Comparison of attribute-based encryption schemes in securing healthcare systems.

E-health has become a top priority for healthcare organizations focused on advancing healthcare services. Thus, medical organizations have been widely adopting cloud services, resulting in the effective storage of sensitive data. To prevent privacy and security issues associated with the data, attribute-based encryption (ABE) has been a popular choice for encrypting private data. Likewise, the attribute-based access control (ABAC) technique has been widely adopted for controlling data access. Researchers have proposed electronic health record (EHR) systems using ABE techniques like ciphertext policy attribute-based encryption (CP-ABE), key policy attribute-based encryption (KP-ABE), and multi authority attribute-based encryption (MA-ABE). However, there is a lack of rigorous comparison among the various ABE schemes used in healthcare systems. To better understand the usability of ABE techniques in medical systems, we performed a comprehensive review and evaluation of the three popular ABE techniques by developing EHR systems using knowledge graphs with the same data but different encryption mechanisms. We have used the MIMIC-III dataset with varying record sizes for this study. This paper can help healthcare organizations or researchers using ABE in their systems to comprehend the correct usage scenario and the prospect of ABE deployment in the most recent technological evolution.

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.

A Policy-Driven Approach to Secure Extraction of COVID-19 Data From Research Papers.

The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to COVID research from which the research community can extract knowledge and information. We show an example system with a machine learning-based knowledge extractor which draws out key medical information from COVID-19 related academic research papers. We represent this knowledge in a Knowledge Graph that uses the Unified Medical Language System (UMLS). However, publicly available studies rely on dataset that might have sensitive data. Extracting information from academic papers can potentially leak sensitive data, and protecting the security and privacy of this data is equally important. In this paper, we address the key challenges around the privacy and security of such information extraction and analysis systems. Policy regulations like HIPAA have updated the guidelines to access data, specifically, data related to COVID-19, securely. In the US, healthcare providers must also comply with the Office of Civil Rights (OCR) rules to protect data integrity in matters like plasma donation, media access to health care data, telehealth communications, etc. Privacy policies are typically short and unstructured HTML or PDF documents. We have created a framework to extract relevant knowledge from the health centers' policy documents and also represent these as a knowledge graph. Our framework helps to understand the extent to which individual provider policies comply with regulations and define access control policies that enforce the regulation rules on data in the knowledge graph extracted from COVID-related papers. Along with being compliant, privacy policies must also be transparent and easily understood by the clients. We analyze the relative readability of healthcare privacy policies and discuss the impact. In this paper, we develop a framework for access control decisions that uses policy compliance information to securely retrieve COVID data. We show how policy compliance information can be used to restrict access to COVID-19 data and information extracted from research papers.

Clinical effectiveness of indocyanine green mediated antimicrobial photodynamic therapy as an adjunct to scaling root planing in treatment of chronic periodontitis- A randomized controlled clinical trial.

BACKGROUND: Antimicrobial photodynamic therapy (aPDT) along with scaling and root planing has been studied for treatment of periodontitis. Various photosensitizers have been utilized for the same. The present study was conducted to assess the clinical efficacy of indocyanine green (ICG), a photosensitizer with better tissue absorption and low toxicity, as an aPDT adjuvant to scaling and root planing (SRP). METHOD: In the present split-mouth clinical study 29 patients suffering from chronic generalized periodontitis were enrolled. The test and control quadrant were randomly allocated, control quadrant was treated with scaling and root planing alone, while the test group received an adjunctive therapy in the form of ICG, mediated aPDT along with SRP. Clinical parameters such a plaque index (PI), modified sulcular bleeding index (mSBI), periodontal probing depth (PPD) and clinical attachment level(CAL)were recorded at baseline and 3 months. RESULT: It was observed that there was a significant reduction in PI and mSBI from baseline to 3 months in both the groups, although, the intergroup comparison for both parameters yielded a non-significant result. Whereas a significant improvement in PPD and CAL in ICG group were recorded during the intergroup comparison between test and control group from baseline to 3 months CONCLUSION: Within the limits of the present study it can be concluded that ICG mediated PDT when used as an adjunct improved the effectiveness of SRP.

Clinical assessment of interdental papilla competency parameters in the esthetic zone.

BACKGROUND: Interdental papilla occupies the space between two adjacent teeth. Its contour and health are regarded with utmost importance in esthetic dental therapy. The loss of papillary height can result in open gingival embrasures, phonetic problems, food impaction, and esthetic concerns: therefore in order to prevent its loss and to better understand the challenges of regenerating the lost gingival papillae, it is crucial to recognize the factors that influence papilla form. AIM: The purpose of this study is to evaluate various factors associated with the fill of interdental spaces by gingival papillae MATERIAL AND METHODS: 150 interdental papillae in 30 patients were assessed with clinical and study models. Papilla score (PS), tooth form/shape, gingival thickness, crest bone height, facio lingual thickness and gingival angle were recorded. RESULTS: In the study it was observed that complete papilla fill was associated with bone crest-contact point distance ≤ 5 mm and crown width: length ≥0.88 while interproximal gingival tissue thickness and gingival angle were significantly higher in competent as compared to incompetent papilla group. CONCLUSION: Interdental papillae appearance was associated significantly with tooth form/shape (CW/CL), crestal bone height, gingival thickness and gingival angle. CLINICAL SIGNIFICANCE: Presence of appropriately positioned interdental papillae in the anterior dentition is essential. Its deficiency leads to complex esthetic and functional problems while its reconstruction poses a formidable challenge. Thus a prior understanding of the various factors that affect its integrity in the anterior region allows for better management of the papillae leading to a more predictable outcome.