Health Insurance Portability Research Articles

Abstract PO2-28-09: Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model

Abstract Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation in Black women of a mammography-driven AI breast cancer risk model (Mirai) originally developed on screening cohorts primarily consisting of White women. In this institutional review board-approved, Health Insurance Portability and Protection Act (HIPAA)-compliant study under a waiver of consent, we retrospectively analyzed a case–cohort sample nested within the core academic breast cancer screening practice of BJC Healthcare, the hospital partner of Washington University in St. Louis. For the purposes of this validation study, relying on 2D digital mammography (DM) images, we focused on Black women presenting for annual DM screening (Selenia or Selenia Dimensions; Hologic) between 2008 and 2018. Eligible breast cancer cases were derived from all women with a breast cancer diagnosis (with associated biopsy-confirmed tumor pathology via institutional cancer registry) after negative (BI-RADS 1 or 2) DM screening 1 to 5 years prior to cancer diagnosis. We also identified a random sample of controls, defined as women who had negative (BI-RADS 1 or 2) DM screening, with 1 to 5 years of screening follow-up without a cancer diagnosis. Risk scores for all DM exams were calculated via the Mirai model. Performance was evaluated using concordance-index (C) analyses and associated 95% confidence intervals (CIs) for the entire cohort, as well as for study subgroups of invasive versus in-situ cancer and cancer molecular subtypes. We analyzed 1368 DM screening exams, including 672 DM exams from 391 women diagnosed with breast cancer (mean age, 58 years; standard deviation, 10 years) and 696 DM exams from 406 controls (mean age, 55 years; standard deviation, 10 years). The overall C-index was 0.62 [95% CIs 0.60–0.64] for all Black women, which was lower compared to previously reported validation results for Mirai in studies of similar design on predominantly White and racially diverse screening cohorts (C-index = 0.67-0.78). There was no evidence of a significant difference between invasive and in-situ cancer (C-index = 0.64 [95% CIs 0.61–0.66] vs. 0.64 [95% CIs 0.61–0.67]). Compared to other cancer molecular subtypes, performance was significantly higher among triple-negative (C-index = 0.67 [95% CIs 0.62–0.71]) and estrogen receptor (ER) negative cancer (C-index = 0.66 [95% CIs 0.61–0.70]). A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, particularly for triple-negative and ER- cancer types. However, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts. Our results suggest that further refinements are needed towards more accurate breast cancer risk assessment in Black women. Long-term breast cancer risk prediction performance (C-indices) of Mirai in the full cohort of Black women and in study subgroups. Citation Format: Juanita Hernandez Lopez, Zhixin Sun, Shirin Shoushtari, Ulugbek Kamilov, Debbie Bennett, Aimilia Gastounioti. Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-09.

Navigating privacy and security in telemedicine for primary care.

To examine patient and provider perspectives on privacy and security considerations in telemedicine during the COVID-19 pandemic. Qualitative study with patients and providers from primary care practices in 3 National Patient-Centered Clinical Research Network sites in New York, New York; North Carolina; and Florida. Semistructured interviews were conducted, audio recorded, transcribed verbatim, and coded using an inductive process. Data related to privacy and information security were analyzed. Sixty-five patients and 21 providers participated. Patients and providers faced technology-related security concerns as well as difficulties ensuring privacy in the transformed shared space of telemedicine. Patients expressed increased comfort doing telemedicine from home but often did not like their providers to offer virtual visits from outside an office setting. Providers initially struggled to find secure and Health Insurance Portability and Accountability Act-compliant platforms and devices to host the software. Whereas some patients preferred familiar platforms such as FaceTime, others recognized potential security concerns. Audio-only encounters sometimes raised patient concerns that they would not be able to confirm the identity of the provider. Telemedicine led to novel concerns about privacy because patients and providers were often at home or in public spaces, and they shared concerns about software and hardware security. In addition to technological safeguards, our study emphasizes the critical role of physical infrastructure in ensuring privacy and security. As telemedicine continues to evolve, it is important to address and mitigate concerns around privacy and security to ensure high-quality and safe delivery of care to patients in remote settings.

No Patient Left Behind: A Novel Paradigm to Fulfill Hepatitis C Virus Treatment for Rural Patients.

This study evaluates a novel multidisciplinary program providing expanded access to hepatitis C virus (HCV) treatment for rural Appalachian patients in South Carolina. This program identified patients via an opt-out emergency department screening program, and it aimed to achieve HCV cure by using community paramedics (CPs) to link and monitor patients from treatment initiation through 12-week sustained virologic response (SVR). Patients aged ≥18 years who were HCV RNA positive were eligible for enrollment if they failed to appear for a scheduled HCV appointment or reported barriers to accessing office-based treatment. CPs provided home visits (initial and 4, 12, and 24 weeks) using a mobile Wi-Fi hotspot to support telemedicine appointments (compliant with the Health Insurance Portability and Accountability Act) and perform focused physical assessments, venipuncture, and coordinated home delivery of medications. Statistics described participant characteristics, prevalence of SVR, and patient satisfaction results at 12 weeks posttreatment. Thirty-four patients were eligible for SVR laboratory tests by 31 August 2023; the majority were male (61.7%) and White (64.7%) with an average age of 56 years (SD, 11.7). Twenty-eight (82.4%) completed treatment and achieved 12-week SVR. Six (17.6%) were lost to follow-up. Two-thirds strongly agreed that they were satisfied with the overall care that they received, and half strongly agreed that their overall health had improved. This CP-augmented treatment program demonstrated success curing HCV for rural patients who lacked access to office-based treatment. Other health care systems may consider this novel delivery model to treat hard-to-reach individuals who are HCV positive.

Is Healthcare Facing A Silent Epidemic: The Rise Of Cyberattacks?

Undesirable consequences, such as the potential harm due to enhanced reliance on electronic platforms, should not be underestimated regardless of the healthcare industry's spectacular growth and the repeatedly applied digital transformation during the COVID-19 pandemic. Healthcare organizations, storing endless documents with very sensitive patient data, have their allure increased among cyber-crooks. The article underlines the fact that hackers find these deficiencies quite appealing, particularly when looking at the slow and unwilling pace of technology adoption and revamping of old systems. Stringent data protection regulations like the Health Insurance Portability and Accountability Act (HIPAA) are catalyzing, cyber-badey organizations into pursuing healthcare systems as their targets for cyber-crime. Healthcare organizations are weakened by the absence of standardized practices for cybersecurity, adoption issues for interoperability, and deficiency in staff training. The article puts the weight on prevention actions as network patching, employee training, and advanced cyber threat detection technologies are vital to protect patient information and provide the high-security standards that correspond the evolving and sophisticated threats. KEYWORDS: Electronic platforms, Healthcare industry, Data protection regulations, Cyber-crime, COVID-19 pandemic.

Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data.

The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Medical data is sensitive and highly regulated and anonymization is often insufficient to protect a patient's identity. Traditional clustering algorithms are also unsuitable for longitudinal behavioral health trials, which often have missing data and observe individual behaviors over varying time periods. In this work, we develop a new decentralized federated multiple imputation-based fuzzy clustering algorithm for complex longitudinal behavioral trial data collected from multisite randomized controlled trials over different time periods. Federated learning (FL) preserves privacy by aggregating model parameters instead of data. Unlike previous FL methods, this proposed algorithm requires only two rounds of communication and handles clients with varying numbers of time points for incomplete longitudinal data. The model is evaluated on both empirical longitudinal dietary health data and simulated clusters with different numbers of clients, effect sizes, correlations, and sample sizes. The proposed algorithm converges rapidly and achieves desirable performance on multiple clustering metrics. This new method allows for targeted treatments for various patient groups while preserving their data privacy and enables the potential for broader applications in the Internet of Medical Things.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.

Insider Threats to the Military Health System: A Systematic Background Check of TRICARE West Providers.

To address the pandemic, the Defense Health Agency (DHA) expanded its TRICARE civilian provider network by 30.1%. In 2022, the DHA Annual Report stated that TRICARE's provider directories were only 80% accurate. Unlike Medicare, the DHA does not publicly reveal National Provider Identification (NPI) numbers. As a result, TRICARE's 9.6 million beneficiaries lack the means to verify their doctor's credentials. Since 2013, the Department of Health and Human Services' (HHS) Office of Inspector General (OIG) has excluded 17,706 physicians and other providers from federal health programs due to billing fraud, neglect, drug-related convictions, and other offenses. These providers and their NPIs are included on the OIG's List of Excluded Individuals and Entities (LEIE). Patients who receive care from excluded providers face higher risks of hospitalization and mortality. We sought to assess the extent to which TRICARE screens health care provider names on their referral website against criminal databases. Between January 1-31, 2023, we used TRICARE West's provider directory to search for all providers within a 5-mile radius of 798 zip codes (38 per state, ≥10,000 residents each, randomly entered). We then copied and pasted all directory results' first and last names, business names, addresses, phone numbers, fax numbers, degree types, practice specialties, and active or closed statuses into a CSV file. We cross-referenced the search results against US and state databases for medical and criminal misconduct, including the OIG-LEIE and General Services Administration's (GSA) SAM.gov exclusion lists, the HHS Office of Civil Rights Health Insurance Portability and Accountability Act (HIPAA) breach reports, 15 available state Medicaid exclusion lists (state), the International Trade Administration's Consolidated Screening List (CSL), 3 Food and Drug Administration (FDA) debarment lists, the Federal Bureau of Investigation's (FBI) list of January 6 federal defendants, and the OIG-HHS list of fugitives (FUG). Our provider search yielded 111,619 raw results; 54 zip codes contained no data. After removing 72,156 (64.65%) duplicate entries, closed offices, and non-TRICARE West locations, we identified 39,463 active provider names. Within this baseline sample group, there were 2398 (6.08%) total matches against all exclusion and sanction databases, including 2197 on the OIG-LEIE, 2311 on the GSA-SAM.gov list, 2 on the HIPAA list, 54 on the state Medicaid exclusion lists, 69 on the CSL, 3 on the FDA lists, 53 on the FBI list, and 10 on the FUG. TRICARE's civilian provider roster merits further scrutiny by law enforcement. Following the National Institute of Standards and Technology 800, the DHA can mitigate privacy, safety, and security clearance threats by implementing an insider threat management model, robust enforcement of the False Claims Act, and mandatory security risk assessments. These are the views of the author, not the Department of Defense or the US government.

Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.

A security framework to protect ePHI in Saudi Arabia's healthcare infrastructure

Today, protecting patient privacy and ensuring the accuracy and integrity of their data are the two most crucial concerns in the healthcare field. Unauthorized access or changes to patients' private health records can lead to serious issues. Moreover, if healthcare providers fail to update a patient's records quickly, it could result in dangerous, even life-threatening situations. Attacks on hospital computer systems also present a significant danger to patient care. Establishing strong security measures and procedures through cybersecurity frameworks can help protect sensitive patient information, known as electronic protected health information (ePHI). The Security Rule by Health Insurance Portability and Accountability Act (HIPAA), a well-established set of security guidelines, focuses on safeguarding ePHI held by healthcare organizations and their associates. This paper suggests creating a Data Cybersecurity Framework (DCF) specifically for the healthcare sector in Saudi Arabia. This framework aims to shield ePHI and align with the security recommendations of the HIPAA Security Rule. The development of this proposed framework involved consultations with healthcare cybersecurity experts and concentrated on the healthcare system in Saudi Arabia. The research concludes that enhancing the protection of patient information and raising public awareness requires the joint efforts of various entities, including government bodies.

Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment.

In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

Improving Access to Care Through Youth-Focused Virtual Sexual Health Navigation

PurposeGender and sexually diverse adolescents and young adults in Baltimore City, Maryland, are disproportionately impacted by HIV. The Virtual and Online Integrated Sexual Health Services for Youth program is a health navigation program which combines virtual sexual health service delivery and health navigation to link youth at risk for HIV acquisition to HIV testing/prevention and sexual healthcare services. MethodsYouth between 13 and 26 years old and residing in the Baltimore area were eligible to participate in the program. Demographic and engagement data from 238 youth (average age 21.4, SD = 2.5) who requested navigation were collected and recorded in a Health Insurance Portability and Accountability Act (HIPAA)-secure medical database and examined for associations between demographics, referral source, and the number of navigational services to which they were linked. Focused populations were defined as residents of high HIV prevalence zip codes who identify as sexual and gender diverse youth. ResultsReceipt of navigational services was significantly associated with self-identifying as sexually diverse. A multivariate regression revealed a significant association between the count of navigational services a youth was linked to and recording one's sexual orientation, identifying as a cisgender male, and residing in a high HIV-prevalence zip code. DiscussionVirtual health navigation has the potential to engage priority populations, including sexual and gender diverse youth. By refining linkage and identification approaches to health navigation, future outreach attempts can be tailored to support vulnerable communities, with the potential to improve sexual healthcare access.

Incidence of Colorectal Cancer After Intestinal Infection Due to Clostridioides difficile.

Clostridioides difficile (C. difficile or C. diff) is a toxin-producing bacteria that is notorious for causing life-threatening diarrhea. Recent literature has investigated various effects of Clostridioides difficile infection (CDI) in cancer patients, but research into the impact of CDI on the development of cancer and its effects on the microbiome is limited. CDI predominately affects the colon, which urges consideration into the sequalae of infection. This study investigated the correlation between CDI and the incidence of colorectal carcinoma (CRC). A retrospective study (2010 - 2020) was conducted using a Health Insurance Portability and Accountability Act (HIPAA) compliant national database. The International Classification of Disease ninth and 10th Codes (ICD-9, ICD-10), Current Procedural Terminology (CPT), and National Drug Codes were used to identify CRC diagnosis, CDI, and matching or control parameters. Patients were matched for age, sex, Charlson Comorbidity Index (CCI), region of residence, and CDI treatment. An additional, but separate, query was executed to include obese patients with and without CDI, who were similarly matched and assessed for CRC. Statistical analyses were implemented to assess significance and estimate odds ratios (ORs). CDI was associated with a decreased incidence of CRC (OR = 0.59, 95% confidence interval (CI): 0.55 - 0.63), and the difference was statistically significant (P < 2.2 × 10-16). CDI treatment, including appropriate antibiotics and fecal microbiota transplant (FMT), was controlled for in both infected and noninfected populations. Patients with a prior CDI who received relevant treatment were compared to patients with no history of CDI and received analogous treatment. Both populations subsequently developed CRC. Results remained statistically significant (P < 2.2 × 10-16) with a relative risk (RR) of 0.57 (95% CI: 0.54 - 0.60). Obesity was explored as a controlled variable in relation to CRC development in patients with and without prior CDI. Obese patients without a history of CDI were found to have a decreased risk of developing CRC. Results were statistically significant (P < 4.3 × 10-13) with an OR of 0.70 (95% CI: 0.63 - 0.77). This study shows a statistically significant correlation between CDI and decreased incidence of CRC. Additionally, there is a statistically significant correlation between obese patients with CDI and an increased incidence of CRC. Further research is needed to explore the mechanism of this striking relationship and the implications of CDIs on the microbiome.

Artificial Intelligence–Generated Draft Replies to Patient Inbox Messages

The emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making. To evaluate the implementation of a large language model used to draft responses to patient messages in the electronic inbox. A 5-week, prospective, single-group quality improvement study was conducted from July 10 through August 13, 2023, at a single academic medical center (Stanford Health Care). All attending physicians, advanced practice practitioners, clinic nurses, and clinical pharmacists from the Divisions of Primary Care and Gastroenterology and Hepatology were enrolled in the pilot. Draft replies to patient portal messages generated by a Health Insurance Portability and Accountability Act-compliant electronic health record-integrated large language model. The primary outcome was AI-generated draft reply utilization as a percentage of total patient message replies. Secondary outcomes included changes in time measures and clinician experience as assessed by survey. A total of 197 clinicians were enrolled in the pilot; 35 clinicians who were prepilot beta users, out of office, or not tied to a specific ambulatory clinic were excluded, leaving 162 clinicians included in the analysis. The survey analysis cohort consisted of 73 participants (45.1%) who completed both the presurvey and postsurvey. In gastroenterology and hepatology, there were 58 physicians and APPs and 10 nurses. In primary care, there were 83 physicians and APPs, 4 nurses, and 8 clinical pharmacists. The mean AI-generated draft response utilization rate across clinicians was 20%. There was no change in reply action time, write time, or read time between the prepilot and pilot periods. There were statistically significant reductions in the 4-item physician task load score derivative (mean [SD], 61.31 [17.23] presurvey vs 47.26 [17.11] postsurvey; paired difference, -13.87; 95% CI, -17.38 to -9.50; P < .001) and work exhaustion scores (mean [SD], 1.95 [0.79] presurvey vs 1.62 [0.68] postsurvey; paired difference, -0.33; 95% CI, -0.50 to -0.17; P < .001). In this quality improvement study of an early implementation of generative AI, there was notable adoption, usability, and improvement in assessments of burden and burnout. There was no improvement in time. Further code-to-bedside testing is needed to guide future development and organizational strategy.

How Sharp Objects Injuries Impact our Healthcare Workers: Unveiling Perspective and Preventive Imperatives.

Introduction Sharp object injuries in the medical field present a considerable occupational hazard for healthcare workers (HCWs), encompassing a spectrum of consequences from immediate discomfort to enduring health consequences. These injuries may expose HCWs to potential infections. Despite efforts to control sharp object injuries in healthcare environments, they are present at every stage involving using or disposing of medical sharp instruments. In Jordan, limited research has focused on sharp object injuries, with most data included from studies concentrating on practicing nurses or nursing students. Consequently, further research is necessary to comprehend the causes behind the high sharp object injury rate and the insufficient knowledge of safety practices and preventive guidelines. Objectives This study was conducted to investigate the impact of sharp object injuries on HCWs, underlying causes,and potential consequences causes of needlestick injuries. To highlight perspective and preventive imperatives. Methods and patients This retrospective institutional-based cross-sectional chart analysis was conducted by reviewing all sharp object injuries report sheets and extracting data directly from these reports for analysis. The study encompassed all reported cases occurring between 2018 and 2023. All the participants' data handling was accomplished according to the Declaration of Helsinki (2013) and the Health Insurance Portability and Accountability (HIPAA) Acts. Results A total of 146 self-reported hospital workers were included in the study. Within the final cohort, 52.73% of the participants were male (77/146), with an average age at diagnosis of 38.6±7.87 years (ranging from 20 to 52 years). Conversely, females comprised 47.27% of the cohort population (69/146) and had an average age at diagnosis of 34.73±6.73 years (ranging from 19 to 47 years). The age group 20-29 years was the most prominent age group, statistical analysis of age and gender data revealed significant differences. The overall prevalence of sharp object injuries was 11.83%, indicating that a sizable portion of HCWs is at risk of exposure to bloodborne pathogens. Among the different professional categories, Physicians constituted the majority of sharp object injuries reported victims in 41 cases (28.08%), followed by nurses in 38 cases (26.02%).Statistical analysis of the profession's data revealed significant differences (P<0.001). Notably, sharp object injuries were most reported in wards. The leading procedures that caused sharp object injuries were identified as during needle recapping in 53 instances (36.30%), then followed by medical waste treatment in 32 cases (21.92%). The left hand was the most affected body part, reported in 83 cases (56.84%).All injured individuals reported the incident promptly. No seroconversions were documented within the reviewed cases during the study period. Conclusion Injuries caused by sharp objects persist as a significant danger for hospital employees, posing immediate harm and long-term health risks linked to bloodborne pathogens. The findings stress the continuous responsibility of healthcare institutions to prioritize staff safety by addressing the root causes of sharp object injuries and fostering reporting and prevention cultures. Underreporting reasons are diverse, encompassing factors like time constraints, fear of consequences, and the misconception of injury insignificance.

Tissue-Based Genomic Testing in Prostate Cancer: 10-Year Analysis of National Trends on the Use of Prolaris, Decipher, ProMark, and Oncotype DX.

Prostate cancer (PCa) management is moving towards patient-tailored strategies. Advances in molecular and genetic profiling of tumor tissues, integrated with clinical risk assessments, provide deeper insights into disease aggressiveness. This study aims to offer a comprehensive overview of the pivotal genomic tests supporting PCa treatment decisions, analyzing-through real-world data-trends in their use and the growth of supporting literature evidence. A retrospective analysis was conducted using the extensive PearlDiver™ Mariner database, which contains de-identified patient records, in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes were employed to identify patients diagnosed with PCa during the study period-2011 to 2021. We determined the utilization of primary tissue-based genetic tests (Oncocyte DX®, Prolaris®, Decipher®, and ProMark®) across all patients diagnosed with PCa. Subsequently, within the overall PCa cohort, patients who underwent radical prostatectomy (RP) and received genetic testing postoperatively were identified. The yearly distribution of these tests and the corresponding trends were illustrated with graphs. During the study period, 1,561,203 patients with a PCa diagnosis were recorded. Of these, 20,748 underwent tissue-based genetic testing following diagnosis, representing 1.3% of the total cohort. An increasing trend was observed in the use of all genetic tests. Linear regression analysis showed a statistically significant increase over time in the use of individual tests (all p-values < 0.05). Among the patients who underwent RP, 3076 received genetic analysis following surgery, representing 1.27% of this group. Our analysis indicates a growing trend in the utilization of tissue-based genomic testing for PCa. Nevertheless, they are utilized in less than 2% of PCa patients, whether at initial diagnosis or after surgical treatment. Although it is anticipated that their use may increase as more scientific evidence becomes available, their role requires further elucidation.

DATA PRIVACY AND SECURITY IN IT: A REVIEW OF TECHNIQUES AND CHALLENGES

In today's interconnected digital world, data privacy and security have emerged as paramount concerns for individuals, organizations, and governments alike. This review provides a comprehensive review of techniques and challenges surrounding data privacy and security in information technology (IT) systems. The review begins by outlining the significance of data privacy and security in IT, emphasizing the proliferation of sensitive information stored and transmitted across various digital platforms. With the exponential growth of data collection, storage, and processing, ensuring the confidentiality, integrity, and availability of data has become imperative. Next, the review delves into the techniques employed to safeguard data privacy and security in IT environments. Encryption techniques, such as symmetric and asymmetric cryptography, play a crucial role in protecting data from unauthorized access and interception. Additionally, access control mechanisms, including authentication and authorization protocols, help manage user privileges and restrict unauthorized entry into sensitive data repositories. Furthermore, anonymization and pseudonymization techniques are utilized to conceal personally identifiable information (PII) and mitigate the risk of identity theft and privacy breaches. Moreover, the review discusses the challenges associated with data privacy and security in IT ecosystems. These challenges include the evolving nature of cyber threats, such as malware, ransomware, and social engineering attacks, which constantly test the resilience of IT defenses. Additionally, compliance with regulatory frameworks, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), presents significant challenges for organizations striving to adhere to stringent data protection standards while maintaining operational efficiency. Furthermore, emerging technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), introduce novel security risks and privacy concerns due to their interconnected nature and reliance on vast amounts of data. In conclusion, the review underscores the critical importance of continuously evaluating and enhancing data privacy and security measures in IT systems to mitigate risks, comply with regulations, and foster trust among stakeholders in an increasingly digitalized world.&#x0D; Keywords: Data, Privacy, Security, IT, AI.

Reidentification of Participants in Shared Clinical Data Sets: Experimental Study

Background Large curated data sets are required to leverage speech-based tools in health care. These are costly to produce, resulting in increased interest in data sharing. As speech can potentially identify speakers (ie, voiceprints), sharing recordings raises privacy concerns. This is especially relevant when working with patient data protected under the Health Insurance Portability and Accountability Act. Objective We aimed to determine the reidentification risk for speech recordings, without reference to demographics or metadata, in clinical data sets considering both the size of the search space (ie, the number of comparisons that must be considered when reidentifying) and the nature of the speech recording (ie, the type of speech task). Methods Using a state-of-the-art speaker identification model, we modeled an adversarial attack scenario in which an adversary uses a large data set of identified speech (hereafter, the known set) to reidentify as many unknown speakers in a shared data set (hereafter, the unknown set) as possible. We first considered the effect of search space size by attempting reidentification with various sizes of known and unknown sets using VoxCeleb, a data set with recordings of natural, connected speech from &gt;7000 healthy speakers. We then repeated these tests with different types of recordings in each set to examine whether the nature of a speech recording influences reidentification risk. For these tests, we used our clinical data set composed of recordings of elicited speech tasks from 941 speakers. Results We found that the risk was inversely related to the number of comparisons an adversary must consider (ie, the search space), with a positive linear correlation between the number of false acceptances (FAs) and the number of comparisons (r=0.69; P&lt;.001). The true acceptances (TAs) stayed relatively stable, and the ratio between FAs and TAs rose from 0.02 at 1 × 105 comparisons to 1.41 at 6 × 106 comparisons, with a near 1:1 ratio at the midpoint of 3 × 106 comparisons. In effect, risk was high for a small search space but dropped as the search space grew. We also found that the nature of a speech recording influenced reidentification risk, with nonconnected speech (eg, vowel prolongation: FA/TA=98.5; alternating motion rate: FA/TA=8) being harder to identify than connected speech (eg, sentence repetition: FA/TA=0.54) in cross-task conditions. The inverse was mostly true in within-task conditions, with the FA/TA ratio for vowel prolongation and alternating motion rate dropping to 0.39 and 1.17, respectively. Conclusions Our findings suggest that speaker identification models can be used to reidentify participants in specific circumstances, but in practice, the reidentification risk appears small. The variation in risk due to search space size and type of speech task provides actionable recommendations to further increase participant privacy and considerations for policy regarding public release of speech recordings.

Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format

By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format. To determine whether an LLM can transform discharge summaries into a format that is more readable and understandable. This cross-sectional study evaluated a sample of the discharge summaries of adult patients discharged from the General Internal Medicine service at NYU (New York University) Langone Health from June 1 to 30, 2023. Patients discharged as deceased were excluded. All discharge summaries were processed by the LLM between July 26 and August 5, 2023. A secure Health Insurance Portability and Accountability Act-compliant platform, Microsoft Azure OpenAI, was used to transform these discharge summaries into a patient-friendly format between July 26 and August 5, 2023. Outcomes included readability as measured by Flesch-Kincaid Grade Level and understandability using Patient Education Materials Assessment Tool (PEMAT) scores. Readability and understandability of the original discharge summaries were compared with the transformed, patient-friendly discharge summaries created through the LLM. As balancing metrics, accuracy and completeness of the patient-friendly version were measured. Discharge summaries of 50 patients (31 female [62.0%] and 19 male [38.0%]) were included. The median patient age was 65.5 (IQR, 59.0-77.5) years. Mean (SD) Flesch-Kincaid Grade Level was significantly lower in the patient-friendly discharge summaries (6.2 [0.5] vs 11.0 [1.5]; P < .001). PEMAT understandability scores were significantly higher for patient-friendly discharge summaries (81% vs 13%; P < .001). Two physicians reviewed each patient-friendly discharge summary for accuracy on a 6-point scale, with 54 of 100 reviews (54.0%) giving the best possible rating of 6. Summaries were rated entirely complete in 56 reviews (56.0%). Eighteen reviews noted safety concerns, mostly involving omissions, but also several inaccurate statements (termed hallucinations). The findings of this cross-sectional study of 50 discharge summaries suggest that LLMs can be used to translate discharge summaries into patient-friendly language and formats that are significantly more readable and understandable than discharge summaries as they appear in electronic health records. However, implementation will require improvements in accuracy, completeness, and safety. Given the safety concerns, initial implementation will require physician review.

DATA PRIVACY LAWS AND COMPLIANCE: A COMPARATIVE REVIEW OF THE EU GDPR AND USA REGULATIONS

This Review provides an overview of the comparative review of data privacy laws and compliance, focusing on the European Union's General Data Protection Regulation (EU GDPR) and data protection regulations in the United States. The analysis explores key similarities and differences, emphasizing their implications for businesses and individuals. The EU GDPR, implemented in 2018, stands as a landmark regulation governing data protection and privacy for individuals within the European Union and the European Economic Area. In contrast, the United States lacks a comprehensive federal data privacy law. Instead, it relies on a patchwork of sector-specific laws and state regulations, such as the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA). One major distinction lies in the overarching principles of these regulations. The EU GDPR adopts a comprehensive and rights-based approach, emphasizing individual rights to privacy, data portability, and the "right to be forgotten." In contrast, the U.S. system often focuses on specific industries or types of data, leading to a more fragmented regulatory landscape. Both regulatory frameworks incorporate principles of transparency, consent, and data breach notification. However, differences in enforcement mechanisms and penalties exist. The EU GDPR imposes significant fines for non-compliance, reaching up to 4% of a company's global annual revenue. In the U.S., penalties vary by state, and enforcement is often reactive, triggered by data breaches. Businesses operating globally must navigate these distinct regulatory landscapes, necessitating a nuanced approach to data privacy compliance. Multinational corporations must adhere to the more stringent requirements when handling EU citizens' data while also considering the diverse regulations within the U.S. This review underscores the ongoing evolution of data privacy laws worldwide and the critical importance for organizations to stay abreast of these developments. It emphasizes the need for a proactive and adaptive approach to data privacy compliance, taking into account the unique requirements and expectations of both the EU GDPR and U.S. regulations.&#x0D; Keywords: Data Privacy, Laws, Compliance, EU GDPR, Regulations.