Preserving Patient Privacy Research Articles

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. The emergence of wearable medical sensors (WMSs) and deep neural networks (DNNs) points to a promising approach to address this challenge. WMSs enable continuous and user-transparent monitoring of physiological signals. However, disease detection based on WMSs/DNNs and their deployment on resource-constrained edge devices remain challenging problems. To address these problems, we propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus and the resultant disease. CovidDeep does not depend on manual feature extraction. It directly operates on WMS data and some easy-to-answer questions in a questionnaire whose answers can be obtained through a smartphone application. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic (to detect the virus), and symptomatic (to detect the disease) patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. The models were also shown to perform well on other performance measures, such as false positive rate, false negative rate, and F1 score. We augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations. This makes the CovidDeep DNNs both accurate and efficient, in terms of memory requirements and computations. The resultant DNNs are embedded in a smartphone application, which has the added benefit of preserving patient privacy.

The OpenDeID corpus for patient de-identification

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.

STHM: A Secured and Trusted Healthcare Monitoring Architecture Using SDN and Blockchain

Healthcare professionals and scholars have emphasized the need for IoT-based remote health monitoring services to track the health of the elderly. Such systems produce a large amount of data, necessitating the security and privacy of that data. On the other hand, Software Defined Networking (SDN) integration could be seen as a good solution to guarantee both flexibility and efficiency of the network which is even more important in the case of healthcare monitoring. Furthermore, Blockchain has recently been proposed as a game-changing tool that can be integrated into the Internet of Things (IoT) to have the optimal level of security and privacy. However, incorporating Blockchain into IoT networks, which rely heavily on patients’ health sensors, is extremely difficult. In this paper, a secure Healthcare Monitoring System (HMS) is proposed with a focus on trust management issues. The architecture seeks to protect multiple healthcare monitoring system components and preserves patient privacy by developing a security interface where separate security modules can be integrated to run side by side to ensure reliable HMS. The security framework architecture we propose takes advantage of the blockchain technology as a secure and timely information back-end. STHM is a proposal that uses Software-Defined Networking (SDN) as the communication medium that allows users to access SDN’s different functional and security technologies and services. Simulation results show that the use of Blockchain for the SDN-based healthcare monitoring can ensure the desired flexibility and security for a very lightweight additional overhead.

Conditional generation of medical images via disentangled adversarial inference.

Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs) use a conditioning factor to generate images and have shown great success in recent years. Intuitively, the information in an image can be divided into two parts: 1) content which is presented through the conditioning vector and 2) style which is the undiscovered information missing from the conditioning vector. Current practices in using cGANs for medical image generation, only use a single variable for image generation (i.e., content) and therefore, do not provide much flexibility nor control over the generated image. In this work we propose DRAI-a dual adversarial inference framework with augmented disentanglement constraints-to learn from the image itself, disentangled representations of style and content, and use this information to impose control over the generation process. In this framework, style is learned in a fully unsupervised manner, while content is learned through both supervised learning (using the conditioning vector) and unsupervised learning (with the inference mechanism). We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL); second, we introduce a self-supervised regularization method to further separate information in the content and style variables. For evaluation, we consider two types of baselines: single latent variable models that infer a single variable, and double latent variable models that infer two variables (style and content). We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation, image retrieval and style-content disentanglement. We show that in general, two latent variable models achieve better performance and give more control over the generated image. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.

Telemedicine and digital narrative medicine for the customization of the diagnostic-therapeutic path at the time of COVID 19

The Covid-19 issues have placed the telemedicine into the limelight, for its ability to reach remote patients affected by COVID-19, offering them support, expert advice, home hospitalization. At the same time, it gives the many fragile patients, who should be submitted to therapeutic checks or adjustments, the opportunity to be followed appropriately, avoiding travelling and associated risks of contagion. The current situation has accelerated its use in diabetological care: but with the risk of reducing the medical-patient empathic relationship. Narrative Medicine (NBM) integrates with Evidence-Based Medicine (EBM) and, taking into account the plurality of perspectives, makes clinical-care decisions more complete, personalized, effective and appropriate. The stories told by patient and by those who take care, are an essential element of contemporary medicine, based on the active participation of the subjects who are involved in the choices. People, through their own stories, become protagonists of the process of care”. The ability of mixing the technology achievements with the humanistic vision of the care process characterizes DNM, the first digital platformentirely designed for the development of narrative medicine projects in the clinical practice. It was conceived by a team of anthropologists and psychologists with the advice of doctors and experts in narrative medicine of OMNI the Observatory of Narrative Medicine Italy. The DNM features aim to maximize the potential of the digital process, at the same time preserving patient privacy and health data confidentiality. KEY WORDS telemedicine; narrative medicine; digital narrative medicine.

Forward Privacy Preservation in IoT-Enabled Healthcare Systems

In recent years, Internet of Things (IoT)-enabled health monitoring wearable devices have become a trend in healthcare systems, regularly collecting vital sign data from patients and uploading them to the cloud. Through on-demand search queries, data are shared with third-party healthcare service providers to monitor patients' health status and provide timely diagnoses. To ensure privacy and security, patient health data should be encrypted before being uploaded to the cloud. The cloud can give search encryption services. However, current searchable encryption (SE) technologies still have problems with forward privacy security and verifiability. This article proposes an IoT-cloud-enabled healthcare data system incorporating a SE method with forward privacy and verifiability. By designing a trapdoor permutation function, we render the resulting output indistinguishable from meaningless random data to the adversary. Thus, the adversary cannot judge the relationship between a newly inserted record and a past search token, and therefore, the system realizes forward privacy or forward secrecy. We propose a multikeyword search verification mechanism based on a pseudo-random function. Our approach solves verifying the correctness of search results in the top-k search scenario with partial search results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></i> formal security analysis proves that our scheme achieves forward privacy preservation, which can help guarantee healthcare data privacy. Additionally, a performance evaluation shows that our method is efficient and effective, providing an information security system to preserve patient privacy in IoT-enabled healthcare systems.

Efficient Search Over Encrypted Medical Data With Known-Plaintext/Background Models and Unlinkability

In advanced health care systems, patients’ medical data can be outsourced to cloud servers to enable remote healthcare service providers to access and analyze patients’ data from any location to provide better treatment. However, outsourcing sensitive medical data makes data owners, i.e., patients, concerned about their privacy because private companies run the cloud service and the data can be accessed by them. Therefore, it is important to encrypt the data in the form of documents before outsourcing them to the cloud in a way that enables a data user, i.e., a doctor, to search over these documents without allowing the cloud provider to learn any private information about patients. Several schemes have been proposed to enable search over encrypted medical cloud data to preserve patient privacy, but the existing schemes suffer from high communication/computation overhead because they are designed for a single-data-owner setting. Moreover, they are not secure against known-plaintext/background and linkability attacks and do not allow doctors to customize their search to avoid downloading irrelevant documents. In this paper, we develop an efficient search scheme over encrypted data for a multi-data-owner setting. To secure our scheme, the cloud server obtains noisy similarity scores and doctors de-noise them to download the most relevant documents. Our scheme enables doctors to prescribe search conditions to customize the search without revealing the conditions to the server. Our formal proof and analysis indicate that our scheme can preserve privacy and is secure against known-plaintext/background and linkability attacks, and the results of extensive experiments demonstrate the efficiency of our scheme compared to the existing works.

The Lumos Pilot Project: Feasibility of Linking General Practice Data to Other Health-Related Data

IntroductionDelivering integrated care is an aspiration of health systems around the world. General Practices (GPs) operate between other types of health care and are therefore key to effective integrated care. However, information to understand the patient journey across the health system, such as between primary and acute care, is currently impeded in Australia by lack of interoperable clinical software, privacy concerns, and cross-jurisdictional barriers. This presents a challenge to the formation of an evidence base for the design and implementation of integrated health services.&#x0D; Objectives and ApproachThe Lumos Pilot Project aims to address these challenges. Following ethics approval in February 2016, the pilot was rolled out over five stages to develop a proof of concept for extracting and linking GP data to other health-related data. The objectives were to:I. Build cross-jurisdictional collaborations between primary care and the state health system in New South Wales (NSW), Australia.II. Develop methodologies to address confidentiality requirements.III. Develop technological solutions that will allow for a large scale program.&#x0D; ResultsData were extracted from 102 GPs, covering nearly 1 million patients, and linked to emergency department, hospital and mortality records. GPs were recruited to participate through collaborations with all NSW Primary Health regions. Privacy preserving record linkage was successfully implemented in the fifth stage of the project. This process encoded patient identifiers prior to extraction from the GP and negated the need for identified information during data linkage. Software was developed and tested across all stages of the project so that once enrolled, GP data extraction and transfer were fully automated.&#x0D; Conclusion / ImplicationsThe resultant dataset has provided a new picture of patients’ healthcare journeys while preserving patient privacy. The technological solutions allow for a large scale up of GP recruitment in future.

Leveraging the Capabilities of the FDA's Sentinel System To Improve Kidney Care.

The Sentinel System is a national electronic postmarketing resource established by the US Food and Drug Administration to support assessment of the safety and effectiveness of marketed medical products. It has built a large, multi-institutional, distributed data network that contains comprehensive electronic health data, covering about 700 million person-years of longitudinal observation time nationwide. With its sophisticated infrastructure and a large selection of flexible analytic tools, the Sentinel System permits rapid and secure analyses, while preserving patient privacy and health-system autonomy. The Sentinel System also offers enhanced capabilities, including accessing full-text medical records, supporting randomized clinical trials embedded in healthcare delivery systems, and facilitating effective collection of patient-reported data using mobile devices, among many other research programs. The nephrology research community can use the infrastructure, tools, and data that this national resource offers for evidence generation. This review summarizes the Sentinel System and its ability to rapidly generate high-quality, real-world evidence; discusses the program's experience in, and potential for, addressing gaps in kidney care; and outlines avenues for conducting research, leveraging this national resource in collaboration with Sentinel investigators.

The Essential Role of Technology in the Public Health Battle Against COVID-19.

Technology has played an important role in responding to the novel coronavirus (SARS-CoV-2) and subsequent COVID-19 pandemic. The virus's blend of lethality and transmissibility have challenged officials and exposed critical limitations of the traditional public health apparatus. However, throughout this pandemic, technology has answered the call for a new form of public health that illustrates opportunities for enhanced agility, scale, and responsiveness. The authors share the Microsoft perspective and illustrate how technology has helped transform the public health landscape with new and refined capabilities - the efficacy and impact of which will be determined by history. Technologies like chatbot and virtualized patient care offer a mechanism to triage and distribute care at scale. Artificial intelligence and high-performance computing have accelerated research into understanding the virus and developing targeted therapeutics to treat infection and prevent transmission. New mobile contact tracing protocols that preserve patient privacy and civil liberties were developed in response to public concerns, creating new opportunities for privacy-sensitive technologies that aid efforts to prevent and control outbreaks. While much progress is still needed, the COVID-19 pandemic has highlighted technology's importance to public health security and pandemic preparedness. Future multi-stakeholder collaborations, including those with technology organizations, are needed to facilitate progress in overcoming the current pandemic, setting the stage for improved pandemic preparedness in the future. As lessons are assessed from the current pandemic, public officials should consider technology's role and continue to seek opportunities to supplement and improve on traditional approaches.

Blockchain-authenticated sharing of cancer patient genomic and clinical outcomes data.

e19358 Background: Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of cancer patients to share their treatment experiences to fuel research, despite potential risks to privacy. The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for dissemination of de-identified clinical and genomic data with a focus on late stage cancer. Methods: We created and piloted a blockchain-authenticated system to enable securely sharing de-identified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHR), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (n = 18), which we uploaded to CGT. EHR data were extracted from both a hospital cancer registry and a common data model format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold-standard source documentation for patients with available data (n = 17). Results: While the total completeness scores were greater for the registry reports than the common data model, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting common data model. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world cancer patient data in a more clinically useful timeframe. We also developed an open-source web application to allow users to seamlessly search, browse, explore, and download CGT data. Conclusions: Our pilot demonstrates the willingness of cancer patients to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

Blockchain-Authenticated Sharing of Genomic and Clinical Outcomes Data of Patients With Cancer: A Prospective Cohort Study.

BackgroundEfficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy.ObjectiveThe objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer.MethodsWe created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17).ResultsAlthough the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data.ConclusionsOur pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.

Toward Design of a Drip-Stand Patient Follower Robot

A person following robot is an application of service robotics that primarily focuses on human-robot interaction, for example, in security and health care. This paper explores some of the design and development challenges of a patient follower robot. Our motivation stemmed from common mobility challenges associated with patients holding on and pulling the medical drip stand. Unlike other designs for person following robots, the proposed design objectives need to preserve as much as patient privacy and operational challenges in the hospital environment. We placed a single camera closer to the ground, which can result in a narrower field of view to preserve patient privacy. Through a unique design of artificial markers placed on various hospital clothing, we have shown how the visual tracking algorithm can determine the spatial location of the patient with respect to the robot. The robot control algorithm is implemented in three parts: (a) patient detection; (b) distance estimation; and (c) trajectory controller. For patient detection, the proposed algorithm utilizes two complementary tools for target detection, namely, template matching and colour histogram comparison. We applied a pinhole camera model for the estimation of distance from the robot to the patient. We proposed a novel movement trajectory planner to maintain the dynamic tipping stability of the robot by adjusting the peak acceleration. The paper further demonstrates the practicality of the proposed design through several experimental case studies.

Pharmacist-Patient Relationship: Commitment to Care

Pharmacist-Patient Relationship: Commitment to Care

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Using pairs of deep neural networks, we generated simulated, synthetic participants that closely resemble participants of the SPRINT trial (Systolic Blood Pressure Trial). We showed that such paired networks can be trained with differential privacy, a formal privacy framework that limits the likelihood that queries of the synthetic participants' data could identify a real a participant in the trial. Machine learning predictors built on the synthetic population generalize to the original data set. This finding suggests that the synthetic data can be shared with others, enabling them to perform hypothesis-generating analyses as though they had the original trial data. Deep neural networks that generate synthetic participants facilitate secondary analyses and reproducible investigation of clinical data sets by enhancing data sharing while preserving participant privacy.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BackgroundHealth care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.ObjectiveThe aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.MethodsWe describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.ResultsThe majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.ConclusionsSyndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Privacy Preserving Feature Selection for Vertically Distributed Medical Data based on Genetic Algorithms and Naïve Bayes

Machine learning is a powerful tool to mine useful knowledge from vast databases. Many establishments in the medical area such as hospitals, laboratories want to join their efforts with the ambition to extract models that are more accurate. However, this approach faces problems. Due to the laws protecting patient privacy or other similar concerns, parties are reluctant to share their data. In vast amounts of data, which are useful and pertinent in constructing accurate data mining models? In this article, the researchers deal with these challenges for vertically distributed medical data. They propose an original secure wrapper solution to perform feature selection based on genetic algorithms and distributed Naïve Bayes. Contrary to the previous solutions, the original data is not perturbed. Therefore, the data utility and performance are preserved. They prove that the proposed solution selects relevant attributes to increase performance, preserving patient privacy.

Abstract 142: Engaging Patients Throughout the Health System: A Landscape Analysis of Top Research Institutions and Cold Call Policies

Background: Healthcare institutions may often prohibit “cold-calling,” or direct contact with a potential research participant when the person initiating contact is unknown to the patient. This policy aims to maintain patient privacy, but may lead to unintended consequences such as physician gatekeeping or increase concerns for coercion if treating physicians are the sole contact allowed. Patients may also be denied full knowledge and benefit of research opportunities, and clinical studies may suffer from selection bias and under enrollment. Differences in cold call policies across institutions have yet to be explored. Objectives: To review the current landscape of cold call policies among top academic institutions in order to inform guidance about future policy change. Methods: Sixteen institutions with the highest NIH grant funding were classified as providing guidance about cold-calling, prohibiting cold-calling, and/or allowing exceptions to the cold call policy, based on publically available data. Results: Among the top 16 research intensive institutions, 5 institutions have no cold-call policy. There are 7 institutions that prohibit cold-calling with exceptions, including when provider contact is impractical. There are 2 that do not have a cold call policy, but provide guidance such as taking extra care to respect patient privacy and inform patients of how they were identified. Finally, 2 prohibit cold-calling without exceptions. For patients not receiving care within the hospital’s health system, some institutions require that the PCP make first contact (Table). Conclusion: The approach to engaging patients in large, research intensive health systems varies significantly. As healthcare has evolved with more virtual visits, team-based care or system-based care, the engagement of potential research participants should similarly evolve while preserving patient privacy and autonomy. Potential solutions include opt-in or opt-out approaches for future contact, replacement of cold call policies with best practices, or a team based collaborative approach between physicians and researchers to guide recruitment decisions.

MicroShare: Privacy-Preserved Medical Resource Sharing through MicroService Architecture.

This paper takes up the problem of medical resource sharing through MicroService architecture without compromising patient privacy. To achieve this goal, we suggest refactoring the legacy EHR systems into autonomous MicroServices communicating by the unified techniques such as RESTFul web service. This lets us handle clinical data queries directly and far more efficiently for both internal and external queries. The novelty of the proposed approach lies in avoiding the data de-identification process often used as a means of preserving patient privacy. The implemented toolkit combines software engineering technologies such as Java EE, RESTful web services, JSON Web Tokens to allow exchanging medical data in an unidentifiable XML and JSON format as well as restricting users to the need-to-know principle. Our technique also inhibits retrospective processing of data such as attacks by an adversary on a medical dataset using advanced computational methods to reveal Protected Health Information (PHI). The approach is validated on an endoscopic reporting application based on openEHR and MST standards. From the usability perspective, the approach can be used to query datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care and medical record services to improve quality of care and treatment.

Secondary Use of Patient Data: Review of the Literature Published in 2016

Summary Objectives: To summarize recent research and emerging trends in the area of secondary use of healthcare data, and to present the best papers published in this field, selected to appear in the 2017 edition of the IMIA Yearbook. Methods: A literature review of articles published in 2016 and related to secondary use of healthcare data was performed using two bibliographic databases. From this search, 941 papers were identified. The section editors independently reviewed the papers for relevancy and impact, resulting in a consensus list of 14 candidate best papers. External reviewers examined each of the candidate best papers and the final selection was made by the editorial board of the Yearbook. Results: From the 941 retrieved papers, the selection process resulted in four best papers. These papers discuss data quality concerns, issues in preserving privacy of patients in shared datasets, and methods of decision support when consuming large amounts of raw electronic health record (EHR) data. Conclusion: In 2016, a significant effort was put into the development of new systems which aim to avoid significant human understanding and pre-processing of healthcare data, though this is still only an emerging area of research. The value of temporal relationships between data received significant study, as did effective information sharing while preserving patient privacy.