A novel report generation approach for medical applications: the SISDS methodology and its applications

The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review

OBJECTIVES

Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).

MATERIALS AND METHODS

We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998).

RESULTS

Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce.

DISCUSSION

The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Text mining-based measurement of precision of polysomnographic reports as basis for intervention

Background

Text mining can be applied to automate knowledge extraction from unstructured data included in medical reports and generate quality indicators applicable for medical documentation. The primary objective of this study was to apply text mining methodology for the analysis of polysomnographic medical reports in order to quantify sources of variation – here the diagnostic precision vs. the inter-rater variability – in the work-up of sleep-disordered breathing. The secondary objective was to assess the impact of a text block standardization on the diagnostic precision of polysomnography reports in an independent test set.

Results

Polysomnography reports of 243 laboratory-based overnight sleep investigations scored by 9 trained sleep specialists of the Sleep Center St. Gallen were analyzed using a text-mining methodology. Patterns in the usage of discriminating terms allowed for the characterization of type and severity of disease and inter-rater homogeneity. The variation introduced by the inter-rater (technician/physician) heterogeneity was found to be twice as high compared to the variation introduced by effective diagnostic information. A simple text block standardization could significantly reduce the inter-rater variability by 44%, enhance the predictive value and ultimately improve the diagnostic accuracy of polysomnography reports.

Conclusions

Text mining was successfully used to assess and optimize the quality, as well as the precision and homogeneity of medical reporting of diagnostic procedures – here exemplified with sleep studies. Text mining methodology could lay the ground for objective and systematic qualitative assessment of medical reports.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13326-022-00259-3).

An effective density-based clustering and dynamic maintenance framework for evolving medical data streams

BACKGROUND

Medical data stream clustering has become an integral part of medical decision systems since it extracts highly-sensitive information from a tremendous flow of medical data. However, clustering and maintaining of medical data streams is still a challenging task. That is because the evolving of medical data streams imposes various challenges for clustering such as the ability to discover the arbitrary shape of a cluster, the ability to group data streams without a predefined number of clusters, and the ability to maintain the data clusters dynamically.

OBJECTIVE

To support the online medical decisions, there is a need to address the clustering challenges. Therefore, in this paper, we propose an effective density-based clustering and dynamic maintenance framework for grouping the patients with similar symptoms into meaningful clusters and monitoring the patients' status frequently.

METHODS

For clustering, we generate a set of initial medical data clusters based on the combination of Piece-wise Aggregate Approximation and the density-based spatial clustering of applications with noise called (PAA+DBSCAN) algorithm. For maintenance, when new medical data streams arrive, we maintain the initially generated medical data clusters dynamically. Since the incremental cluster maintenance is time-consuming, we further propose an Advanced Cluster Maintenance (ACM) approach to improve the performance of the dynamic cluster maintenance.

RESULTS

The experimental results on real-world medical datasets demonstrate the effectiveness and efficiency of our proposed approaches. The PAA+DBSCAN algorithm is more efficient and effective than the exact DBSCAN algorithm. Moreover, the ACM approach requires less running time in comparison with the Baseline Cluster Maintenance (BCM) approach using different tuning parameter values in all datasets. That is because the BCM approach tracks all the data points in the cluster.

CONCLUSION

The proposed framework is capable of clustering and maintaining the medical data streams effectively by means of grouping the patients who share similar symptoms and tracking the patients status that naturally tends to be changing over time.

Patients Decision Aid System Based on FHIR Profiles

Patients are becoming more and more involved in clinical decision-making process. Several factors support this process. Advances in omics allows individualization of diagnosis and treatment. Patient awareness and easy availability of data on the Internet allows patients to become informed decision makers when it comes even to disease management. Mass media emphasize the issue of medical errors, making patients demanding for quality in medical care. In some healthcare settings, patents face a problem of interpreting medical data and making decisions on treatment tactics without having a doctor, who could potentially support them. Delegating this task to a Patient Decision Aide system can add automatically generated recommendations to result reports without adding significant workload on the doctors, increase patients' motivation and support their decisions. We have implemented a patient decision aid system based on the productions rules, which: Collects data from available sources; Automatically analyses and interprets laboratory test results; Recommends running additional tests for a more precise diagnostic; Delivers automatically generated reports to doctors and patients in a natural language. To achieve semantic interoperability with other systems we have implemented a FHIR engine. The knowledge base has been organized as a graph structure. The application is structured as a set of lightly coupled services, which implement the logic of the decision support system. In total, we have modelled 365 nodes of test components, 5084 nodes of inference rules, 49932 connections and 3072 blocks of text for medical certificates. The findings of the research provide a deep understanding of how the semantically interoperable clinical decision support systems are implemented. Advances in notification the patients with the elements of patient decision aid is important for clinical data management, and for patients' empowerment and protection. We suppose that the system empowering patients in such way can play a meaningful role in helping patients to make informed decisions during the process of diagnostics and treatment.

ESR paper on structured reporting in radiology

Structured reporting is emerging as a key element of optimising radiology's contribution to patient outcomes and ensuring the value of radiologists' work. It is being developed and supported by many national and international radiology societies, based on the recognised need to use uniform language and structure to accurately describe radiology findings. Standardisation of report structures ensures that all relevant areas are addressed. Standardisation of terminology prevents ambiguity in reports and facilitates comparability of reports. The use of key data elements and quantified parameters in structured reports ("radiomics") permits automatic functions (e.g. TNM staging), potential integration with other clinical parameters (e.g. laboratory results), data sharing (e.g. registries, biobanks) and data mining for research, teaching and other purposes. This article outlines the requirements for a successful structured reporting strategy (definition of content and structure, standard terminologies, tools and protocols). A potential implementation strategy is outlined. Moving from conventional prose reports to structured reporting is endorsed as a positive development, and must be an international effort, with international design and adoption of structured reporting templates that can be translated and adapted in local environments as needed. Industry involvement is key to success, based on international data standards and guidelines.

KEY POINTS

• Standardisation of radiology report structure ensures completeness and comparability of reports. • Use of standardised language in reports minimises ambiguity. • Structured reporting facilitates automatic functions, integration with other clinical parameters and data sharing. • International and inter-society cooperation is key to developing successful structured report templates. • Integration with industry providers of radiology-reporting software is also crucial.

Personalization and Patient Involvement in Decision Support Systems: Current Trends

OBJECTIVES

This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care.

METHODS

We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support.

RESULTS

We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness.

CONCLUSIONS

Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports

OBJECTIVES

Our main objective is to design a method of, and supporting software for, interactive correction and semantic annotation of narrative clinical reports, which would allow for their easier and less erroneous processing outside their original context: first, by physicians unfamiliar with the original language (and possibly also the source specialty), and second, by tools requiring structured information, such as decision-support systems. Our additional goal is to gain insights into the process of narrative report creation, including the errors and ambiguities arising therein, and also into the process of report annotation by clinical terms. Finally, we also aim to provide a dataset of ground-truth transformations (specific for Czech as the source language), set up by expert physicians, which can be reused in the future for subsequent analytical studies and for training automated transformation procedures.

METHODS

A three-phase preprocessing method has been developed to support secondary use of narrative clinical reports in electronic health record. Narrative clinical reports are narrative texts of healthcare documentation often stored in electronic health records. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is easily readable for health professionals with the knowledge of the language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase is a semi-structured normalized clinical report where the extracted clinical terms are matched to codebook terms. Software tools for interactive correction, expansion and semantic annotation of narrative clinical reports has been developed and the three-phase preprocessing method validated in the cardiology area.

RESULTS

The three-phase preprocessing method was validated on 49 anonymous Czech narrative clinical reports in the field of cardiology. Descriptive statistics from the database of accomplished transformations has been calculated. Two cardiologists participated in the annotation phase. The first cardiologist annotated 1500 clinical terms found in 49 narrative clinical reports to codebook terms using the classification systems ICD 10, SNOMED CT, LOINC and LEKY. The second cardiologist validated annotations of the first cardiologist. The correct clinical terms and the codebook terms have been stored in a database.

CONCLUSIONS

We extracted structured information from Czech narrative clinical reports by the proposed three-phase preprocessing method and linked it to electronic health records. The software tool, although generic, is tailored for Czech as the specific language of electronic health record pool under study. This will provide a potential etalon for porting this approach to dozens of other less-spoken languages. Structured information can support medical decision making, quality assurance tasks and further medical research.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

BACKGROUND

In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available.

METHODS

The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features.

RESULTS

The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively.

CONCLUSION

Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.