Evaluation of Patient-Friendly Diagnosis Clarifications in a Hospital Patient Portal.

BACKGROUND: Medical data can be difficult to comprehend for patients, but only a limited number of patient-friendly terms and definitions are available to clarify medical concepts. Therefore, we developed an algorithm that generalizes diagnoses to more general concepts that do have patient-friendly terms and definitions in SNOMED CT. We implemented the generalizations, and diagnosis clarifications with synonyms and definitions that were already available, in the problem list of a hospital patient portal. OBJECTIVE: We aimed to assess the extent to which the clarifications cover the diagnoses in the problem list, the extent to which clarifications are used and appreciated by patient portal users, and to explore differences in viewing problems and clarifications between subgroups of users and diagnoses. METHODS: We measured the coverage of diagnoses by clarifications, usage of the problem list and the clarifications, and user, patient and diagnosis characteristics with aggregated, routinely available electronic health record and log file data. Additionally, patient portal users provided quantitative and qualitative feedback about the clarification quality. RESULTS: Of all patient portal users who viewed diagnoses on their problem list (n = 2,660), 89% had one or more diagnoses with clarifications. In addition, 55% of patient portal users viewed the clarifications. Users who rated the clarifications (n = 108) considered the clarifications to be of good quality on average, with a median rating per patient of 6 (interquartile range: 4-7; from 1 very bad to 7 very good). Users commented that they found clarifications to be clear and recognized the clarifications from their own experience, but sometimes also found the clarifications incomplete or disagreed with the diagnosis itself. CONCLUSION: This study shows that the clarifications are used and appreciated by patient portal users. Further research and development will be dedicated to the maintenance and further quality improvement of the clarifications.

Diagnosis clarification by generalization to patient-friendly terms and definitions: Validation study.

BACKGROUND: Now that patients increasingly get access to their healthcare records, its contents require clarification. The use of patient-friendly terms and definitions can help patients and their significant others understand their medical data. However, it is costly to make patient-friendly descriptions for the myriad of terms used in the medical domain. Furthermore, a description in more general terms, leaving out some of the details, might already be sufficient for a layperson. We developed an algorithm that employs the SNOMED CT hierarchy to generalize diagnoses to a limited set of concepts with patient-friendly terms for this purpose. However, generalization essentially implies loss of detail and might result in errors, hence these generalizations remain to be validated by clinicians. We aim to assess the medical validity of diagnosis clarification by generalization to concepts with patient-friendly terms and definitions in SNOMED CT. Furthermore, we aim to identify the characteristics that render clarifications invalid. RESULTS: Two raters identified errors in 12.7% (95% confidence interval - CI: 10.7-14.6%) of a random sample of 1,131 clarifications and they considered 14.3% (CI: 12.3-16.4%) of clarifications to be unacceptable to show to a patient. The intraclass correlation coefficient of the interrater reliability was 0.34 for correctness and 0.43 for acceptability. Errors were mostly related to the patient-friendly terms and definitions used in the clarifications themselves, but also to terminology mappings, terminology modelling, and the clarification algorithm. Clarifications considered to be most unacceptable were those that provide wrong information and might cause unnecessary worry. CONCLUSIONS: We have identified problems in generalizing diagnoses to concepts with patient-friendly terms. Diagnosis generalization can be used to create a large amount of correct and acceptable clarifications, reusing patient-friendly terms and definitions across many medical concepts. However, the correctness and acceptability have a strong dependency on terminology mappings and modelling quality, as well as the quality of the terms and definitions themselves. Therefore, validation and quality improvement are required to prevent incorrect and unacceptable clarifications, before using the generalizations in practice.

Evaluation of lexical clarification by patients reading their clinical notes: a quasi-experimental interview study.

BACKGROUND: Patients benefit from access to their medical records. However, clinical notes and letters are often difficult to comprehend for most lay people. Therefore, functionality was implemented in the patient portal of a Dutch university medical centre (UMC) to clarify medical terms in free-text data. The clarifications consisted of synonyms and definitions from a Dutch medical terminology system. We aimed to evaluate to what extent these lexical clarifications match the information needs of the patients. Secondarily, we evaluated how the clarifications and the functionality could be improved. METHODS: We invited participants from the patient panel of the UMC to read their own clinical notes. They marked terms they found difficult and rated the ease of these terms. After the functionality was activated, participants rated the clarifications provided by the functionality, and the functionality itself regarding ease and usefulness. Ratings were on a scale from 0 (very difficult) to 100 (very easy). We calculated the median number of terms not understood per participant, the number of terms with a clarification, the overlap between these numbers (coverage), and the precision and recall. RESULTS: We included 15 participants from the patient panel. They marked a median of 21 (IQR 19.5-31) terms as difficult in their text files, while only a median of 2 (IQR 1-4) of these terms were clarified by the functionality. The median precision was 6.5% (IQR 2.3-14.25%) and the median recall 8.3% (IQR 4.7-13.5%) per participant. However, participants rated the functionality with median ease of 98 (IQR 93.5-99) and a median usefulness of 79 (IQR 52.5-97). Participants found that many easy terms were unnecessarily clarified, that some clarifications were difficult, and that some clarifications contained mistakes. CONCLUSIONS: Patients found the functionality easy to use and useful. However, in its current form it only helped patients to understand few terms they did not understand, patients found some clarifications to be difficult, and some to be incorrect. This shows that lexical clarification is feasible even when limited terms are available, but needs further development to fully use its potential.

Towards an Adoption Framework for Patient Access to Electronic Health Records: Systematic Literature Mapping Study.

BACKGROUND: Patient access to electronic health records (EHRs) is associated with increased patient engagement and health care quality outcomes. However, the adoption of patient portals and personal health records (PHRs) that facilitate this access is impeded by barriers. The Clinical Adoption Framework (CAF) has been developed to analyze EHR adoption, but this framework does not consider the patient as an end-user. OBJECTIVE: We aim to extend the scope of the CAF to patient access to EHRs, develop guidance documentation for the application of the CAF, and assess the interrater reliability. METHODS: We systematically reviewed existing systematic reviews on patients' access to EHRs and PHRs. Results of each review were mapped to one of the 43 CAF categories. Categories were iteratively adapted when needed. We measured the interrater reliability with Cohen's unweighted kappa and statistics regarding the agreement among reviewers on mapping quotes of the reviews to different CAF categories. RESULTS: We further defined the framework's inclusion and exclusion criteria for 33 of the 43 CAF categories and achieved a moderate agreement among the raters, which varied between categories. CONCLUSIONS: In the reviews, categories about people, organization, system quality, system use, and the net benefits of system use were addressed more often than those about international and regional information and communication technology infrastructures, standards, politics, incentive programs, and social trends. Categories that were addressed less might have been underdefined in this study. The guidance documentation we developed can be applied to systematic literature reviews and implementation studies, patient and informal caregiver access to EHRs, and the adoption of PHRs.

Determinants and outcomes of patient access to medical records: Systematic review of systematic reviews.

BACKGROUND: Patient access to electronic health records (EHRs) is associated with several determinants and outcomes, which are interrelated. However, individual studies and the reviews summarizing them have only addressed particular aspects, such as policy, usability or health outcomes of adoption. Therefore, no comprehensive overview exists. Additionally, reviews used different theoretical frameworks, which makes results difficult to compare. OBJECTIVE: We aimed to systematically review recent systematic reviews on determinants and outcomes of patient access to EHRs to create a comprehensive overview and inform policy-makers and EHR implementers about the available literature, and to identify knowledge gaps in the literature reviews. METHODS: We searched MEDLINE, EMBASE and PsycINFO for systematic reviews on patient portals, personal health records, and patient access to records that addressed determinants and outcomes of adoption. We synthesized the results from these reviews into the Clinical Adoption Framework (CAF), by mapping quotes from the reviews to categories and dimensions of the CAF, starting with the most recent ones until saturation of the CAF had been reached. The risk of bias in the reviews was assessed using the AMSTAR2 checklist. RESULTS: We included nineteen reviews from 8871 records that were retrieved until February 19th, 2018. The reviews had a median of 4 (IQR: 4-4) critical flaws according to the AMSTAR2 checklist. The reviews contained a total of 1054 quotes that were mapped to the CAF. All reviews reported on the dimension 'People' that can affect adoption (e.g. personal characteristics such as age) and the dimension 'HIS use' (health information system use). Most reviews reported the dimensions 'Organisation', 'Implementation', HIS 'System quality', and outcomes of HIS use. However, gaps in knowledge might exist on macro-level determinants and outcomes, such as healthcare standards, funding, and incentives, because few reviews addressed these aspects. CONCLUSIONS: No review covered all aspects of the CAF and there was a large variety in aspects that were addressed, but all dimensions of the CAF were addressed by at least two reviews. Although reviews had critical flaws according to the AMSTAR2 checklist, almost half of the reviews did use methods to assess bias in primary studies. Implementers can use the synthesized results from this study as a reference for implementation and development when taking quality restrictions into account. Researchers should address the risk of bias in primary studies in future reviews and use a framework such as CAF to make results more comparable and reusable.