Data-driven approach for creating synthetic electronic medical records

<p>Abstract</p> <p>Background</p> <p>New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.</p> <p>Methods</p> <p>This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.</p> <p>Results</p> <p>We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.</p> <p>Conclusions</p> <p>A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.</p

Randomised controlled trials of complex interventions and large-scale transformation of services

Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development. Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential

Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is the key factor for the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can help improve the accuracy, robustness and privacy of downstream AI algorithms. However, access to good quality datasets is limited by the technical difficulty of data acquisition and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with a similar distribution as real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Thus, in this paper, we will review the synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-styled review paper will provide comprehensive descriptions of non-imaging medical data synthesis on aspects including algorithms, evaluations, limitations and future research directions.Comment: 35 pages, Submitted to ACM Computing Survey

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Avoiding disclosure of individually identifiable health information: a literature review

Achieving data and information dissemination without arming anyone is a central task of any entity in charge of collecting data. In this article, the authors examine the literature on data and statistical confidentiality. Rather than comparing the theoretical properties of specific methods, they emphasize the main themes that emerge from the ongoing discussion among scientists regarding how best to achieve the appropriate balance between data protection, data utility, and data dissemination. They cover the literature on de-identification and reidentification methods with emphasis on health care data. The authors also discuss the benefits and limitations for the most common access methods. Although there is abundant theoretical and empirical research, their review reveals lack of consensus on fundamental questions for empirical practice: How to assess disclosure risk, how to choose among disclosure methods, how to assess reidentification risk, and how to measure utility loss.public use files, disclosure avoidance, reidentification, de-identification, data utility

Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

Justice in Review: New Trends in State Sentencing and Corrections 2014-2015

In 2014 and 2015, 46 states enacted at least 201 bills, executive orders, and ballot initiatives to reform at least one aspect of their sentencing and corrections systems. In conducting this review of state criminal justice reforms, Vera found that most of the policy changes focused on three areas: creating or expanding opportunities to divert people away from the criminal justice system; reducing prison populations by enacting sentencing reform, expanding opportunities for early release from prison, and reducing the number of people admitted to prison for violating the terms of their community supervision; and supporting reentry into the community from prison. By providing concise summaries of representative reforms in each of these areas, this report serves as a practical guide for other state and federal policymakers looking to affect similar changes in criminal justice policy

The ATEN Framework for Creating the Realistic Synthetic Electronic Health Record

Realistic synthetic data are increasingly being recognized as solutions to lack of data or privacy concerns in healthcare and other domains, yet little effort has been expended in establishing a generic framework for characterizing, achieving and validating realism in Synthetic Data Generation (SDG). The objectives of this paper are to: (1) present a characterization of the concept of realism as it applies to synthetic data; and (2) present and demonstrate application of the generic ATEN Framework for achieving and validating realism for SDG. The characterization of realism is developed through insights obtained from analysis of the literature on SDG. The development of the generic methods for achieving and validating realism for synthetic data was achieved by using knowledge discovery in databases (KDD), data mining enhanced with concept analysis and identification of characteristic, and classification rules. Application of this framework is demonstrated by using the synthetic Electronic Healthcare Record (EHR) for the domain of midwifery. The knowledge discovery process improves and expedites the generation process; having a more complex and complete understanding of the knowledge required to create the synthetic data significantly reduce the number of generation iterations. The validation process shows similar efficiencies through using the knowledge discovered as the elements for assessing the generated synthetic data. Successful validation supports claims of success and resolves whether the synthetic data is a sufficient replacement for real data. The ATEN Framework supports the researcher in identifying the knowledge elements that need to be synthesized, as well as supporting claims of sufficient realism through the use of that knowledge in a structured approach to validation. When used for SDG, the ATEN Framework enables a complete analysis of source data for knowledge necessary for correct generation. The ATEN Framework ensures the researcher that the synthetic data being created is realistic enough for the replacement of real data for a given use-case