primary care consultation
Recently Published Documents


TOTAL DOCUMENTS

51
(FIVE YEARS 11)

H-INDEX

15
(FIVE YEARS 1)

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Sophia Abner ◽  
Clare L. Gillies ◽  
Sharmin Shabnam ◽  
Francesco Zaccardi ◽  
Samuel Seidu ◽  
...  

Abstract Objective To assess trends in primary and specialist care consultation rates and average length of consultation by cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), or cardiometabolic multimorbidity exposure status. Methods Observational, retrospective cohort study used linked Clinical Practice Research Datalink primary care data from 01/01/2000 to 31/12/2018 to assess consultation rates in 141,328 adults with newly diagnosed T2DM, with or without CVD. Patients who entered the study with either a diagnosis of T2DM or CVD and later developed the second condition during the study are classified as the cardiometabolic multimorbidity group. Face to face primary and specialist care consultations, with either a nurse or general practitioner, were assessed over time in subjects with T2DM, CVD, or cardiometabolic multimorbidity. Changes in the average length of consultation in each group were investigated. Results 696,255 (mean 4.9 years [95% CI, 2.02–7.66]) person years of follow up time, there were 10,221,798 primary and specialist care consultations. The crude rate of primary and specialist care consultations in patients with cardiometabolic multimorbidity (N = 11,881) was 18.5 (95% CI, 18.47–18.55) per person years, 13.5 (13.50, 13.52) in patients with T2DM only (N = 83,094) and 13.2 (13.18, 13.21) in those with CVD (N = 57,974). Patients with cardiometabolic multimorbidity had 28% (IRR 1.28; 95% CI: 1.27, 1.31) more consultations than those with only T2DM. Patients with cardiometabolic multimorbidity had primary care consultation rates decrease by 50.1% compared to a 45.0% decrease in consultations for those with T2DM from 2000 to 2018. Specialist care consultation rates in both groups increased from 2003 to 2018 by 33.3% and 54.4% in patients with cardiometabolic multimorbidity and T2DM, respectively. For patients with T2DM the average consultation duration increased by 36.0%, in patients with CVD it increased by 74.3%, and in those with cardiometabolic multimorbidity it increased by 37.3%. Conclusions Annual primary care consultation rates for individuals with T2DM, CVD, or cardiometabolic multimorbidity have fallen since 2000, while specialist care consultations and average consultation length have both increased. Individuals with cardiometabolic multimorbidity have significantly more consultations than individuals with T2DM or CVD alone. Service redesign of health care delivery needs to be considered for people with cardiometabolic multimorbidity to reduce the burden and health care costs.


PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254644
Author(s):  
Stefanie Stark ◽  
Lukas Worm ◽  
Marie Kluge ◽  
Marco Roos ◽  
Larissa Burggraf

Background Primary care consultation is significantly influenced by communication between the General Practitioner (GP) and their patients. Hypothesising that patient satisfaction can be tested based on an expectation-experience comparison, the aim of this article is to discuss the influence of communication on patient satisfaction. Methods A standardised questionnaire was developed striving for a universal primary care survey tool that focuses on patient satisfaction in the context of patient-centred-communication. The sample consisted of 14 German GPs with 80 patients each (n = 1120). Due to the inclusion in an overarching cluster-randomised-study (CRT), the medical practices to be examined were divided into intervention and control groups. The intervention was developed as a reflective training on patient-centred communication. Results The results in the present sample show no correlation between patient-centred-communication and patient satisfaction. There are also no significant differences between the intervention and control group. Discussion The results raise the question to what extent patient satisfaction can be shaped significantly through patient-centred-communication. The presented project represents part of the basic research in general medical care research and contributes to the transparent processing of theoretical assumptions. With the results described here, communication models with a focus on patient centredness can be evaluated with regard to their practical relevance and transferability.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 757-757
Author(s):  
M. Al-Attar ◽  
W. J. Gregory ◽  
J. Mcbeth ◽  
W. Dixon

Background:Patients with Axial Spondyloarthritis (AxSpA) often suffer a significant delay to diagnosis. This is associated with poorer outcomes in quality of life, functional capabilities and work productivity [1]. These patients are frequent consulters to primary care in the years preceding rheumatology referral [2]. We hypothesise that analysis of primary care consultation patterns may identify as-yet undiagnosed disease, and suggest that implementing an automated diagnostic algorithm may support early action in primary care.Objectives:To undertake a preliminary exploration of primary care consultation patterns in patients with a delayed diagnosis of AxSpA and identify themes for further research.Methods:The study was run in Salford, UK, where unique linkage exists across electronic health records (EHR) from primary and secondary care. A dataset of patients with AxSpA was obtained from 2018-2020 hospital physiotherapy clinic records. Ten patients with a time to diagnosis ≥ 5 years were randomly selected for this exploratory analysis. Diagnostic delay was calculated based on rheumatology clinic letter documentation. Age, sex, and HLA-B27 status were recorded. All “Problem” codes from the primary care EHR up to the point of diagnosis were manually reviewed.Results:Age at diagnosis was 32-49 years with seven males and three females. Seven were HLA-B27 positive. The average delay to diagnosis was 15.8 years (range 5-30).On average, patients had 15 primary care consultations (range 5-24) between first coded AxSpA-related symptom and rheumatology referral. Around half of these codes were potentially AxSpA-related (for example, see Figure 1).Six patients had a coded history of back pain. Two patients presented with other axial symptoms, including: rib pain, MSK chest pain and sciatica.Five patients presented with peripheral joint symptoms, including: ankle pain, shoulder pain, knee problem, pain in arm, medial epicondylitis elbow, hip pain and groin pain. Of these, four had multiple presentations and three had a previous visit with axial pain.Two patients had uveitis preceding axial symptoms. One patient had peripheral joint symptoms (hip pain) preceding uveitis.Inconsistent codes were used for the same problem presenting at different times in nine cases, including: back pain, backache, low back pain, lower back pain.Other relevant codes were used in seven cases, including: stiffness, arthritis, saw physiotherapist and referred to pain clinic.Figure 1 illustrates the consultation pattern for a male patient who first presented to primary care with back pain at the age of 35. Despite a relatively typical presentation, his diagnosis was made incidentally 10 years later after an ESR was checked for unrelated reasons. He was significantly disabled in function at the point of being referred to rheumatology.Conclusion:Our preliminary analysis suggests that patients with a delayed diagnosis of AxSpA have repeated primary care visits with potentially recognisable symptoms of their disease. These findings support the feasibility of future automated detection, with areas of focus including recognition of non-back pain axial symptoms, extra-articular manifestations, and peripheral joint symptoms.Whilst half of presentations were not directly AxSpA-related, modern machine learning techniques have the ability to explore whether the pattern or frequency of these consultations are relevant to identifying undiagnosed disease. Such methods can also highlight patterns obscured by extensive data sets and inconsistent coding, with opportunity for implementation back into primary care.References:[1]Redeker I et al. Determinants of diagnostic delay in axial spondyloarthritis: an analysis based on linked claims and patient-reported survey data. Rheumatology (Oxford) 2019;58:1634–8.[2]Yi E et al. Clinical, Economic, and Humanistic Burden Associated With Delayed Diagnosis of Axial Spondyloarthritis: A Systematic Review. Rheumatol Ther. 2020;7(1):65–87Disclosure of Interests:None declared.


Author(s):  
Thomas Pernin ◽  
Melissa Dominicé Dao ◽  
Boris Cheval ◽  
Delphine Courvoisier ◽  
Dagmar M. Haller ◽  
...  

AbstractUniversity and college students present specific health issues with vulnerabilities related to mental health and sexual health, risk-taking behaviors, and delayed access to primary care. A new student outpatient clinic was created in September 2016 at Geneva University Hospitals to respond to the health needs of this population. We present here the clinical management framework developed for a primary care consultation with students. A 3-step approach (ABC) was designed by expert consensus using different sources. A post-consultation satisfaction survey was conducted among students attending the clinic. The approach proposed 3 steps comprising general information, social evaluation, and preventive care. The importance of offering modern means of communication (online appointments, email exchanges with clinicians) was emphasized by experts. The question of cultural identity and connectedness was also addressed, especially for international students or those coming from a different Swiss region. In November 2018, a survey conducted among 128 patients out of 449 consultations showed that 94.5% agreed or totally agreed to recommend the consultation to fellow students, and 89% considered that care providers adequately addressed their specific student-related issues. A specific approach is needed in primary care for university/college students requiring particular competences across several domains. Our findings suggest that our approach is effective to cover the main health challenges faced by students. A comparison of the outcomes of this novel 3-step primary care consultation approach with non-structured approaches should be evaluated in future studies, including clinician’s satisfaction, elements of patient’s participation to governance, and medico-economic aspects.


2020 ◽  
Vol 7 ◽  
Author(s):  
Antonios Bertsias ◽  
Emmanouil Symvoulakis ◽  
Chariklia Tziraki ◽  
Symeon Panagiotakis ◽  
Lambros Mathioudakis ◽  
...  

Introduction: Dementia severely affects the quality of life of patients and their caregivers; however, it is often not adequately addressed in the context of a primary care consultation, especially in patients with multi-morbidity.Study Population and Methods: A cross-sectional study was conducted between March-2013 and December-2014 among 3,140 consecutive patients aged >60 years visiting 14 primary health care practices in Crete, Greece. The Mini-Mental-State-Examination [MMSE] was used to measure cognitive status using the conventional 24-point cut-off. Participants who scored low on MMSE were matched with a group of elders scoring >24 points, according to age and education; both groups underwent comprehensive neuropsychiatric and neuropsychological assessment. For the diagnosis of dementia and Mild-Cognitive-Impairment (MCI), the Diagnostic and Statistical Manual-of-Mental-Disorders (DSM-IV) criteria and the International-Working-Group (IWG) criteria were used. Chronic conditions were categorized according to ICD-10 categories. Logistic regression was used to provide associations between chronic illnesses and cognitive impairment according to MMSE scores. Generalized Linear Model Lasso Regularization was used for feature selection in MMSE items. A two-layer artificial neural network model was used to classify participants as impaired (dementia/MCI) vs. non-impaired.Results: In the total sample of 3,140 participants (42.1% men; mean age 73.7 SD = 7.8 years), low MMSE scores were identified in 645 (20.5%) participants. Among participants with low MMSE scores 344 (54.1%) underwent comprehensive neuropsychiatric evaluation and 185 (53.8%) were diagnosed with Mild-Cognitive-Impairment (MCI) and 118 (34.3%) with dementia. Mental and behavioral disorders (F00-F99) and diseases of the nervous system (G00-G99) increased the odds of low MMSE scores in both genders. Generalized linear model lasso regularization indicated that 7/30 MMSE questions contributed the most to the classification of patients as impaired (dementia/MCI) vs. non-impaired with a combined accuracy of 82.0%. These MMSE items were questions 5, 13, 19, 20, 22, 23, and 26 of the Greek version of MMSE assessing orientation in time, repetition, calculation, registration, and visuo-constructive ability.Conclusions: Our study identified certain chronic illness-complexes that were associated with low MMSE scores within the context of primary care consultation. Also, our analysis indicated that seven MMSE items provide strong evidence for the presence of dementia or MCI.


10.2196/18109 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18109
Author(s):  
Geronimo Jimenez ◽  
Shilpa Tyagi ◽  
Tarig Osman ◽  
Pier Spinazze ◽  
Rianne van der Kleij ◽  
...  

Background Digital medical interview assistant (DMIA) systems, also known as computer-assisted history taking (CAHT) systems, have the potential to improve the quality of care and the medical consultation by exploring more patient-related aspects without time constraints and, therefore, acquiring more and better-quality information prior to the face-to-face consultation. The consultation in primary care is the broadest in terms of the amount of topics to be covered and, at the same time, the shortest in terms of time spent with the patient. Objective Our aim is to explore how DMIA systems may be used specifically in the context of primary care, to improve the consultations for diabetes and depression, as exemplars of chronic conditions. Methods A narrative review was conducted focusing on (1) the characteristics of the primary care consultation in general, and for diabetes and depression specifically, and (2) the impact of DMIA and CAHT systems on the medical consultation. Through thematic analysis, we identified the characteristics of the primary care consultation that a DMIA system would be able to improve. Based on the identified primary care consultation tasks and the potential benefits of DMIA systems, we developed a sample questionnaire for diabetes and depression to illustrate how such a system may work. Results A DMIA system, prior to the first consultation, could aid in the essential primary care tasks of case finding and screening, diagnosing, and, if needed, timely referral to specialists or urgent care. Similarly, for follow-up consultations, this system could aid with the control and monitoring of these conditions, help check for additional health issues, and update the primary care provider about visits to other providers or further testing. Successfully implementing a DMIA system for these tasks would improve the quality of the data obtained, which means earlier diagnosis and treatment. Such a system would improve the use of face-to-face consultation time, thereby streamlining the interaction and allowing the focus to be the patient's needs, which ultimately would lead to better health outcomes and patient satisfaction. However, for such a system to be successfully incorporated, there are important considerations to be taken into account, such as the language to be used and the challenges for implementing eHealth innovations in primary care and health care in general. Conclusions Given the benefits explored here, we foresee that DMIA systems could have an important impact in the primary care consultation for diabetes and depression and, potentially, for other chronic conditions. Earlier case finding and a more accurate diagnosis, due to more and better-quality data, paired with improved monitoring of disease progress should improve the quality of care and keep the management of chronic conditions at the primary care level. A somewhat simple, easily scalable technology could go a long way to improve the health of the millions of people affected with chronic conditions, especially if working in conjunction with already-established health technologies such as electronic medical records and clinical decision support systems.


2020 ◽  
Author(s):  
Geronimo Jimenez ◽  
Shilpa Tyagi ◽  
Tarig Osman ◽  
Pier Spinazze ◽  
Rianne van der Kleij ◽  
...  

BACKGROUND Digital medical interview assistant (DMIA) systems, also known as computer-assisted history taking (CAHT) systems, have the potential to improve the quality of care and the medical consultation by exploring more patient-related aspects without time constraints and, therefore, acquiring more and better-quality information prior to the face-to-face consultation. The consultation in primary care is the broadest in terms of the amount of topics to be covered and, at the same time, the shortest in terms of time spent with the patient. OBJECTIVE Our aim is to explore how DMIA systems may be used specifically in the context of primary care, to improve the consultations for diabetes and depression, as exemplars of chronic conditions. METHODS A narrative review was conducted focusing on (1) the characteristics of the primary care consultation in general, and for diabetes and depression specifically, and (2) the impact of DMIA and CAHT systems on the medical consultation. Through thematic analysis, we identified the characteristics of the primary care consultation that a DMIA system would be able to improve. Based on the identified primary care consultation tasks and the potential benefits of DMIA systems, we developed a sample questionnaire for diabetes and depression to illustrate how such a system may work. RESULTS A DMIA system, prior to the first consultation, could aid in the essential primary care tasks of case finding and screening, diagnosing, and, if needed, timely referral to specialists or urgent care. Similarly, for follow-up consultations, this system could aid with the control and monitoring of these conditions, help check for additional health issues, and update the primary care provider about visits to other providers or further testing. Successfully implementing a DMIA system for these tasks would improve the quality of the data obtained, which means earlier diagnosis and treatment. Such a system would improve the use of face-to-face consultation time, thereby streamlining the interaction and allowing the focus to be the patient's needs, which ultimately would lead to better health outcomes and patient satisfaction. However, for such a system to be successfully incorporated, there are important considerations to be taken into account, such as the language to be used and the challenges for implementing eHealth innovations in primary care and health care in general. CONCLUSIONS Given the benefits explored here, we foresee that DMIA systems could have an important impact in the primary care consultation for diabetes and depression and, potentially, for other chronic conditions. Earlier case finding and a more accurate diagnosis, due to more and better-quality data, paired with improved monitoring of disease progress should improve the quality of care and keep the management of chronic conditions at the primary care level. A somewhat simple, easily scalable technology could go a long way to improve the health of the millions of people affected with chronic conditions, especially if working in conjunction with already-established health technologies such as electronic medical records and clinical decision support systems.


Sign in / Sign up

Export Citation Format

Share Document