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How should electronic health records be designed? A cross-sectional study in patients with psoriasis.

ABSTRACT: BACKGROUND:Electronic health records (EHRs) are promising tools for routine care. These applications might not only enhance the interaction between patient and physician but also support therapy management. This is crucial in complex and chronic conditions like psoriasis. However, EHRs can only unfold their full potential when being accepted by the users. Therefore, this study aims to analyse how EHRs should be designed for patients with psoriasis and to identify differences between patient subgroups. METHODS:We developed a questionnaire on the acceptability of EHRs based on literature research and results from focus groups. Participants completed a paper-based or electronic version of the questionnaire. We recruited participants at an outpatient clinic as well as online via patient associations and a social media platform. We analysed data using descriptive statistics and bivariate analyses applying Chi-square and Fisher's exact test. RESULTS:The sample encompassed 187 patients with psoriasis. Data reveals that 84.4% of the participants can think of entering data into an EHR. Participants prefer entering data at home (72.2%) instead of entering data in the waiting room (44.9%) and using an own internet-ready device (laptop/computer: 62.6%; smartphone/tablet: 61.5%) instead of a provided device (46.0%). Altogether, 55.6% of participants would accept entering data on a monthly basis when this lasts between one and 10 minutes and further 27.8% would accept even longer lasting data entry. Data privacy is of great concern (e.g. patient should decide who has access to data: 96.7%). Subgroup analyses reveal differences with regard to age, educational level, burden due to psoriasis, number of internet activities, use of electronic questionnaires and mode of administration. CONCLUSION:The high acceptance of entering data is favourable for the implementation of EHRs. The results suggest technical and structural recommendations: Differences between subgroups support the development of flexible EHRs encompassing a basic module, which is expandable with further add-ons, and compatible to different devices. Furthermore, involving patients by entering data into an EHR requires that physicians communicate open-mindedly with the patient and consider data throughout decision-making. Patients should remain owner of their own health data and decide about its processing.

SUBMITTER: Klein TM

PROVIDER: S-EPMC6849227 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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How should electronic health records be designed? A cross-sectional study in patients with psoriasis.

Klein Toni Maria TM Augustin Matthias M Otten Marina M

BMC medical informatics and decision making 20191112 1

<h4>Background</h4>Electronic health records (EHRs) are promising tools for routine care. These applications might not only enhance the interaction between patient and physician but also support therapy management. This is crucial in complex and chronic conditions like psoriasis. However, EHRs can only unfold their full potential when being accepted by the users. Therefore, this study aims to analyse how EHRs should be designed for patients with psoriasis and to identify differences between pati ...[more]

PMID: 31718653

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Project description:BackgroundElectronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients' ePHR use is important to increase adoption rates and improve the implementation success of ePHRs.ObjectiveThis study aimed to examine factors associated with patients' use of ePHRs in England.MethodsThe unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling.ResultsOf 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB.ConclusionsThis study identified the main factors that affect patients' use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients' preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors.

Project description:BackgroundPatient portals that provide access to electronic health records offer a means for patients to better understand and self-manage their health. Yet, patient access to electronic health records raises many concerns among physicians, and little is known about the use practices and experiences of patients who access their electronic health records via a mature patient portal that has been available for citizens for over five years.ObjectiveWe aimed to identify patients' experiences using a national patient portal to access their electronic health records. In particular, we focused on understanding usability-related perceptions and the benefits and challenges of reading clinical notes written by health care professionals.MethodsData were collected from 3135 patient users of the Finnish My Kanta patient portal through a web-based survey in June 2021 (response rate: 0.7%). Patients received an invitation to complete the questionnaire when they logged out of the patient portal. Respondents were asked to rate the usability of the patient portal, and the ratings were used to calculate approximations of the System Usability Scale score. Patients were also asked about the usefulness of features, and whether they had discussed the notes with health professionals. Open-ended questions were used to ask patients about their experiences of the benefits and challenges related to reading health professionals' notes.ResultsOverall, patient evaluations of My Kanta were positive, and its usability was rated as good (System Usability Scale score approximation: mean 72.7, SD 15.9). Patients found the portal to be the most useful for managing prescriptions and viewing the results of examinations and medical notes. Viewing notes was the most frequent reason (978/3135, 31.2%) for visiting the portal. Benefits of reading the notes mentioned by patients included remembering and understanding what was said by health professionals and the instructions given during an appointment, the convenience of receiving information about health and care, the capability to check the accuracy of notes, and using the information to support self-management. However, there were challenges related to difficulty in understanding medical terminology, incorrect or inadequate notes, missing notes, and usability.ConclusionsPatients actively used medical notes to receive information to follow professionals' instructions to take care of their health, and patient access to electronic health records can support self-management. However, for the benefits to be realized, improvements in the quality and availability of medical professionals' notes are necessary. Providing a standard information structure could help patients find the information they need. Furthermore, linking notes to vocabularies and other information sources could also improve the understandability of medical terminology; patient agency could be supported by allowing them to add comments to their notes, and patient trust of the system could be improved by allowing them to control the visibility of the professionals' notes.

Project description:INTRODUCTION:Electronic Health Records are receiving considerable attention as a valuable tool for managing clinical information. Despite the prospects of Electronic Health Records in developing countries, many pre-implementation assessments target organizational, managerial, and infrastructural readiness, but barely include a detailed examination of health provider readiness. Meanwhile, health provider readiness is a critical success factor for electronic health records in settings where the majority of the workforce is less likely to have basic computer skills. We sought to assess the readiness of health providers for electronic health records in Ghana. MATERIALS AND METHOD:An institutional-based cross-sectional study was conducted among 350 health providers in northern Ghana from June-September 2019. Data were collected using a modified questionnaire on provider readiness. The mean overall readiness was calculated for each respondent. Providers with readiness score below the overall mean score were categorized as not being ready while those at or above the mean score were considered ready. Multiple linear regression was conducted to determine the factors that determine provider readiness. RESULTS:Two hundred and nine health providers responded to the questionnaire (59.7 response rate). The mean overall readiness was 3.61 (SD = .76), mean core readiness was 3.74 (SD = .80), and mean engagement readiness was 3.47 (SD = .67). Using the average overall readiness score as the cut-off for determining being ready and not ready for electronic health records, overall readiness was 54.9%, core readiness was 67.2%, while engagement readiness was 43.1%. Age, sex, old employees compared to new employees, computer literacy, and knowledge of electronic health records were significant determinants of health providers' readiness to adopt electronic health records. CONCLUSION:We observed that health providers were marginally ready for electronic health records adoption. While participants might have expressed dissatisfaction with paper-based records and expressed a desire for electronic health records, they expressed fear of the potential impact of computerized records. We proposed a robust informatics curriculum and capacity building workshops for improving provider readiness for electronic health records.

Project description:ImportanceOther than single-center case studies, little is known about generalized pustular psoriasis (GPP) flares.ObjectiveTo assess GPP flares and their treatment, as well as differences between patients with and patients without flares documented in US electronic health records (EHRs).Design, setting, and participantsThis retrospective cohort study included adult patients with GPP (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code L40.1) identified in Optum deidentified EHR data between July 1, 2015, and June 30, 2020. The index GPP diagnosis was the first occurrence in the EHR, with no coded history of GPP for at least 6 months prior. Flare episodes were identified using an algorithm based on diagnosis coding, care setting, type of clinician, GPP disease terms, and flare terms and attributes in the EHR.Main outcomes and measuresFlare episodes were characterized by the frequency of occurrence per patient, the care setting in which they were identified, the type of specialist managing the episode, associated symptoms, and the type of treatment before, during, and after the episode. Patients were divided into groups based on whether or not they had a flare episode documented in their EHR. Comparisons were made between the groups based on demographic characteristics, comorbidity burden, health care use, and treatments.ResultsOf 1535 patients with GPP (1018 women [66.3%]; mean [SD] age, 53.4 [14.7] years), 271 had 513 flares documented. Compared with patients without flares, patients with flares had a 34% higher mean (SD) Charlson Comorbidity Index score (2.80 [3.11] vs 2.09 [2.52]), were almost 3 times more likely to have inpatient visits (119 of 271 [44%] vs 194 of 1264 [15%]), were more than twice as likely to have emergency department (ED) visits (126 of 271 [47%] vs 299 of 1264 [24%]), and had higher use of almost all treatment classes. Flares were identified in outpatient (271 of 513 [53%]), inpatient (186 of 513 [36%]), and ED (48 of 513 [9%]) settings. The most common treatments during flares were topical corticosteroids (35% of episodes [178 of 513]), opioids (21% [106 of 513]), other oral treatments, (eg, methotrexate, cyclosporine, tacrolimus; 13% [67 of 513]), and oral corticosteroids (11% [54 of 513]). Almost one-fourth of flare episodes (24% [122 of 513]) had no dermatologic treatment 30 days before, during, or 30 days after a flare episode.Conclusions and relevanceThis cohort study suggests that there is significant unmet need for the treatment of GPP and its flares, as evidenced by patients seeking treatment in inpatient and ED settings, as well as the lack of advanced treatments.

Project description:ObjectiveTo determine common patterns of recorded primary care for osteoarthritis (OA), and patient and provider characteristics associated with the quality of recorded care.DesignAn observational study nested within a cluster-randomised controlled trial.SettingEight UK general practices who were part of the Management of Osteoarthritis in Consultations study.ParticipantsPatients recorded as consulting within the eight general practices for clinical OA.Primary outcomesAchievement of seven quality indicators of care (pain/function assessment, information provision, exercise/weight advice, analgesics, physiotherapy), recorded through an electronic template or routinely recorded in the electronic healthcare records, was identified for patients aged ≥45 years consulting over a 6-month period with clinical OA. Latent class analysis was used to cluster patients based on care received. Clusters were compared on patient and clinician-level characteristics.Results1724 patients (median by practice 183) consulted with clinical OA. Common patterns of recorded quality care were: cluster 1 (38%, High) received most quality indicators of care; cluster 2 (11%, Moderate) had pain and function assessment, and received or were considered for other indicators; cluster 3 (17%, Low) had pain and function assessment, and received or were considered for paracetamol or topical non-steroidal anti-inflammatory drugs; cluster 4 (35%, None) had no recorded quality indicators. Patients with higher levels of recorded care consulted a clinician who saw more patients with OA, consulted multiple times and had less morbidity. Those in the High cluster were more likely to have recorded diagnosed OA and have knee/hip OA.ConclusionsPatterns of recorded care for OA fell into four natural clusters. Appropriate delivery of core interventions and relatively safe pharmacological options for OA are still not consistently recorded as provided in primary care. Further research to understand clinical recording behaviours and determine potential barriers to quality care alongside effective training for clinicians is needed.Trial registration numberISRCTN06984617; Results.

Project description:IntroductionThe extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring.ObjectiveThe aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics.MethodsA cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants.ResultsThe overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues.ConclusionsSystematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring.

Project description:BackgroundWithin the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.MethodsTo reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC's were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.ResultsThe ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.ConclusionThe results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.

Project description:BackgroundPostoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.ObjectiveThe objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.MethodsIn our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type.ResultsIn the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables.ConclusionsA machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

Dataset Information

How should electronic health records be designed? A cross-sectional study in patients with psoriasis.

Publications

How should electronic health records be designed? A cross-sectional study in patients with psoriasis.

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