tools based on clinical data are required. The growing demand for PC may benefit from a screening tool to identify patients with PC needs during hospital admission. Several tools, such as the Surprise Question (SQ) or the creation of different indexes and scores, have been proposed with varying degrees of success. Recently, the use of artificial intelligence algorithms, specifically Machine Learning (ML), has arisen as a potential solution given their capacity to learn from the Electronic Health Records (EHRs) and with the expectation to provide accurate predictions for admission to PC programs. This thesis focuses on creating ML-based digital tools for identifying patients with palliative care needs at hospital admission. We have used mortality and frailty as the two clinical criteria for decision-making, being short survival and increased frailty, as our targets to make predictions. We also have focused on implementing these tools in clinical settings and studying their usability and acceptance in clinical workflows. To accomplish these objectives, first, we studied and compared ML algorithms for one-year survival in adult patients during hospital admission. To do so, we defined a binary variable to predict, equivalent to the SQ and defined the set of predictive variables based on literature. We compared models based on Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP), attending to their performance, especially to the Area under the ROC curve (AUC ROC). Additionally, we obtained information on the importance of variables for tree-based models using the GINI criterion. Second, we studied frailty measurement of Quality of Life (QoL) in candidates for PC intervention. For this second study, we narrowed the age of the population to elderly patients (≥ 65 years) as the target group. Then we created three different models: 1) for the adaptation of the one-year mortality model for elderly patients, 2) a regression model to estimate the number of days from admission to death to complement the results of the first model, and finally, 3) a predictive model for frailty status at one year. These models were shared with the academic community through a web application a that allows data input and shows the prediction from the three models and some graphs with the importance of the variables. Third, we proposed a version of the 1-year mortality model in the form of an online calculator. This version was designed to maximize access from professionals by minimizing data requirements and making the software responsive to the current technological platforms. So we eliminated the administrative variables specific to the dataset source and worked on a process to minimize the required input variables while maintaining high the model's AUC ROC. As a result, this model retained most of the predictive power and required only seven bed-side inputs. Finally, we evaluated the Clinical Decision Support System (CDSS) web tool on PC with an actual set of users. This evaluation comprised three domains: evaluation of participant's predictions against the ML baseline, the usability of the graphical interface, and user experience measurement. A first evaluation was performed, followed by a period of implementation of improvements and corrections to the plaBlanes Selva, V. (2022). Clinical Decision Support Systems for Palliative Care Referral: Design and Evaluation of Frailty and Mortality Predictive Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19099

Catalan model of care forpeople with frailty, complex chronic (CCP) and advanced chronic (ACP) conditions

Atenció hospitalària; Malalts crònics; Atenció individualitzadaAtención hospitalaria; Enfermos crónicos; Atención individualizadaHospital care; Chronically ill; Individualized attentionDocument que vol promoure un model d'atenció més individualitzat per a les persones amb cronicitat complexa i/o avançada.Documento que desea promover un modelo de atención más individualizado para las personas con cronicidad compleja y/o avanzada.Document that promotes a more individualized care model for people with complex and/or advanced chronicity

Dying at home in Norway: Health care service utilization in the final months of life

Background: Although many people prefer to die at home, few people die at home in Norway. We know little about sociodemographic characteristics of people who die at home, the extent of palliative end of life care provided by health care services and whether they enable people to die at home. Aim: Investigate individual characteristics of decedents, health care service utilization in the last three months of life and associations with dying at home. Method: Population-based registry data from the Norwegian Cause of Death Registry were linked with other Norwegian registries, covering all decedents in Norway within 2012-2013, with data from the last 13 weeks before death. Paper 1 investigated individual sociodemographic factors and estimated potentially planned home deaths that occurred at home. In Paper 2, trajectories of home nursing services and admissions to short-term skilled nursing facilities were estimated. Potentially planned home deaths for deaths in all locations were also estimated. Paper 3 investigated follow-up from general practitioners, OOH services and hospitalizations. Associations with home deaths and factors of interest were estimated by regression analyses in all papers. Results: Overall, 15% of the total population (22% of the community-dwelling) died at home. We estimated that 24% of community-dwelling people (16% of total population) had deaths that were potentially planned to occur at home, regardless of actual location of death; nearly a third occurred at home. The most common causes of death at home were circulatory disease (35%) and cancer (22%). The predicted probability of dying at home increased with 39% when cause of death was symptoms/ill-defined and 9% for external causes of death but decreased with 12% for cancer compared to circulatory disease. In total, 18% of men and 12% of women died at home. There was a trend where younger decedents were more likely to die at home, ranging from a 39% predicted probability in people <40 years to 8% in those ≥90 years. For the community-dwelling, we estimated four trajectories of home nursing services and four short-term skilled nursing facility trajectories. Almost half received no home nursing. A quarter received a high level of home nursing; almost 7 hrs/wk. This was the only home nursing service trajectory associated with dying at home compared to hospital (aRRR 1.29). A fifth had decreasing home nursing and about 8% accelerating home nursing towards the end of life. Almost 70% had a low probability of having a short-term skilled nursing facility stay. Another 7% had intermediate probability, 16% escalating probability and 8% increasing probability of a short-term skilled nursing facility stay. Trajectories of increasing (aRRR 0.40), escalating (aRRR 0.32) and intermediate skilled nursing facility (aRRR 0.65) were associated with reduced likelihood of dying at home. Almost half the people with causes of death that predicted a potentially planned home death followed the high home nursing service trajectory. Nearly all people with potentially planned home deaths followed the trajectory with low probability of skilled nursing facility stays. During the last 13 weeks, 14% of the total population received ≥1 GP home visit, 43% ≥1 GP office consultations and 41% had GP interdisciplinary collaboration. A minority had OOH consultations, while hospitalizations escalated. During the last four weeks, 7% of patients (10% of community-dwelling) received ‘appropriate’ follow-up with ≥1 home visit when the GP had ≥1 interdisciplinary collaboration. GP home visits (1: 3%; ≥2: 7%) and interdisciplinary collaboration (1: 2%; ≥2: 5%) increased the predicted probability of dying at home in a dose-dependent manner. Health care services where the person had to leave home, including GP office consultations, OOH consultations and hospitalizations reduced the predicted probability of dying at home. Conclusions and implications: Few people died at home and many home deaths appear to have been unplanned. At a population level, follow-up from GPs and home nursing services at the end of life may enable people to die at home. Our results imply that most people dying in Norway do not receive enough ‘appropriate’ follow-up to make a home death feasible. The potential for delivering palliative end of life care at home is not utilized. To enable more home deaths, we should start talking about our preferences regarding end of life care and place of death. The way forward must include both an individual and a system perspective to give dying people a real choice about where they spend the end of life

General practitioners’ referrals to specialist health services. Exploring elements and factors in the referral process having an impact om patients’ access to specialty care

Background: The referral process between first and second line health care is complex and multidimensional, with medical, interpersonal, logistical, legal, as well as indeterminate aspects. There is a great need to explore the various elements and factors having an impact on the referral process. Main objectives: The objective of this thesis has been to study general practitioners´ and hospital consultants´ role in the referral process, from the moment the GP decides to refer a patient to hospital, until the hospital consultant assesses the referral and prioritizes the patient for further investigation or treatment in specialist health care. The specific aims for the three sub-studies were to identify and describe 1) general practitioners’ reflections on and attitudes to the referral process and their cooperation with hospital consultants, 2) hospital consultants’ reflections on and attitudes to the referral process and their cooperation with general practitioners, and 3) potential characteristics of GPs’ referral practice by investigating their opinions about referring and their self-reported experiences of what they do when they refer. Design and methods: The first two parts were qualitative studies. General practitioners and hospital consultants were interviewed and a systematic text condensation method was used for analysis. The third part was a quantitative cross-sectional study of GPs’ impressions and feelings about referring and a registration of a selection of data on the work done by referring to hospital during one month, analysed by using a principal component analysis and abduction. Results: The GPs expressed a dual responsibility towards patients and the national health system. Many experienced pressure from patients to be referred; the younger doctors especially specified this as a frequent reason for a referral. All the participants expressed a willingness to change according to guidelines, as long as such guidelines were the result of a consensus between hospital specialists and general practitioners. The hospital consultants experienced a considerable workload assessing referrals and prioritizing patients for further investigation and treatment. They emphasized the importance of precise referrals as essential for a reasonable and fair prioritization process. All focused on the importance of good communication and cooperation with the referring GPs. Good referrals were considered to make the prioritization process easier. As for the typologies, younger male GPs experienced more heavy work-load and patient pressure when they referred to hospital. The experienced female GPs referred in a more patient-centred way, completing the referrals during the consultation with the patient present. Conclusions and implications: Many factors have an impact on the referral process and the individual referral rates. Good communication and cooperation by phone or electronically between hospital consultants and GPs are important factors to make the referral process more balanced, and the participants more like partners. More use of electronic decision support systems for the referring physicians can make this process more standardized and predictable for both partners. Educating and training GPs in professional competence and personal confidence as well as a more patient-centred way of referring, making priority decisions and completing the referrals during the consultation may be timesaving for the actors and can be associated with less work-load