The recent introduction of the Equal Access to Specialty Care Everywhere Act (EASE Act) by a bipartisan group of United States House representatives, including Reps. Michelle Steel (R-CA) and Susie Lee (D-NV), represents a move to enhance the delivery of specialty healthcare services to Medicare and Medicaid beneficiaries. This legislative proposal seeks to establish a virtual network utilizing existing Center for Medicare and Medicaid Innovation (CMMI) funds. The network aims to facilitate access to specialty care for underserved populations through various virtual care modalities, such as eConsults and telehealth, in coordination with patients’ primary care providers. By connecting patients with specialists in fields like cardiology, neurology, and endocrinology, the act aims to make quality healthcare more accessible, especially in rural and underserved areas, potentially reducing reliance on emergency room visits by providing early and appropriate care.
The EASE Act has garnered support from lawmakers, showing a bipartisan commitment to addressing healthcare accessibility and affordability challenges. Its referral to key committees is a step towards operationalizing a model that integrates virtual care into the existing healthcare framework, aiming to ensure more access to specialized medical services. The endorsement of the act by OCHIN, a nonprofit focused on research and innovation in healthcare, outlines the potential benefits of the act in expanding clinical capacity and reducing healthcare costs, especially in communities that traditionally face barriers to accessing specialty care.
This legislative effort is set against challenges in accessing specialty healthcare, particularly for patients in rural communities served by Medicare and Medicaid. Research indicates major obstacles in securing new patient specialty visits, with some specialties like orthopedics being particularly difficult to access. By advocating for a virtual network that connects primary care providers with specialists, the EASE Act proposes a solution to improve healthcare delivery and improve the patient experience. This approach aims to utilise technology to overcome geographical and logistical barriers, ensuring patients have the necessary access to specialist care when needed.
In advocating for the EASE Act, lawmakers and supporters display the potential of virtual care technologies to improve healthcare delivery, in the context of increasing healthcare demands and the transition towards more integrated care models. The act’s focus on enhancing access to specialty care through digital platforms may effectively use technology in modern healthcare to create a more efficient healthcare system that can better meet the needs of underserved populations, ultimately improving health outcomes and reducing healthcare costs
The fast advancement of generative artificial intelligence (AI) and large language models (LLMs) in clinical medicine is quickly changing the processes of patient care. This is most impactful on everyday clinical tasks, where technologies like LLMs designed to summarize clinical notes, medications, and patient data, are nearing patient implementation without the oversight of the United States Food and Drug Administration (FDA). The utility of LLMs in creating concise and up-to-date clinical snapshots from a range of data sources within electronic health records (EHRs) signifies that patient care is becoming more efficient. However, this also shows potential concerns regarding the safety and efficacy of these AI tools in clinical settings, given their capacity to bypass FDA medical device oversight. The possibility of LLMs entering clinical practice without rigorous safety assessments is worrying, and is a reminder that careful implementation and evaluation to ensure they serve as beneficial aids rather than sources of inadvertent harm, is still highly necessary
The task of summarizing clinical data is inherently complex and variable within LLM outputs. These models, while offering the promise of improved clinical documentation and decision support, also present challenges in ensuring the accuracy, consistency, and reliability of their summaries. Variations in the length, organization, and tone of LLM-generated summaries can influence clinician interpretations and decision-making in subtle but substantial ways. This variability, along with the probabilistic nature of LLMs, raises concerns about the potential for these tools to introduce biases or errors into clinical decisions. For example, differences in summaries can emphasize certain patient conditions over others or frame clinical histories in ways that might sway diagnostic or treatment pathways. Such nuances portray the importance of developing comprehensive standards and rigorous testing protocols for LLM-generated clinical summaries to ensure they contribute positively to patient care.
The phenomenon of “sycophancy” bias within LLM-generated summaries illustrates the difficulty of AI interactions with clinical decision-making. LLMs may produce summaries that align too closely with clinicians’ preexisting beliefs, potentially exacerbating confirmation biases and diagnostic errors. This issue is particularly pertinent in scenarios where subtle prompt variations can lead to different summaries, emphasizing the need for clinicians to approach LLM-assisted decision-making with a critical eye. Small errors in summaries, though seemingly minor, can have profound implications for clinical judgments and patient outcomes. These considerations show the need for transparency, rigorous testing, and the establishment of robust standards to mitigate risks associated with their use.
The path forward for the integration of LLMs in clinical settings necessitates a well thought out approach, combining regulatory oversight, clinical validation, and the development of comprehensive standards for AI-generated summaries. Despite the absence of clear legal authority for the FDA to regulate most LLMs under current statutes, there is a requirement for industry-wide collaboration to establish guidelines that ensure the safety, accuracy, and utility of AI tools in healthcare. This effort should extend beyond the confines of large technology companies and include stakeholders from the clinical and scientific communities. By prioritizing the development of standards that address accuracy, bias, and the potential for clinical errors, alongside proactive regulatory clarifications, healthcare providers can derive the benefits from LLMs while mitigating the risks.
The Department of Health and Human Services (HHS) Office for Civil Rights (OCR) has imposed a large financial penalty on Montefiore Medical Center as an enforcement action to address alleged violations of the Health Insurance Portability and Accountability Act (HIPAA). Montefiore Medical Center, a prominent non-profit hospital system based in New York City, has agreed to a settlement involving a $4.75 million penalty. This settlement is a success for the OCR, as they continue their strict enforcement of HIPAA regulations, and shows the severity of the infractions identified during the investigation. The case stems from a 2015 incident reported by the New York Police Department, where it was discovered that a patient’s protected health information (PHI) had been stolen by an employee, leading to an extensive audit of Montefiore Medical Center’s compliance with HIPAA mandates.
The OCR’s investigation into Montefiore Medical Center revealed lapses in adherence to the HIPAA Security Rule, with specfic failures in conducting a comprehensive risk analysis and in implementing procedures to monitor information system activity. These infractions, deemed severe by the OCR, led to the unauthorized access and theft of PHI from 12,517 patients, an act perpetrated by an employee over six months. This incident, along with subsequent insider breaches reported in the following years, are derived from systemic issues in Montefiore Medical Center’s management of PHI and its protection against insider threats. In response to these findings, Montefiore Medical Center has agreed to a corrective action plan that includes a thorough risk assessment and the development of a risk management plan to address identified vulnerabilities.
Following the breach and investigation, Montefiore Medical Center has undertaken efforts to enhance its HIPAA compliance and data security framework. The organization has implemented a more rigorous system for monitoring access to patient records, aiming to swiftly detect and respond to any unauthorized access attempts. It is positive that the organization has taken direct response to the identified lapses, particularly the incidents of insider breaches that compromised patient privacy. Montefiore has also updated its policies and procedures related to the protection of electronic protected health information (ePHI), aligning its practices more closely with HIPAA requirements. These updates include revising the way staff members are trained on privacy and security policies, ensuring that all personnel are fully aware of their responsibilities under HIPAA and the grave implications of policy violations.
In compliance with the settlement’s terms, Montefiore Medical Center has also agreed to a corrective action plan that mandates a thorough reassessment of its security measures and the development of a comprehensive risk management strategy. This plan is aimed at addressing the specific vulnerabilities that led to past breaches and establishing a strong, ongoing process for evaluating and mitigating potential risks to ePHI security. The corrective actions undertaken by Montefiore, alongside its commitment to extensive staff training and the adoption of advanced security technologies, signify a proactive approach to prevent future incidents of unauthorized access.
Findhelp has partnered with the Commonwealth’s Department of Human Services and members of Pennsylvania’s Consortium of Health Information Exchanges (HIEs) to launch PA Navigate. The primary objective of this is to address the social determinants of health (SDoH) across Pennsylvania. This statewide online social care tool is a new initiative to lessen the gap between Pennsylvanians and basic community services. Designed to meet needs such as food, shelter, and transportation, PA Navigate utilizes Findhelp’s closed-loop referral platform, demonstrating an innovative approach to social care. This collaboration is a positive step in Pennsylvania’s ability to use technology for improving the well-being of individuals, ensuring that essential services are accessible to all Pennsylvanians with dignity and privacy.
While PA Navigate is a tool for individuals seeking assistance; it is also a comprehensive resource for social service agencies, local nonprofits, community-based organizations, and healthcare providers. This system enables these entities to make connections to services on behalf of individuals, track referral outcomes, and ensure that the needs of the community are met. By working closely with the four HIE Consortium members—Central PA Connect, Clinical Connect HIE, HealthShare Exchange, and Keystone Health Information Exchange / KeyHIE—Findhelp aims to address the overall economic and social conditions that influence health outcomes. The collaboration effort focuses on the importance of social determinants in achieving good health outcomes.
The development and implementation of PA Navigate shows that Findhelp is committed to enhancing access to social care services. With more than 750,000 Pennsylvanians already using Findhelp’s platform to connect with social care services, the company has established a strong foundation of trust within the community. This trust, combined with Findhelp’s advanced integration capabilities and its recognition as a leader in SDoH networks by KLAS, positions PA Navigate as a tool in the ongoing effort to address social health determinants. The platform’s ability to offer healthcare providers a comprehensive view of an individual’s social care history is a fantastic use of technology to change healthcare delivery in the long term, ensuring that individuals receive holistic care that includes both medical and social needs.
Findhelp is at the forefront of creating a more integrated and accessible healthcare system by initiating a collaboration among health information exchanges, state agencies, and healthcare organizations,. This partnership highlights the important role of social determinants in health outcomes and sets a precedent for how technology can be utilized to improve the well-being of entire communities. As PA Navigate rolls out across Pennsylvania, it may become a model for other states to follow, showcasing how innovation and collaboration can lead to a more equitable healthcare landscape.
Electronic Health Record (EHR) systems were designed with the intention of streamlining medical processes. The efficiency they promised has been a point of debate for many in the healthcare sector. A recent KLAS report, conducted by interviews with 67 healthcare organizations, provides a an insight on this ongoing issue in the healthcare industry.
The Significance and Issue of EHR Efficiency
EHR’s effectiveness remains a key concern in the clinician experience, as shown by LAS Arch Collaborative research. Despite its potential, there is a gap in its performance: only 46% of clinicians feel their EHR system enhances efficiency. As well as this, there is a clear direct correlation between the lack of EHR efficiency and clinician burnout. Consequently, many healthcare institutions are seeking external help to boost EHR efficiency and improve the user experience.
Key Offerings to Enhance EHR Efficiency
The KLAS report identifies six primary offerings designed to improve EHR efficiency:
- Clinical Transformation Strategy: This includes formulating strategies for clinician efficiency, spearheading informatics program development, and initiating delivery-model alterations.
- Technical Build Assistance: Creating EHR systems and third-party applications.
- Virtual Scribes: Services focused on optimizing clinical documentation.
- Workflow Assessment and Refinement Services: Offering staff for customization initiatives, refining existing processes, and making documentation enhancements via efficiency data insights.
- Vendor Selection: Assisting in the EHR selection process, evaluating EHR modules, and pinpointing efficiency-driven add-ons.
- Interoperability Optimization: Aiming to make externally-sourced patient data more actionable for clinicians.
The report also singles out three software enhancements to augment EHR efficiency:
- Documentation Burden Reduction: Aims at data aggregation and visualization in the EHR, supplemented by features like ambient voice and real-time speech recognition.
- Message/Task Management (Inbox): A review and restructuring of message flows to correct inefficiencies.
- Team Communication and Coordination: Enhancing team communication and automating a series of tasks.
Spotlight on EHR Vendors
EHR vendors such as athenahealth, Epic, MEDITECH, and NextGen Healthcare are noted in the report for their contributions:
- Athenahealth is distinguished for its extensive service offerings and software solutions, ranging from strategy development to care team communication.
- Epic is credited for its focus on strategy, application enhancement, and workflow design, among other areas.
- MEDITECH stands out for its service offerings that span from strategy formulation to efficiency data analysis.
- NextGen Healthcare, while spotlighted for a narrower range of services, is acknowledged for software solutions emphasizing data visualization and task coordination.
Gathering Feedback via KLAS Interviews
KLAS conducts numerous interviews with healthcare professionals annually, aiming to understand their perspectives on IT solutions. For this specific report, vendors were prompted to identify deep adopters of their clinician efficiency offerings. These adopters were then interviewed by KLAS to validate the offerings and extract insights about their respective experiences.
A Closer Look at the KLAS Arch Collaborative
The Arch Collaborative is a conglomerate of healthcare organizations with a shared vision: to improve the EHR experience. This is achieved through standardized surveys and benchmarking exercises. With participation from almost 300 healthcare organizations and feedback accumulated from over 440,000 clinicians, the Arch Collaborative’s mission is to transform patient care by maximizing the EHR’s capabilities.