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Since the early 2000s, Chinese government has sought to encourage the growth of private health insurance (PHI) while simultaneously expanding the breadth of coverage in its social health insurance (SHI) system. This paper examines how the prevalence of PHI has changed during this period and the extent to which PHI contributed to the growth of horizontal and geographical inequities with a focus on healthcare utilization. National data from China Health and Nutrition Survey between 2000 and 2015 were analysed using a multilevel modelling approach. The analysis investigated the impact of SHI membership as related to PHI uptake, PHI enrolees’ utilization of health services and out-of-pocket (OOP) expenses. This study found being covered by an SHI scheme reduced the uptake of PHI between 2004 and 2015. Having PHI caused an increase in utilizing outpatient care but did not affect OOP expenses. Coverage prevalence of PHI in a residential community was positively associated with the average level of healthcare utilization. Coverage prevalence of PHI and its effects on healthcare utilization varied geographically. The findings suggest that expanding the role of PHI was not effective without clear support from government policy. Furthermore, the expansion of PHI may cause an increase in horizontal and geographical inequities in healthcare utilization.

Modern prenatal genetic screening techniques such as cell-free fetal DNA and expanded carrier screening genotype substantial amounts of maternal and fetoplacental DNA. Although DNA can be deidentified by stripping protected health information from genetic data, anonymized DNA can be reidentified using genetic databases, raising long-term genetic privacy concerns for both mother and fetus. In this commentary, we explore the evolution of prenatal genetic screening and how modern screening techniques may pose unanticipated privacy risks. We highlight knowledge gaps and outline steps to improve patient awareness of and control over their genetic privacy, including specific recommendations for laboratories and prenatal care practitioners who offer screening. We also encourage our colleagues who provide prenatal care to be well informed about the privacy implications of the genetic tests we order and to be vocal advocates for our patients' genetic privacy, both with the laboratories that perform these tests and in the public sphere.

Opportunities to share or sell images are common in radiology. But because these images typically originate as protected health information, their use admits a host of ethical and regulatory considerations. This article discusses four scenarios that reflect data sharing or selling arrangements in radiology, especially as they might occur in “big data” systems or applications. The objective of this article is to acquaint radiologists with a variety of regulatory standards and ethical perspectives that pertain to certain data use agreements, such that the attitudes and practices of data holders and their sharers or purchasers can withstand ethical or regulatory scrutiny and not invite undesirable outcomes.

Purpose of review For over a decade, vulnerabilities in the healthcare industry have been identified. Medical devices such as cardiovascular implantable electronic devices (CIEDs) are particularly concerning because of direct threats to patient safety and protected health information (PHI). Although these vulnerabilities have been identified and changes have been made, there is significant room for improvement. We identify changes and improvements to be made in the industry, by providers, and by patients. Recent findings Cybersecurity threats in cardiac implantable devices are legitimate concerns for patient safety and PHI. Changes to cybersecurity in these devices have been made, but are far from sufficient. Summary The number of CIEDs implanted worldwide are expected to increase over the next decade. As computer technology advances, cybersecurity threats will only continue to evolve and become more complex. The healthcare industry should seriously consider improvements to protect patients and providers.

Background Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. Objectives To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. Methods We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). Results For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. Discussion and conclusion This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.

All it takes is one. One person, one email, one click, to grant cybercriminals access to your confidential files, your applications, and your patients’ protected health information. But the damage ...

There are limited data evaluating chronic nonsteroidal anti-inflammatory drug (NSAID) exposure and decline in kidney function in primary care practice where rates of NSAID prescribing are high (1,2). The majority of published observational studies have evaluated the association of prescription NSAID use with AKI using claims data in focused geographic areas outside of the United States (1). It is estimated that >36 million Americans use over-the-counter (OTC) NSAIDs and data suggest that many users, regardless of age, exceed the recommended dose (1,3,4). Education materials for prescription ( e.g ., Food and Drug Administration Medication Guide) and OTC (label) NSAIDs have also been noted to have content that is not aligned with the universal precautions for health literacy. Poor literacy around NSAID education materials has been documented among primary care patients with CKD (5). This study sought to evaluate eGFR changes associated with chronic NSAID exposure in a large, geographically vast, primary care cohort using data from the DARTNet Practice Performance Registry receiving prescription and OTC NSAIDs (6). This project was determined to not require oversight by the University of Michigan Institutional Review Board. This determination was based on the fact that the data set analyzed had no protected health information. Eligible patients were ≥18 years old and had at least two eGFR measurements (between 29 ml/min per 1.73 m2) at least 3 months apart. Baseline was defined as the first qualifying eGFR during the study period of January 1, …

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children’s Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

The introduction and rapid growth of the Internet of Medical Things (IoMT), a subset of the Internet of Things (IoT) in the medical and healthcare systems, has brought numerous changes and challenges to current medical and healthcare systems. Healthcare organizations share data about patients with research organizations for various medical discoveries. Releasing such information is a tedious task since it puts the privacy of patients at risk with the understanding that textual health documents about an individual contains specific sensitive terms that need to be sanitized before such document can be released. Recent approaches improved the utility of protected output by substituting sensitive terms with appropriate “generalizations” that are retrieved from several medical and general-purpose knowledge bases (KBs). However, these approaches perform unnecessary sanitization by anonymizing the negated assertions, e.g., AIDS-negative. This paper proposes a semantic privacy framework that effectively sanitizes the sensitive and semantically related terms in healthcare documents. The proposed model effectively identifies the negated assertions (e.g., AIDS-negative) before the sanitization process in IoMT which further improves the utility of sanitized documents. Moreover, besides considering the sensitive medical findings, we also incorporated state-of-the-art metrics, i.e., Protected Health Information (PHI), as defined in the privacy rules such as Health Insurance Portability and Accountability Act (HIPAA), Informatics for Integrating Biology & the Bedside (i2b2), and Materialize Interactive Medical Image Control System (MIMICS). The proposed approach is evaluated on real clinical data provided by i2b2. On average the detection (for both PHI’s and medical findings) accuracy is improved with Precision, Recall and F-measure score at 21%, 51%, and 54% respectively. The overall improved data utility of our proposed model is 8% as compared to C-sanitized and 25% when comparing it with a simple reduction approach. Experimental results show that our approach effectively manages the privacy and utility trade-off as compared to its counterparts.

Sleep telemedicine practitioners must ensure their practice complies with all applicable institutional, state, and federal regulations. Providers must be licensed in any state in which they provide care, have undergone credentialing and privileging procedures at outside facilities, and avoid real or perceived conflicts of interest while providing that care. Internet-based prescribing remains limited to certain circumstances. Whether or not a malpractice insurance policy covers telemedicine depends on the insurer, especially if interstate care is provided. All telemedicine programs must protect patient health information. Similarly, bioethical principles of autonomy, beneficence, nonmaleficence, and justice apply to both in-person and telemedicine-based care.

The presentation of radiology exams can be enhanced through the use of dynamic images. Dynamic images differ from static images by the use of animation and are especially useful for depicting real-time activity such as the scrolling or the flow of contrast to enhance pathology. This is generally superior to a collection of static images as a representation of clinical workflow and provides a more robust appreciation of the case in question. Dynamic images can be shared electronically to facilitate teaching, case review, presentation, and sharing of interesting cases to be viewed in detail on a computer or mobile devices for education. The creation of movies or animated images from radiology data has traditionally been challenging based on technological limitations inherent in converting the Digital Imaging and Communications in Medicine (DICOM) standard to other formats or concerns related to the presence of protected health information (PHI). The solution presented here, named Cinebot, allows a simple “one-click” generation of anonymized dynamic movies or animated images within the picture archiving and communication system (PACS) workflow. This approach works across all imaging modalities, including stacked cross-sectional and multi-frame cine formats. Usage statistics over 2 years have shown this method to be well-received and useful throughout our enterprise. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-020-00325-1) contains supplementary material, which is available to authorized users.

De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic de-identification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

Because the scope of anesthesia practice continues to expand, especially within the perioperative domain, our specialty must continually examine technological services that allow us to provide care in innovative ways. Telemedicine has facilitated the remote provision of medical services across many different specialties, but it remains somewhat unclear whether the use of telemedicine would fit within the practice of anesthesiology on a consistent basis. There have been several reports on the successful use of telemedicine within the preoperative and intraoperative realm. However, patient selection, patient and provider satisfaction, case cancellation rates, equipment reliability, and security of protected health information are just some of the issues that require further examination. This article seeks to review comprehensively the available literature related to the use of telemedicine within the preoperative, intraoperative, and postoperative phases of anesthetic care as well as analyze the major hurdles often encountered when implementing a teleconsultation service. Security of connection, data storage and encryption, federal and state medical licensure compliance, as well as overall cost/savings analysis are a few of the issues that warrant further exploration and research. As telemedicine programs develop within the perioperative arena, it is imperative for institutions to share knowledge, successes, and pitfalls to improve the delivery of care in today's technology-driven medical landscape.

Cybercrime has become an increasing concern for consumers in the United States and internationally. In recent years, cybercrimes in the healthcare industry have drastically increased in type, impact, and frequency. These attacks have negatively impacted patient privacy, the ability of providers to deliver care, and the security of healthcare organizations. Nurses are uniquely positioned to help protect against and report cybercrimes because they are one of the largest employed populations in the healthcare industry and they are on the front line of patient care and healthcare technology use. This article discusses the main concerns of cybersecurity in healthcare, the nurse’s role in preventing and managing cyber security, and recommendations for nurses, educators, and regulators.

The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non- exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging data.

This research has two parts; the first is to identify enterprise Hyperledger Fabric (HLF) blockchain cybersecurity vulnerabilities, threats, and legal obligations in a Post-Quantum Cryptography (PQC) world. HLF is a permissioned blockchain designed by IBM and uses Public Key Infrastructure (PKI), for digital signatures, and digital identities (X.509 certificates), which are critical to the operational security of its network. On 24 January 2019, Aetna, Anthem, Health Care Service Corporation, PNC Bank, and IBM announced collaboration to establish a blockchain-based ecosystem for the healthcare industry [1]. Quantum computing poses a devasting impact on PKI and estimates of its large-scale commercial arrival should not be underestimated and cannot be predicted. The HIPAA (Health Insurance Portability and Accountability Act) and General Data Protection Regulation (GDPR), requires “reasonable” measures to be taken to protect Protected Health Information (PHI), and Personally Identifiable Information (PII). However, HLF’s ecosystem is not post-quantum resistant, and all data that is transmitted over its network is vulnerable to immediate or later decryption by large scale quantum computers. The second part of this research is the independent evaluation and testing of National Institute of Standards and Technology (NIST), based Second Round Candidate PQC, lattice-based digital signature scheme, qTESLA. It’s, second-round submission is much improved, however; its algorithm characteristics and parameters are such that it is unlikely to be a quantum-resistant “as is,” simple “plug-and-play” function and replacement for HLF’s PKI. This work also proposes qTESLA’s public keys be used to create a quantum-resistant\classical hybrid PKI near-term replacement.

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.

The Gut Cell Atlas (GCA), an initiative funded by the Helmsley Charitable Trust, seeks to create a reference platform to understand the human gut, with a specific focus on Crohn’s disease. Although a primary focus of the GCA is on focusing on single-cell profiling, we seek to provide a framework to integrate other analyses on multi-modality data such as electronic health record data, radiological images, and histology tissues/images. Herein, we use the research electronic data capture (REDCap) system as the central tool for a secure web application that supports protected health information (PHI) restricted access. Our innovations focus on addressing the challenges with tracking all specimens and biopsies, validating manual data entry at scale, and sharing organizational data across the group. We present a scalable, cross-platform barcode printing/record system that integrates with REDCap. The central informatics infrastructure to support our design is a tuple table to track longitudinal data entry and sample tracking. The current data collection (by December 2020) is illustrated with types and formats of the data that the system collects. We estimate that one terabyte is needed for data storage per patient study. Our proposed data sharing informatics system addresses the challenges with integrating physical sample tracking, large files, and manual data entry with REDCap.

The privacy protection mechanism in the health context is becoming a crucial task given the exponential increase in the adoption of the Electronic Health Records (EHRs) all around the world. This kind of data can be used for medical investigation and research only if it is filtered out of all the so called Protected Health Information (PHI). This paper proposes a clinical de-identification system based on deep learning techniques for Named Entity Recognition and aimed at recognizing PHI entities to be replaced by surrogates in EHRs for anonymization purposes. This system is based on ELECTRA, a recent neural language model, and is enhanced through a sub-document level analysis aimed at grouping input sentences together, through a Sentences Grouping Factor (SGF), with the aim of broadening the representation context and consequently enhancing its ability to learn. This system was experimentally tested on the official dataset distributed in 2014 by Informatics for Integrating Biology & the Bedside research group, exhibiting superior performance compared to the state of the art in terms of detection at the category level, crucial for properly substituting PHI entities with surrogates. The effectiveness of the proposed system with respect to its components has been also confirmed by a further experimental analysis performed by substituting BERT language model in place of ELECTRA and varying SGF in accordance with limitations concerning the maximum input size for the language model used.

Electronic Health Record System, EHRS, has recently become a common healthcare information technology that has been widely adopted by many physicians. Due to compromised internal structures and systems, safeguarding the privacy and security of EHRS becomes a very challenging issue based on the most recent studies on trends and characteristics of protected health information breaches in the United States. Traditionally intrusion detection systems were proposed to address security of EHRS infrastructure by detecting unauthorized accesses; they, however, tend to generate a large number of false alerts mainly due to lack of proper features to model normal behaviors and overfitting when using signature-based detection algorithms. In this paper we address this limitation and propose a mixture model combining both misuse detection and anomaly detection approaches to minimize the number of false alerts over a specific time period in real time through its self-learning-fixing-and-improving capability built upon the perceptron algorithm.

To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysis of descriptive data breach information with the use of text mining and visualization. Insider threats, vulnerabilities, breach incidents, impacts, and responses to the breaches are analyzed for three data breach types.

Medical personnel is the front-liner in receiving the electronic Protected Health Information (ePHI). Thus, they become a crucial factor in preserving the security of ePHI in HIS. Previous studies exhibit numerous negligence issues from medical personnel resulting in data leakage. Therefore, this study is conducted to analyze the security awareness level, and which aspects and profile groups need more emphasis on understanding the security standard. The assessment uses the HIPAA Security Rule as a reference and is divided into administrative and physical aspects. The results illustrated an inadequate understanding of the medical personnel with score of 2,588 from the target

We describe the adaptation of a non-clinical pseudonymization system, originally developed for a German email corpus, for clinical use. This tool replaces previously identified Protected Health Information (PHI) items as carriers of privacy-sensitive information (original names for people, organizations, places, etc.) with semantic type-conformant, yet, fictitious surrogates. We evaluate the generated substitutes for grammatical correctness, semantic and medical plausibility and find particularly low numbers of error instances (less than 1%) on all of these dimensions.

ObjectiveAs a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research.MethodsBecause UPMC is one of the largest health care systems in the US with multiple vendors’ electronic health record (EHR) systems, we designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data is stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including EHRs, health plans, and research studies; and includes knowledge for mapping to standard terminologies. Neptune enables efficient provisioning of data to large analytics-oriented data models and to individual investigators.ResultsNeptune contains data for more than 5 million patients longitudinally organized as HIPAA Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of IRB-approved research projects by local investigators and for national projects such as the Accrual to Clinical Trials (ACT) network, the All of Us Research Program, and the National Patient-Centered Clinical Research Network.DiscussionThe design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It features several desiderata of an RDW, including robust protected health information management, an extensible information storage model, and binding to standard terminologies at the time of data delivery. It also includes several unique aspects, including the physical warehouse straddling the University of Pittsburgh and UPMC networks and management under a HIPAA Business Associates Agreement.ConclusionWe describe the design and implementation of an RDW at a large academic health care system that uses a distinctive atomic design where data is stored at a high level of granularity.

BACKGROUND: The American College of Surgeons (ACS), Committee on Trauma, trauma center verification process is designed to help hospitals improve trauma care. Due to the COVID-19 pandemic social distancing restrictions, performing virtual site visits was piloted. OBJECTIVE: The purpose of this article is to describe the first pilot ACS pediatric trauma center virtual reverification visit performed in the United States. METHODS: This is a descriptive review of a 2020 pilot virtual Level I pediatric trauma center reverification visit. In-person site visit checklists were altered to adjust to the virtual format. All documents, prereview questionnaire, patient charts, and resource documents were prepared electronically. Collaboration with the departments of information technology, clinical education and informatics, and the general counsel's office prepared the infrastructure to allow reviewers access to protected health information. RESULTS: Multiple hospital departments collaborated to facilitate the transition to an electronic format. Organized virtual meeting room scheduling, communications, and coordination between the ACS staff, the reviewers, and the various hospital departments resulted in a successful virtual visit. CONCLUSION: Lessons learned and opportunities for improvement were identified for this first-ever pilot virtual pediatric trauma center reverification site visit. Once the information technology logistic questions were answered, allowing reviewers protected health information access, the general program and document preparation for a virtual trauma reverification site visit was similar to an in-person site visit. Although the review day agenda was similar, execution challenges were identified.

Background: Privacy is recognized as a basic human right in the United States and has been identified as a core principle of ethics in clinical research. However, changes in the regulations, changes in how research is conducted, and the availability of health data stored in electronic health record systems all pose risks to individuals’ privacy. Methods: The Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule addresses the use and disclosure of individuals’ health information and sets standards for privacy rights so that individuals can understand and control how their health information is used. However, despite the significant increase in the complexity of the data privacy landscape, the HIPAA Privacy Rule has been largely unchanged since its enactment in 1996. Results: Generally, healthcare entities may not use or disclose protected health information (PHI) for research without written authorization from each subject permitting that use or disclosure. However, the HIPAA Privacy Rule allows an institutional review board (IRB) to waive the need for such authorization if documentation is provided that the use or disclosure of PHI presents “no more than a minimal risk to the privacy” of the subjects. Because IRBs were one of the only bodies allowed to waive the need for authorizations in the research context, they essentially served as the gatekeepers of privacy for human subjects. However, this situation changed with the 2018 revisions to 45 CFR §46—known as the Common Rule—that added new categories of exempt research. Under the new regulations, research administrative staff may review a submitted research study and determine that it is exempt without the IRB ever being involved and with no independent review of privacy considerations. This change lessens privacy protections for research subjects. Therefore, IRBs must be mindful of the relevant HIPAA guidance and carefully consider all facts and circumstances available when granting approvals of HIPAA authorization waiver requirements, especially in the content of exempt research, so that the IRB is confident that reasonable safeguards to protect patient privacy have been maintained. Research institutions should amend their processes to ensure that the appropriate level of privacy review is given to all studies, even those that are exempt. Conclusion: Few concrete rules are applicable in the research context that ensure compliance with the HIPAA Privacy Rule. Ultimately, more definitive regulatory guidance integrating HIPAA and the revised Common Rule should be promulgated.

This article is the second part of a continuing education series reviewing the Health Insurance Portability and Accountability Act (HIPAA) of 1996. The term HIPAA should be familiar to those who work in the medical profession, but this article includes details on its rules, patients' rights, violations, breaches, and penalties. To help administer these safeguards, HIPAA requires that every organization designate a HIPAA privacy and security officer. HIPAA violations can have serious repercussions when rules are not followed; these violations can be either negligent or willful. If breaches of unsecured protected health information occur, HIPAA requires covered entities to notify affected individuals, the Secretary of Health and Human Services, and in some cases the media. Violations in which the covered entity did not know of the violation are now punishable under the first tier of penalties. Unintended violations carry a minimum penalty of $100 per violation and a maximum of $50,000 per violation. All patients have a right to privacy and a right to confidential use of their medical records. The role of medical professionals includes understanding how and when to apply these HIPAA rules verbally and electronically.

Medical health records often contain clinical investigations results and critical information regardingpatient health conditions. In these medical records, along with patient health information, patientProtected Health Information (PHI) such as names, locations and date information can co-exist. As perHealth Insurance Portability and Accountability Act (HIPAA), before sharing the medical records withresearchers and others, all types of PHI information needs to be de-identified. Manual de-identificationthrough human annotators is laborious and error prone, hence, a reliable automated de-identificationsystem is need of the hour.In this work, various state of the art techniques for de-identification of patient notes in electronic healthrecords were analyzed for their performance, based on the performance quoted in the literature,NeuroNER was selected to de-identify Indian Radiology reports. NeuroNER is a named-entity recognition text de-identification tool developed by Massachusetts Institute of Technology (MIT). This tool is based on the Artificial Neural Networks written in Python and uses Tensorflow machine-learning framework and it comes with five pre-trained models. To test the NeuroNER models on Indian context data such as name of the person and place, 3300 medical records were simulated. Medical records were simulated by extracting clinical findings, remarks from MIMIC-III data set. For collection of all the relevant Indian data, various websites were scraped to include Indian names, Indian locations (all towns and cities), and Indian Hospital and unit names. During the testing of NeuroNER system, we observed that some of the Indian data such as name, location, etc. were not de-identified satisfactorily. To improve the performance of NeuroNER on Indian context data, along with the existing NeuroNER pre-trained model, a new pre-trained model was added to handle Indian medical reports. Medical dictionary lookup was used to reduce number of misclassifications. Results fromall four pre-trained models and the model trained on Indian simulated data were concatenated and final PHI token list was generated to anonymize the medical records to obtain de-identified records. Using this approach, we improved the applicability of the NeuroNER system to Indian data and improved its efficiency and reliability. 2000 simulated reports were used for transfer learning as training set, 1000 reports were used for test set and 300 reports were used for validation (unseen) set.

It is easy to capture and share clinical photographs and x-ray images using modern smartphones. This technology affords health-care providers the ability to rapidly collaborate and facilitate care for their patients. This improvement, however, has increased concerns regarding patient privacy and the safeguarding of protected health information. Health-care providers should understand the deidentification process for patient photographs because this process fundamentally changes the expectations and requirements for how providers are to handle this information. Properly deidentified patient photographs (and other data) are no longer considered identifiable protected health information and are not subject to the handling requirements mandated by the Health Insurance Portability and Accountability Act. This article addresses patient privacy concerns attendant to the acquisition, transmission, and sharing of clinical photographs among health-care providers. It provides guidelines for providers seeking to minimize the risk of noncompliance with privacy requirements as they adopt these new technologies into their practices. Keywords: Photograph, Smartphone, Privacy, Health Insurance Portability and Accountability Act, Deidentification

We describe a new graphical user interface-based application, US-Pro, designed to enable customized, high-throughput ultrasound video anonymization and dynamic cropping before output to video or high-efficiency disk storage. This application is distributed in a Docker container environment, which supports facile software installation on the most commonly used operating systems, as well as local processing of data sets, precluding the external transfer of electronic protected health information. The US-Pro application will facilitate the reproducible production of large-scale ultrasound video data sets for varied applications, including machine-learning analysis, educational distribution, and quality assurance review.

Background Cybersecurity risks in health care systems have traditionally been measured in data breaches of protected health information, but compromised medical devices and critical medical infrastructure present risks of disruptions to patient care. The ubiquitous prevalence of connected medical devices and systems may be associated with an increase in these risks. Objective This article details the development and execution of three novel high-fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices. Methods Clinical simulations were developed that incorporated patient-care scenarios featuring hacked medical devices based on previously researched security vulnerabilities. Results Clinicians did not recognize the etiology of simulated patient pathology as being the result of a compromised device. Conclusions Simulation can be a useful tool in educating clinicians in this new, critically important patient-safety space.

In response to a case of an undocumented patient who was reported to immigration authorities, this commentary considers whether a patient's immigration status should be deemed protected health information (PHI) under the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. A legal argument, supported by clinical data, is offered that immigration status should be regarded as PHI not subject to valid exception for release without patient authorization. This argument concludes that covered entities (eg, hospitals and health care professionals) are legally precluded under the HIPAA Privacy Rule from disclosing a patient's immigration status.

Medical data privacy is nowadays an alarming issue thanks to the technological revolution witnessed in the medical field and the ease of data access and exchange leveraged by newly implemented Hospital Information Systems (HIS). In order to help protect patient data while offering them the required medical procedures, many computerized techniques could be made available to be implemented in HIS since an early stage of their design. Those techniques should be applied throughout the rolling of clinical pathways to preserve medical data privacy and security in order to enhance privacy governance within Hospitals. When considered as processes, and because of their complexity and multidisciplinary nature, clinical pathways should be modelled in a simple way paying attention to medical tasks and the underlining shared clinical data. It is important to highlight the data with higher protection and sensitivity level. These data characteristics will influence many governance and security decisions of each process. This work aims to present a methodology to model clinical pathway specifications for data driven clinical processes, distinguishing sensitive data from other data and identifying personal data protection principles and the Protected Health Information (PHI). In this context, we precise for each clinical task potentially involving data processing and sharing, the level of protection the data requires through the use of privacy tags and labels added to data elements predefined using the HL7 standard. This method of tagging would help mapping extracted data, classified into categories, to a set of privacy requirements as needed by the HIPAA legislation. Hence data protection and privacy governance are leveraged in a seamless and highly transparent way. The use of HL7 allowed better data discovery and parsing which facilitates the definition of medical data protection measures at a later stage.

Objective De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can make the model vulnerable for deployment. In this work, we aim to close the domain gap by leveraging unlabeled data from the target domain. Materials and Methods We introduce a self-training framework to address the domain adaptation issue by leveraging unlabeled data from the target domain. We validate the effectiveness on 4 standard de-identification datasets. In each experiment, we use a pair of datasets: labeled data from the source domain and unlabeled data from the target domain. We compare the proposed self-training framework with supervised learning that directly deploys the model trained on the source domain. Results In summary, our proposed framework improves the F1-score by 5.38 (on average) when compared with direct deployment. For example, using i2b2-2014 as the training dataset and i2b2-2006 as the test, the proposed framework increases the F1-score from 76.61 to 85.41 (+8.8). The method also increases the F1-score by 10.86 for mimic-radiology and mimic-discharge. Conclusion Our work demonstrates an effective self-training framework to boost the domain adaptation performance for the de-identification task for electronic health records.

Chronic disease exists over a period of time and it is very crucial because it can affect the health of disability people more. Normally people think that they are not suffering or affected by the disease because they do not have symptoms. But it does not necessarily mean a person with no symptom has no chronic disease. This paper presents design and prototype implementation of a blockchain system which remotely monitors patient health records. The patient monitoring system is a big challenge and other security threats arises as the health care data transferred from one to another and authorization to create and read transactions, is a major concern related to data loss. The data loss of the patient protected health information which are generated from the devices should be handled carefully. So in this paper we proposed a solidity based smart contract run over a Ethereum virtual machine to facilitate the secure analysis and storing of transactions from and to the sensors. Using the Ethereum protocol, based on a private blockchain and writing the smart contracts to keep track of the patient health information. We have created an automatic system through which the sensors collects the data, then communicates with the decentralized application via the smart contract. The method inside the smart contract collects data when they are called, also reads and writes transactions of all events on the Ethereum virtual network. This solidity based smart contract system along with injected web3, metamask, and truffle environment would support alert transaction to the patient and medical professionals.

In the past decade, a growing adoption of electronic health record (EHR) systems have made massive amounts of clinical narrative data available in computable form. However, extracting relevant information from clinical narratives remains challenging. Clinical notes often contain abbreviations, medical terms, and other jargon that are easy for health professionals, but challenging for automated approaches to disambiguate. Many EHR systems use non-standard document structures to record critical information about medications, diagnoses, and potential complications. Finally, clinical narratives contain sensitive patient information, which raises privacy and security concerns. Data science and natural language processing (NLP) methods, including the recently popular deep learning-based approaches, can unlock information from narrative text and have received great attention in the medical domain. Many clinical NLP methods based on deep learning models have shown promising results in various information extraction tasks. These methods and tools have also been successfully applied to facilitate clinical research, as well as to support healthcare applications. In this tutorial, we will highlight some methods, tools, and technologies to identify medical concepts and entities in clinical text. Deriving from examples in cohort selection, medication extraction, and de-identification of protected health information, the tutorial presenters will lead a hands-on exercise to develop an NLP pipeline for clinical information extraction. The tutorial will spotlight state-of-the-art approaches with domain examples from multiple clinical domains.

As smartphones and smartphone applications are widely used in a healthcare context (e.g., remote healthcare), these devices and applications may need to comply with the Health Insurance Portability and Accountability Act (HIPAA) of 1996. In other words, adequate safeguards to protect the user’s sensitive information (e.g., personally identifiable information and/or medical history) are required to be enforced on such devices and applications. In this study, we forensically focus on the potential of recovering residual data from Android medical applications, with the objective of providing an initial risk assessment of such applications. Our findings (e.g., documentation of the artifacts) also contribute to a better understanding of the types and location of evidential artifacts that can, potentially, be recovered from these applications in a digital forensic investigation.

Digitization of services offered by healthcare sector to enable efficient and convenient patient care demands more attention as security and privacy concerns must be addressed cautiously. Healthcare apps developed to deliver such services are under constant threat of malware attacks. Nowadays, malware-infused medical apps are on the rise, inflicting severe damage to the end user. Attackers are lured by the benefits of hacking electronic protected health information (ePHI). This article takes up a proof of concept approach to validate the hypothesis that malware-infused medical apps try to bypass detection by anti-malware engines utilizing machine learning techniques internally for detection. Evading detection can be achieved by the possibility of adversarial attacks on malware detectors deploying learning-based techniques. The article utilizes a generative adversarial networks (GANs)-based attacking technique to craft adversarial samples which insert perturbations into the malicious apps. These perturbed samples when fed to the malware detector are misclassified as benign. The article takes a stand to provide defense by denoising approach and tries to utilize generative models suitable for denoising such as autoencoders (AE) and variational autoencoders (VAE). The denoised malware samples when fed to the malware classifier are expected to be classified as malware. The current results are not promising but showcase the pathway for improvements.

The medical record is the work product of the healthcare team. The information contained in the medical record is comprised of patient complaints and recollections; medications and substance use history; data such as laboratory values, radiograph reports, and physical examination findings; and impressions and judgment. In order for patients to discuss their health issues frankly and honestly with healthcare professionals, there must be a sense of confidentiality and trust. In the past, handwritten medical charts could be lost or photocopied, but such breaches were limited in scope. The electronic medical record (EMR) created a cyberdocument that was ethereal that allowed for multiuser access, and which was portable; however, as with all electronic data, the EMR was subject to inadvertent large-scale dissemination and large-scale “hacking” access. In order to leverage the opportunities of the EMR and to best utilize the EMR for electronic data interchange and analysis, the passage of security laws was necessary. Laws and regulations regarding the privacy of medical information include not only HIPAA and HITECH but also state privacy laws and disciplinary rules and regulations. The scope and impact of such privacy rules and regulations on healthcare continue to evolve.

Security and privacy are paramount pillars to the healthcare industry as protected health information can reveal very sensitive information about oneself. Prior to the Health Insurance Portability and Accountability Act of 1996 (HIPAA), controls for securing health care facilities were discretionary (HHS, 2013, para. 2). The adoption of prescriptive controls allows a reasonable baseline of security to be applied to safeguard sensitive information. The prescriptive nature of the baseline security must be flexible enough to allow organizations to adopt new technology, while also providing an adequate level of protection to the data produced by the new technology. Despite moving to the prescriptive set of control standards, 88% of all ransomware attacks occurring during the second quarter of 2016 occurred in the healthcare industry (Mulero, 2017, para. 2). While this statistic only includes three months, this is astonishingly high. To further complicate matters, a study conducted by the Ponemon Institute that included 91 healthcare organizations saw that 90% had suffered a breach from 2014-2016 (Ponemon, 2016, p. 1). The primary motivation for the healthcare suffering such attacks is the ability for a malicious actor to siphon enough information to steal entire identities (Security Scorecard, 2019, p. 2). This paper aims to discuss the role of the security officer in a major healthcare organization with regard to evaluating security posture. To provide a holistic approach to understanding the risks associated with the healthcare industry, it is important to understand the various functions performed inside the hospital and the electronic records that facilitate these departments operate effectively. Hospitals and doctor’s offices alike are also subject to many regulations to include HIPPA, Health Information Technology for Economic and Clinical Health (HITECH) Act, and Payment Card Industry standards (Murphy, 2015, p. 64). Therefore, the technical, operational, and physical controls must uniformly comply with the provisions set forth across multiple laws. While it is prudent for and organization to choose the methodology that most closely aligns with the organizations business goals, a singular methodology may not provide adequate coverage. Therefore, choosing controls across a swath of methodologies to provide adequate coverage for the organization is recommended. For the assessment of the healthcare organization, this paper will focus on leveraging the National Institute for Science and Technology (NIST) Cyber Security Framework (CSF). The Department of Health and Human Services has published a crosswalk between the HIPAA security rule and various frameworks security controls. The crosswalk demonstrates the connection between the administrative, technical, and physical safeguard standard and implementation specification in the HIPAA security rule to a relevant control mapping between Control Objectives for Information and Related Technology (COBIT), International Standards Organization 27001, International Society of Automation 62443, and NIST 800-53 (HHS, 2016, p. 2). This linkage provides a rich resource to begin creation of a risk profile management system. The strongest controls implement nine layers between detective, corrective, and preventative functions across the administrative, technical, and physical control methods (Cannon, 2016, p. 142).

Every individual has the right to protect and maintain the privacy of the Personal Identifying Information (PII) and Protected Health Information (PHI). But to avail health services, this PHI is essential to be shared with health service professionals. While doing so, the individuals must have a facility to make informed decisions about PII and PHI. The proposed work explores this area of providing better control to people in protecting their data. The paper explains the application of privacy protection policy on electronic health care systems. It compares several different Privacy By Design principles and selects the appropriate one. The features and the protection that should be used to protect such a system are proposed in the paper. The usage of a privacy dashboard is proposed to give the user complete control over their data. Other data protection methods are suggested to cover other aspects like minimization and data hiding.

Security concerns on data transfer and data transactions arise when the Internet of Things (IoT) devices and other automatic health monitoring apps rise in popularity. Therefore, this chapter aims to develop a Protected Health Information (PHI), using blockchain-based smart contracts to assist safe data analysis, data sharing, data transfer, and data management to handle the secured health information of the smart telerehabilitation app. The app called Autism Telerehabilitation App (ATA) uses a private blockchain based on the Ethereum protocol to write history and records of all the electronic health records (EHRs) of patients from the smart device. Furthermore, this ATA would provide medical interventions and real-time patient observing by sending alerts to the patient and medical specialists. Besides, it can secure and maintain the record of who has initiated these activities. This proposed blockchain with ATA offers high data security taken and undertakes many safety susceptibilities related with patient notifications and patient monitoring to all involved parties.

Despite well-documented cyber threats to patients' protected health information (PHI), sparse evidence exists about the state of cybersecurity behavior of health care workers and medical private practices. There is evidence of insecure behavior in hospital settings, even though specific insights about private practice are still absent. In addition to mandatory standards for securing PHI, such as the Health Insurance Portability & Accountability Act (HIPAA), small business viability and their patients' security and privacy are critically dependent upon technology availability and reliability. In this survey of 131 clinical audiologists we show that many lack time, staff expertise, or funds to deploy adequate cybersecurity that prevents and mitigates threats to security and privacy. We find widespread deployment of HIPAA-compliant cybersecurity, including antivirus software and individual logins. Only 9.9% of participants reported at least one data breach in 2019, significantly less than the average for small businesses and health care providers, and only 24.4% reported having cyber insurance. Practice owners view patient data as well protected and unlikely victims for cyber attacks and breaches. These results have important implications for cybersecurity products and services, and to medical professionals who must acknowledge the acute importance of cybersecurity in securing protected health information and mitigating risks. Small business private practice health care providers who are particularly sensitive to the impacts of cyber attacks and must prioritize and adopt countermeasures that decrease the risks to patients and their own businesses.

Background Provider organizations increasingly allow incorporation of patient-generated data into electronic health records (EHRs). In 2015, we began allowing patients to upload data to our EHR without physician orders, which we henceforth call patient-initiated data (PAIDA). Syncing wearable heart rate monitors to our EHR allows for uploading of thousands of heart rates per patient per week, including many abnormally low and high rates. Physician informaticists expressed concern that physicians and their patients might be unaware of abnormal heart rates, including those caused by treatable pathology. Objective This study aimed to develop a protocol to address millions of unreviewed heart rates. Methods As a quality improvement initiative, we assembled a physician informaticist team to meet monthly for review of abnormally low and high heart rates. By incorporating other data already present in the EHR, lessons learned from reviewing records over time, and from contacting physicians, we iteratively refined our protocol. Results We developed (1) a heart rate visualization dashboard to identify concerning heart rates; (2) experience regarding which combinations of heart rates and EHR data were most clinically worrisome, as opposed to representing artifact; (3) a protocol whereby only concerning heart rates would trigger a cardiologist review revealing protected health information; and (4) a generalizable framework for addressing other PAIDA. Conclusion We expect most PAIDA to eventually require systematic integration and oversight. Our governance framework can help guide future efforts, especially for cases with large amounts of data and where abnormal values may represent concerning but treatable pathology.

Static access control models do not take the dynamic sensitivity and risk levels of data and the compliance requirements together when allowing or disallowing access to part or whole of such data. For example, HIPAA requires of "minimum access" to protected Health information. In general several compliance requirements require "least-privilege" principle to be followed for protecting private data. In that process, the existing access control models fail to honor compliance requirements. In this paper, we have analyzed this problem and proposed how dynamic access control can be used to reduce the risk of sensitive data exposure due to static access control models.

Clinical narratives host vast accumulations of patient data pivotal for research and development of health related products. In order for this data to be utilized, the underlying protected health information needs to be de-identified to ensure medical confidentiality. Given the voluminous size of clinical texts, manual de-identification of such large datasets is both expensive and impractical. Therefore, the concept of automated de-identification is a highly appealing prospect. Machine learning or model based sequential labeling algorithms, such as the named entity recognition algorithms and rule-based algorithms are among the most effective approaches to automated de-identification. A natural question to ask is how we can combine them to have the best of both worlds. In this paper, we present an analytical and easy to interpret framework to dynamically combine a sequential labeling model and a soft-rule-based model in an incremental learning setup. This framework is applied to a case study, which is part of a project prototyping automated de-identification system for New Zealand clinical free text data. Evaluations show that our approach can accommodate changes in the incoming data through dynamic updating. The simplicity of the framework also allowed us to gain insights on behaviour e.g. change of importance between the machine learning and rule models.

In 2017, the Association of State and Territorial Health Officials fielded the Public Health Workforce Interests and Needs Survey (PH WINS), a nationally representative sample of state-level (SHA) and local health department (LHD) public health workers in the United States. The survey was an opportunity to measure the PHI workforce as well as assess the informatics needs of the broader PH workforce. We performed a cross-sectional study using the nationally representative 2017 PH WINS. A total of 17,136 SHA and 26,533 LHD employees participated in the survey. Respondents were asked to rate selected PH competencies with respect to the importance to their day-to-day work (i.e., not important to very important) and their current skill level (e.g., unable to perform, beginner, proficient, expert). We examined skill gaps, defined as discordance between self-reported importance (i.e., need) and skill level, for example, those reporting the competency as “somewhat important” or “very important” and “unable to perform” or “beginner.” Informaticians accounted for 1.1% of SHA respondents and 0.5% of LHD respondents working in a Big City Health Coalition agency, those that serve the top 30 most populous urban areas in the United States. While informaticians generally reported having the skills they needed for their jobs, other PH roles identified gaps. For example, 22.9% of clinical and laboratory workers felt the ability to “identify appropriate sources of data and information to assess the health of a community” was an important skill but they currently possessed low competency. This group similarly identified a gap with respect to collecting ‘valid data for use in decision making.' An informatics-savvy health department requires PHI competencies not just among PHI specialists but also among front line workers, program area managers, and executive leadership. Discordance suggests that agencies should examine ways to enhance training for PHI-related competencies for all PH workers. Key messages The informatics specialists’ role is rare in public health agencies. Significant data and informatics skills gaps persist among the broader public health workforce.

Background Financial Protection is a key dimension of universal health coverage and health system performance assessment and is also monitored within the framework of the sustainable development goals. Analyses of financial hardship among people who have out-of-pocket (OOP) spending through the use of health services in Germany usually focus solely on households with statutory health insurance due to the peculiarities of private health insurance (PHI) data. This study aims to analyse financial protection among privately insured in Germany. Methods A cross-sectional survey was conducted in 2018 with 20,000 privately insured in Germany. The survey comprised questions on household net-income, OOP spending on health, unmet needs, perceived financial burden). Catastrophic expenditure was analysed and defined as share of households with OOP spending greater than 10% of household income. Further results on financial protection and access are based on descriptive analyses. Results A total of 3,601 participants (18.0%) completed the survey (58.6 years ± 14.6; 64.6% male). 82.2% of households reported OOP spending, mostly on OTC drugs, deductibles and medical aids. Median OOP spending was €300-499 per year. Average OOP spending as share of household income was 1.3% and 0.6% of households have catastrophic OOP spending. In total, 6.6% of respondents reported unmet needs due to financial barriers, of which 5.3% had no OOP spending. 11.2% of all respondents reported to feel strongly financially burdened by OOP spending with a gap between the lowest (26.2%) and highest income group (9.9%). Conclusions PHI insured have comparatively low OOP spending with less than 1% of households having catastrophic expenditures on health. However, 6.2% of respondents reported to forgo care due to financial reasons which supports the assumption that OOP payments can create a barrier to access. Unmet needs and financial burden are higher in lower income groups which raises concerns about equity. Key messages Financial protection is good among privately insured in Germany with only few households reporting catastrophic expenditures which indicates a broad and sufficient benefit coverage under PHI. Although out-of-pocket spending is low, privately insured perceive a strong financial burden (particularly in lower income groups) and face financial barriers to access care mirrored in forgone care.