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Pediatricians work to help children growing up in poverty

While pediatricians are being urged to screen their patients for poverty, some doctors are reluctant to ask families about their finances for fear the questioning may seem invasive or will identify problems that they can't treat, according to an NPR report...

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Our mission is to collaboratively facilitate implementation of innovative healthcare models supporting quality, cost-effectiveness, and health promotion for the improvement of the well-being of community health systems. The vision of the Caravan Health is to be the national leader of the transformation of community healthcare systems from fee-based to value-based care by creating an affordable, replicable framework that results in improved health for our communities, at the lowest possible cost, and strengthens and preserves local access to care.

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Health Technology, Digital Healthcare

Unlocking the Puzzle: Navigating EHR Interoperability Solutions

Article | August 16, 2023

Unlock EHR interoperability solutions with this article. Discover how healthcare overcomes EHR interoperability challenges to facilitate seamless information sharing for better clinical decisions. 1. Exploring Hurdles in EHR Interoperability 2. Addressing EHR Interoperability Challenges: Mapping Effective Paths 2.1 Upgrading from Outdated Legacy Systems 2.2 Managing Inconsistent Information Across Multiple Sources 2.3 Overcoming Organizational Resistance to Sharing Data 2.4 Balancing Security and Consent 2.5 Harmonizing Data Standards Across Diverse Software Systems 2.6 Optimizing Training Resources for EHR Interoperability 2.7 Strategizing Costs for Specialist-driven Interoperability Management 2.8 Navigating Budget Constraints in EHR Interoperability 2.9 Unifying Patient Identification Standards Across HIEs 2.10 Advancing Allergy Management to Enhance Patient Care 3. Embracing Interoperability for a Connected Healthcare Future 1.Exploring Hurdles in EHR Interoperability Despite significant efforts and investments in health information systems and technology, coupled with many years of widespread availability, the full benefits of electronic health records (EHRs) still need to be realized. The reality is that most physicians continue to rely on faxing and mailing patient records, just as they did a decade ago. Numerous government-certified EHR products are being used, each utilizing distinct clinical terminologies, technical specifications, and functional capabilities. These differences make it challenging to establish a unified standard interoperability format for data sharing. Interestingly, even EHR systems built on the same platform might not be interoperable, as they are frequently highly customized to an organization’s specific workflow and preferences. Given these circumstances, the article examines ten challenges and their corresponding EHR interoperability solutions to enhance patient care. 2.Addressing EHR Interoperability Challenges: Mapping Effective Paths The primary goal of healthcare interoperability is to enable seamless sharing of health-related information between healthcare providers and patients, aiding in clinical decision-making. Here are several challenges to accomplishing this aim, along with their corresponding interoperability solutions: 2. 1 Upgrading from Outdated Legacy Systems One of the significant challenges in achieving EHR interoperability is the need to transition from outdated legacy systems. Many healthcare facilities still rely on older, proprietary EHR systems that need more compatibility and standards to communicate seamlessly with modern, interconnected healthcare networks. These legacy systems often need more data exchange capabilities, leading to inefficiencies, data inconsistencies, and barriers to collaborative patient care. The intricate process of upgrading or replacing these systems while ensuring data integrity and continuity of care poses a considerable obstacle to achieving comprehensive EHR interoperability. Healthcare institutions need to implement a strategic and phased approach to address this challenge. This involves assessing the existing EHR, identifying interoperability gaps, and selecting modern healthcare interoperability solutions that adhere to industry standards, such as Fast Healthcare Interoperability Resources (HL7 FHIR) and open APIs. A well-defined migration plan should be developed, including data migration, new system integration, and staff training. Collaboration with EHR vendors, IT experts, and clinical stakeholders is crucial to ensuring a smooth transition. 2.2 Managing Inconsistent Information Across Multiple Sources As patients move through different healthcare settings and encounter various medical professionals, their health information becomes distributed across multiple sources, leading to discrepancies, duplications, and variations in data. This inconsistency can compromise patient safety, treatment accuracy, and healthcare quality. Furthermore, different institutions' varying data formats, coding systems, and documentation practices exacerbate the challenge of creating a unified and accurate patient record. A potential solution to this challenge involves developing and adopting standardized data exchange protocols. By implementing common data standards and practices, healthcare providers can ensure that patient information is accurately represented and uniformly understood across different systems. In addition, robust data validation processes and reconciliation algorithms can help identify and rectify inconsistencies during data integration. Moreover, creating a centralized patient identity management system that links various patient records to a single, accurate identity can significantly mitigate the issue of duplicated or mismatched information. 2.3 Overcoming Organizational Resistance to Sharing Data This EHR interoperability challenge pertains to the reluctance of healthcare institutions, clinics, and providers to readily exchange patient information and medical records due to concerns over data privacy, competitive advantage, and operational complexities. This resistance often leads to fragmented patient care, hindered medical research, and compromised clinical decision-making. Addressing this challenge necessitates the establishment of clear data-sharing protocols, robust privacy safeguards, and incentivized collaboration. By fostering a culture of trust, emphasizing the collective benefits of data exchange, and implementing interoperability standards, the healthcare ecosystem can encourage reluctant organizations to actively share essential patient data, ultimately leading to improved patient outcomes and more efficient healthcare delivery. 2.4 Balancing Security and Consent This challenge in EHR interoperability revolves around the delicate equilibrium between ensuring patient data security and privacy while enabling the seamless sharing of EHRs across different healthcare systems. Striking the right balance involves addressing concerns about unauthorized access, data breaches, and patient consent preferences. While robust security measures are necessary to safeguard sensitive health information, overly stringent restrictions can hinder the efficient exchange of vital medical data, potentially impeding timely and informed patient care, medical research, and healthcare system efficiency. Potential EHR interoperability solutions to this challenge include implementing a layered security and consent management approach. This involves combining strong encryption, authentication protocols, and access controls to ensure the integrity and confidentiality of EHRs. Moreover, the adoption of standardized and granular consent mechanisms empowers patients to regulate both access to their data and the purposes for which it can be accessed. An integrated framework that employs advanced technologies like blockchain for secure audit trails and data-sharing logs can enhance transparency and accountability. Furthermore, patient education and awareness campaigns can empower individuals to make informed data-sharing decisions, fostering a collaborative environment where security, consent, and interoperability coexist harmoniously. 2.5 Harmonizing Data Standards Across Diverse Software Systems This challenge encompasses integrating and exchanging medical data across various software platforms and applications used within the healthcare industry. To tackle this challenge, a comprehensive solution includes the widespread adoption and adherence to standardized data formats, coding conventions, and communication protocols by developers, healthcare organizations, and EHR integration software. To address this challenge, a comprehensive solution involves the establishment of standardized data formats, coding conventions, and communication protocols widely adopted and adhered to by EHR software developers and healthcare organizations. This could be achieved through industry collaboration, government regulations, and incentives for adopting interoperability standards. Additionally, implementing APIs that translate and map data between different formats can help bridge the gap between diverse software systems. 2.6 Optimizing Training Resources for EHR Interoperability This hurdle involves preparing healthcare professionals, IT staff, and other stakeholders to effectively navigate and implement interoperable EHR systems. Ensuring that healthcare personnel possess the necessary skills and knowledge to seamlessly integrate, maintain, and utilize interconnected EHR systems amidst rapidly evolving technology and standards poses a significant hurdle. This challenge involves understanding the intricacies of interoperability protocols and grasping the broader context of data security, patient privacy, and efficient data exchange among diverse healthcare entities. To address this challenge, developing comprehensive and up-to-date training programs that cover both technical aspects (interoperability standards, APIs, and data formats) and practical considerations (security protocols, data governance) is crucial. Collaborations with vendors, industry experts, and academia can ensure the training content remains aligned and updated with current EHR trends. Integrating EHR interoperability education into medical and IT curricula can also lay a foundation for future professionals. Continuous learning opportunities, including EHR analytics courses, certifications, and knowledge-sharing platforms, can further bolster the continual development of skills and knowledge exchange. This process cultivates a skilled workforce capable of fully leveraging EHR interoperability while upholding the integrity and privacy of patient data. 2.7 Strategizing Costs for Specialist-driven Interoperability Management This challenge pertains to the complex and costly task of ensuring seamless data exchange among diverse EHR systems, mainly when managed by specialists with domain-specific knowledge. These specialists play a crucial role in tailoring EHR interoperability solutions to the unique needs of their medical domains. Still, the financial implications of such endeavors can be substantial, involving customization, integration, and maintenance expenses. Finding an effective solution requires a multi-faceted approach involving standardized interoperability frameworks, modular system design, strategic resource allocation, and collaborative partnerships among EHR vendors, healthcare institutions, and specialists. By optimizing the balance between customization and standardization and leveraging technological advances like APIs and cloud computing, healthcare ecosystems can mitigate costs while achieving efficient and secure data exchange that benefits patients and healthcare providers. 2.8 Navigating Budget Constraints in EHR Interoperability This issue relates to healthcare organizations' significant financial limitations when striving to establish seamless EHR data exchange across disparate systems. As healthcare entities aim to enhance patient care coordination and data accessibility, the cost of implementing and maintaining interoperable EHR systems becomes a substantial hurdle. This challenge necessitates a delicate balance between allocating resources for EHR integration, customization, and ongoing maintenance while ensuring that patient data remains secure and accessible to authorized stakeholders. A possible avenue to deal with the budget constraints in EHR interoperability is the strategic adoption of open-source frameworks. By leveraging open-source solutions, healthcare organizations can reduce licensing fees and development costs associated with proprietary systems, allowing them to allocate resources more efficiently. Additionally, collaborating with industry consortia and governmental initiatives that promote standardized data exchange protocols can foster economies of scale, streamlining the implementation process. Moreover, investing in cloud-based technologies can offer scalable and cost-effective data storage and sharing infrastructure. 2.9 Unifying Patient Identification Standards Across HIEs The crux of this issue involves the need for consistent patient identification methods across different healthcare systems and data-sharing networks. This inconsistency results in errors, data duplication, and compromised patient safety as information is exchanged between entities. Without a standardized patient identification system, accurate matching of patient records becomes a complex endeavor, hindering the seamless exchange of EHRs and undermining the potential benefits of interoperability. To address this challenge, a comprehensive solution involves establishing and adopting a universally recognized patient identification standard that spans all participating HIEs. This standard could include using unique patient identifiers or a combination of demographic, biometric, and cryptographic identifiers to ensure accurate and secure patient matching. Additionally, implementing advanced data governance practices, strong privacy protections, and robust data validation algorithms would enhance the accuracy and security of patient identification. Collaboration between healthcare organizations, government agencies, and technology experts is crucial to developing and implementing this standardized approach, fostering a more interconnected and effective healthcare ecosystem while safeguarding patient privacy and data integrity. 2.10 Advancing Allergy Management to Enhance Patient Care Healthcare providers need help seamlessly sharing allergy-related patient data across different EHR platforms, hindering comprehensive patient care. This lack of interoperability leads to fragmented information, potential medication errors, and compromised treatment decisions, ultimately impacting patient safety and outcomes. One viable solution for addressing this challenge is to establish standardized data exchange protocols alongside a unified health information exchange framework. Implementing FHIR standards can enable the consistent and secure sharing of allergy information among EHR systems. Additionally, incentivizing healthcare organizations to adopt these interoperability EHR standards and invest in compatible technologies will promote a cohesive ecosystem where allergy data can be accurately and swiftly exchanged. Collaborative efforts among EHR vendors, healthcare providers, and regulatory bodies are essential to ensure the seamless flow of allergy-related information, resulting in enhanced patient care, reduced medical errors, and improved healthcare efficiency. 3.Embracing Interoperability for a Connected Healthcare Future With the goal of a cohesive healthcare future in mind, the value of embracing interoperability is immeasurable. This article highlights the essential role of interoperability in overcoming the challenges posed by fragmented data and improving patient outcomes. As healthcare systems continue to develop, the smooth exchange of EHRs becomes crucial, fostering collaboration among diverse stakeholders and facilitating well-informed decision-making. By creating an environment in which EHRs can seamlessly communicate, healthcare providers have the potential to offer more comprehensive, patient-centered care, minimize duplication, and expedite both diagnoses and treatments. Although achieving an interoperable healthcare ecosystem may involve complexity, the benefits of efficiency, precision, and overall quality of care underscore its necessity as a transformative journey.

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Health Technology, Digital Healthcare

Unlocking Better Health Online: Exploring Power of EHR Telemedicine

Article | September 7, 2023

Embark on a journey into the frontier of healthcare innovation in this article. Discover how EHR telemedicine and remote patient monitoring serve as catalysts, driving forward a new era in healthcare. Contents 1. Integration of EHRs in Telemedicine and Remote Patient Monitoring 2. Technical Challenges and Solutions in EHR Integration 3. Financial Analysis: Cost-Benefit Assessment of Integration 4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems 5. Forging Stronger Patient-Clinician Relationships 1. Integration of EHRs in Telemedicine and Remote Patient Monitoring EHR telemedicine and remote patient monitoring have reshaped healthcare delivery by seamlessly integrating electronic health records, allowing healthcare providers and patients to exchange information effortlessly, regardless of geographical barriers. This synergy empowers healthcare professionals to access patients' comprehensive medical histories in real time, facilitating more informed decision-making during virtual consultations. During the spring of 2020, when pandemic restrictions kept most people in the US at home, the use of telehealth rose to about 51%. [Source: Elation Health] Moreover, it enhances the accuracy of remote patient monitoring by providing up-to-date data, enabling timely interventions and improving overall healthcare outcomes. Integrating EHR telemedicine systems enhances efficiency and ensures that patient care remains at the forefront of modern healthcare, transcending traditional physical boundaries. 2. Technical Challenges and Solutions in EHR Integration Navigating telehealth EHR integration and remote patient monitoring solutions uncovers a range of technical challenges, each with its own set of potential remedies. These include interoperability issues, which can be mitigated by adopting standardized data formats like HL7 FHIR. EHR interoperability solutions may involve using data exchange protocols such as HL7's Consolidated Clinical Document Architecture (C-CDA) or developing custom APIs to facilitate seamless data exchange between EHRs and telemedicine platforms. Additionally, the imperative need for data security and privacy is achieved through robust encryption and adherence to regulations like HIPAA or GDPR. Data integration challenges arising from varying EHR data storage methods can be resolved using middleware or integration platforms. Investing in telecom infrastructure and developing offline-capable telemedicine apps can address limited connectivity in remote areas. Ensuring real-time data access involves optimizing EHR databases and creating low-latency systems. Other challenges encompass integrating data from medical devices, ensuring data accuracy, scalability, user-friendly interfaces, regulatory compliance, and cost management strategies. 3. Financial Analysis: Cost-Benefit Assessment of Integration When contemplating the integration of EHR telemedicine and remote patient monitoring systems, conducting a comprehensive cost-benefit analysis is crucial. This assessment covers financial aspects, including initial implementation costs (software development, hardware upgrades, training, and data migration), ongoing operational expenses (maintenance and data storage), and potential efficiency gains (streamlined workflows and improved data accessibility). It also evaluates the impact on patient outcomes, satisfaction, and financial benefits of enhanced healthcare quality, reduced readmissions, and increased patient engagement. Healthcare organizations can estimate cost savings in remote patient monitoring and explore expanding telemedicine services to underserved populations to make informed financial decisions. Additionally, this analysis considers long-term financial viability and alignment with organizational goals, including regulatory compliance costs, risk assessment, scalability considerations, and the competitive advantages of integrated telemedicine services. By calculating ROI and assessing potential risks, healthcare entities can develop risk mitigation strategies, ensuring that EHR integration in telemedicine and remote patient monitoring enhances healthcare delivery and aligns with the organization's financial sustainability and long-term success. 4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems Data privacy and obtaining informed consent are paramount in integrated EHR and telemedicine systems. Patients should provide explicit consent, understanding the data collected and its intended use, with strict encryption protocols safeguarding data during transmission. Access controls and data minimization practices restrict unauthorized access, while patient portals enable individuals to manage their data-sharing preferences and revoke consent if needed. Compliance with regulations such as HIPAA or GDPR is crucial, as is maintaining comprehensive audit trails to track data access. Training, awareness, and robust incident response plans fortify data privacy efforts, fostering trust and transparency in these integrated systems where healthcare organizations and patients share responsibility for secure data handling. 5. Forging Stronger Patient-Clinician Relationships Integrating EHR telemedicine and remote monitoring systems goes beyond mere efficiency and accessibility objectives. It serves as a catalyst for nurturing more substantial and meaningful patient-clinician relationships. This fusion of technology and healthcare has the capacity to bridge physical distances, allowing clinicians to truly understand and engage with their patients on a deeper level. Patients, armed with increased access to their health data, become more active participants in their healthcare, while clinicians, with their comprehensive information, can offer more personalized and informed guidance. The potential of EHR telemedicine reaches far beyond the digital screen; it empowers both patients and clinicians to collaborate in pursuit of improved health outcomes, ushering in a new era of patient-centric care grounded in trust, communication, and shared knowledge.

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Health Technology, Digital Healthcare

AI in Healthcare Businesses: From Efficient Uses Cases to ROI Growth

Article | August 21, 2023

It’s no secret now that healthcare is an in-demand field. Today, business leaders need modern and intelligent decision-making solutions for their customers and clients. They must also focus on the right investment areas and learn the tricks for investing, spending, and setting goals for revenue generation to accelerate business. With continuous developments in the healthcare sector, integrating AI into processes can help increase ROI. Therefore, if you, like any other business leader, are looking for solutions to empower your services and products in the healthcare domain, this article will help you through AI’s ultimate use cases and churn out a higher ROI. What’s with AI in Healthcare at Present? AI’s role in healthcare is evolving and enhancing traditional business operations, particularly marketing. According to a study by IBM, 71% of customers expect real-time communication. Thus, global demand is fueling the rising adoption of AI marketing solutions. The effects of AI in healthcare are evident. Gartner reports increased marketing efficiency and effectiveness (86%), improved decision-making (71%), better data analysis and new insights (79%). Global AI spending will rise from $450 million in 2019 to over $28 billion by 2024 is not surprising. Similar and further studies are ongoing on various use cases of AI in healthcare at scale. What are the efficient use cases of AI that will help healthcare businesses boost their ROI? Let’s find out. How is AI Applied in Healthcare? The promising applications of AI in healthcare to improve outcomes are very intriguing. While there is still much to achieve in the AI-dependent healthcare business, there is sufficient potential that tech companies are willing to invest in AI-powered tools and solutions. Let’s examine the potential examples of AI in healthcare to prepare and support business strategies accordingly and foster higher ROI generation. Predictive Analytics AI-based predictive analytics impacts a business by automating administrative tasks, predicting sales outcomes for a year, customers’ behavior and making strategies accordingly. According to a Forbes study, AI-based predictive analytics can save businesses $18 billion in tasks, expenses, and pricing. To understand this, one example of using AI to automate admin tasks is a collaboration between the Cleveland Clinic and IBM. Cleveland Clinic uses IBM’s Watson to mine big data and provides personalized services for customers and clients on marketing deeds. Some of the practical applications of AI and predictive analytics in healthcare are: Monitoring market trends to maximize marketing efforts Organizing datasets Creating marketing campaigns tailored to each demographic-based client Mining collective data for future decision-making Fraud Prevention AmerisourceBergen Corp detects fraud and misleading business operations through AI. A sales account team conducts audits with AI to detect usual lea and queries to prevent hefty expenses for businesses. The example explains that implementing AI in your process will help detect any significant fraud attempts inside your business operation. This will help your business save huge expenditures. Boost Sales By putting down false leads, AI helps in maximizing sales numbers, resulting in significant ROI generation. For example, AI transforms data into personalized data, which reduces the cost of operations. Chatbots Most healthcare businesses leverage chatbots on their websites to engage more and more customers and boost engagement. In this way, businesses tend to gain multiple leads and convert them into clients by providing the best marketing solutions. Chatbots are fruitful for AI start-ups in healthcare—small businesses can deploy AI to their websites. By doing so, they can save millions in administrative costs and attract numerous leads. The most prominent examples of AI in healthcare hail from giant tech titans such as IBM, Amazon, and Microsoft. They are assisting healthcare providers with AI to create and deploy digital-human employees. Segmentation of Marketing Targets Is your target audience not responding to your marketing campaigns (for example, by not clicking a link, subscribing or unsubscribing to a newsletter, or not registering for a medical event)? If that's the case, how should you go ahead? Using AI-based tools allows your marketing to easily identify target behaviors and reactions based on the type of marketing actions to be carried out. Analyzing these actions can help segment targets based on your company's marketing objectives. The most significant development took place in April 2022, when Amazon Alexa became fully HIPAA compliant. It works with health developers and service providers that manage protected information for customers. AI Leads to Data Modernization It’s all about the data—not any data! There’s a precise association between AI and data management, resulting in data modernization. According to a Cognizant research study, healthcare leaders have made significant progress in modernizing their data. In contrast, most upcoming businesses are expecting to do so by 2024. The maximum acceleration of AI in modernizing data will be seen in the manufacturing and marketing of healthcare products and services, respectively. It is because AI helps to churn data easily. The accessibility of data, in particular, becomes simpler with automation than doing it manually, which generates a massive amount of data. Such effects of AI in healthcare can be one of the prime reasons for the higher ROI of your business in the future. “There has never been a greater need for skilled analytic talent in health care. Because AI is becoming more strategic, organizations must ensure access to this skill set, either by growing their analytic teams or seeking out experienced partners." Steve Griffiths, CEO of Optum Enterprise Analytics AI Expenditure is on the Rise McKinsey says that by 2025, the use of AI in healthcare will be widespread, resulting in significant expenditure by global healthcare leaders. AI is a significant concern for healthcare decision-makers, investors, and innovators as customers extensively engage and react to AI-powered services and solutions. AI is constantly bringing improvements to almost all processes, including cost savings, management of services and products, and monitoring of multiple operations. Even small businesses in the healthcare industry are proactively investing in AI applications to match steps with the current wave of innovation in healthcare services. Accelerate ROI Using AI AI in healthcare is becoming one of the prime responsible technologies for accelerating ROI. Technology can eradicate multiple business growth challenges. Let’s find out how. Enhanced Performance As previously stated, use cases of AI in healthcare can relieve stress on employees. This would allow them to devote their time to more value-added marketing activities to churn more ROI. Emphasize Cost-Effectiveness Most of the businesses associated with healthcare are concerned about the costs involved. With AI, they now develop policies to spend less on non-essential activities and necessitate profit-oriented actions. "We believe in the potential of AI to deliver insights and operational efficiencies that unlock better health-care performance." Robert Musslewhite, CEO at OptumInsight Frequently Asked Questions How is AI used in healthcare? AI in healthcare automates and predicts processes by analyzing data throughout. It is used to predict potential customers, improve business management workflows, and manufacture medical products. How does AI drive growth in the healthcare industry? AI drives business growth by improving the ability to understand better day-to-day customer patterns and needs based on services and products. How is AI changing the Healthcare industry? AI applications in healthcare have demonstrated their potential to improve analytics and data management and assist service providers in making timely medical decisions.

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AI

How AI is levelling the playing field when it comes to gender and healthcare

Article | December 21, 2021

Global efforts to tackle gender inequality have grown in recent years. But there is still so much to be done. Figures from the United Nations show that outcomes for women and girls continue to lag across a range of issues, including poverty, education, work and health. And according to the World Economic Forum, at the current rate, it will take 108 years to close the gender gap. Although healthcare is founded in objectivity and science, gender bias is still remarkably common. We wanted to understand more about female perceptions of healthcare, so we undertook consumer research that delved into the experiences of women compared to men. The results pointed to a clear disparity, finding that women are less likely to visit the doctor when they have symptoms of ill health and, in some cases, are taken less seriously when they do seek medical advice. Women being left behind According to our research, a significant proportion of British women feel disappointed in the healthcare they receive, with one in five reporting they weren’t taken seriously when presenting symptoms to a healthcare provider. What’s more, a staggering one in four said they are reluctant to seek medical advice at all for fear of wasting a GP’s time. These statistics suggest that, not only are female experiences of healthcare damaging their relationship with clinicians, but they could be eroding confidence in recognising and acting on warning signs and symptoms too. This sentiment is particularly evident when focusing on cardiac care. One in eight women (13%) feel ignored when presenting symptoms of heart disease to healthcare professionals, compared to just 4% of men. And of UK adults who have received a coronary heart disease (CHD) diagnosis, women experiencing symptoms were 55% more likely than men to visit the doctor multiple times before receiving a referral for further investigation. On top of this, women are five times more likely to receive a false finding from the cardiac stress tests that are traditionally used to assess heart health. “There does appear to be a gender bias in onward referral to secondary care and for diagnostics in the local area, which is influenced by the attending healthcare professionals’ risk assessment. Traditional teaching has led to gender bias, as we are programmed to attribute a lower level of pre-test probability and risk to females. This may have contributed to a general lack of awareness around cardiovascular health in women. For example, in a survey I carried out among more than 600 female employees working within North West Anglia NHS Foundation Trust, 82% said they didn’t feel informed about their cardiovascular health. Considering participants included some of the most medically informed women in the UK, the results speak volumes about how we view cardiac health among women.” - Dr Rebecca Schofield, consultant cardiologist at North West Anglia NHS Foundation Trust These widespread misconceptions around heart disease and heart attacks are often exacerbated by what we see in the media – think of the countless TV stereotypes of male characters clutching their chests and falling to the floor. But given that CHD is responsible for one in 13 female deaths, it appears that public health efforts have failed to make people aware of the risks for women. It is, perhaps, not surprising then that 42% of women with CHD did not immediately recognise their symptoms as signs of heart disease. In short, women are missing out on time-critical diagnoses and treatment due to a lack of awareness and education among both healthcare providers and the public. Technologies making a difference Thankfully, progress is being made to improve healthcare outcomes for women. Innovative technologies are increasingly providing diagnostic solutions that can reduce incidences of human bias and give clinicians greater clarity on the presence or severity of different conditions in their female patients. For example, AI is already being used to detect diseases such as cancer more accurately. Its adoption is facilitating reviews and translations of mammograms 30 times faster, with 99% accuracy, reducing the need for unnecessary biopsies. There’s extraordinary potential for AI and healthcare, and it’s something the NHS continues to recognise, most recently with the launch of its Artificial Intelligence Laboratory (AI Lab) and NHS England’s (NHSE) MedTech Funding Mandate. The latter aims to accelerate the uptake of selected innovative medical devices, diagnostics, and digital products to patients. As part of the NHS efforts, NHSE has mandated the HeartFlow Analysis for use in hospitals across England for patients, male or female, who might otherwise be sent for a cardiac stress test. The HeartFlow Analysis is a gender-neutral technology that takes data from a coronary CT scan of the heart and leverages deep learning (a form of AI) and highly trained analysts to create a personalised, digital 3D model of each patient’s coronary arteries. This then helps clinicians to quickly diagnose CHD and decide the appropriate treatment for patients of any gender. Time spent in hospital is minimised for patients and often layered testing and unnecessary invasive diagnostic procedures can be avoided. Final thoughts While AI is helping us tackle gender bias in certain areas such as oncologic and cardiac testing, healthcare professionals are not absolved of responsibility when it comes to confronting this problem. It remains incumbent upon clinicians to recognise unconscious bias that would deter them from referring women or minority patients for much-needed testing. Outside of the hospital, public health education efforts must expand so that far more of us can recognise shortness of breath, nausea, vomiting, back or jaw pain, and other symptoms beyond chest pain to be indicators of a heart attack in a woman. Knowing what to look for and overcoming personal bias that might lead to these signs being disregarded, may allow us to help one of the more than 100 women who will experience a heart attack in the UK today.

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Health Technology, Digital Healthcare

Exploring EHR Trends: A Path to Healthcare Transformation

Article | August 16, 2023

Unlock EHR interoperability solutions with this article. Discover how healthcare overcomes EHR interoperability challenges to facilitate seamless information sharing for better clinical decisions. 1. Exploring Hurdles in EHR Interoperability 2. Addressing EHR Interoperability Challenges: Mapping Effective Paths 2.1 Upgrading from Outdated Legacy Systems 2.2 Managing Inconsistent Information Across Multiple Sources 2.3 Overcoming Organizational Resistance to Sharing Data 2.4 Balancing Security and Consent 2.5 Harmonizing Data Standards Across Diverse Software Systems 2.6 Optimizing Training Resources for EHR Interoperability 2.7 Strategizing Costs for Specialist-driven Interoperability Management 2.8 Navigating Budget Constraints in EHR Interoperability 2.9 Unifying Patient Identification Standards Across HIEs 2.10 Advancing Allergy Management to Enhance Patient Care 3. Embracing Interoperability for a Connected Healthcare Future 1.Exploring Hurdles in EHR Interoperability Despite significant efforts and investments in health information systems and technology, coupled with many years of widespread availability, the full benefits of electronic health records (EHRs) still need to be realized. The reality is that most physicians continue to rely on faxing and mailing patient records, just as they did a decade ago. Numerous government-certified EHR products are being used, each utilizing distinct clinical terminologies, technical specifications, and functional capabilities. These differences make it challenging to establish a unified standard interoperability format for data sharing. Interestingly, even EHR systems built on the same platform might not be interoperable, as they are frequently highly customized to an organization’s specific workflow and preferences. Given these circumstances, the article examines ten challenges and their corresponding EHR interoperability solutions to enhance patient care. 2.Addressing EHR Interoperability Challenges: Mapping Effective Paths The primary goal of healthcare interoperability is to enable seamless sharing of health-related information between healthcare providers and patients, aiding in clinical decision-making. Here are several challenges to accomplishing this aim, along with their corresponding interoperability solutions: 2. 1 Upgrading from Outdated Legacy Systems One of the significant challenges in achieving EHR interoperability is the need to transition from outdated legacy systems. Many healthcare facilities still rely on older, proprietary EHR systems that need more compatibility and standards to communicate seamlessly with modern, interconnected healthcare networks. These legacy systems often need more data exchange capabilities, leading to inefficiencies, data inconsistencies, and barriers to collaborative patient care. The intricate process of upgrading or replacing these systems while ensuring data integrity and continuity of care poses a considerable obstacle to achieving comprehensive EHR interoperability. Healthcare institutions need to implement a strategic and phased approach to address this challenge. This involves assessing the existing EHR, identifying interoperability gaps, and selecting modern healthcare interoperability solutions that adhere to industry standards, such as Fast Healthcare Interoperability Resources (HL7 FHIR) and open APIs. A well-defined migration plan should be developed, including data migration, new system integration, and staff training. Collaboration with EHR vendors, IT experts, and clinical stakeholders is crucial to ensuring a smooth transition. 2.2 Managing Inconsistent Information Across Multiple Sources As patients move through different healthcare settings and encounter various medical professionals, their health information becomes distributed across multiple sources, leading to discrepancies, duplications, and variations in data. This inconsistency can compromise patient safety, treatment accuracy, and healthcare quality. Furthermore, different institutions' varying data formats, coding systems, and documentation practices exacerbate the challenge of creating a unified and accurate patient record. A potential solution to this challenge involves developing and adopting standardized data exchange protocols. By implementing common data standards and practices, healthcare providers can ensure that patient information is accurately represented and uniformly understood across different systems. In addition, robust data validation processes and reconciliation algorithms can help identify and rectify inconsistencies during data integration. Moreover, creating a centralized patient identity management system that links various patient records to a single, accurate identity can significantly mitigate the issue of duplicated or mismatched information. 2.3 Overcoming Organizational Resistance to Sharing Data This EHR interoperability challenge pertains to the reluctance of healthcare institutions, clinics, and providers to readily exchange patient information and medical records due to concerns over data privacy, competitive advantage, and operational complexities. This resistance often leads to fragmented patient care, hindered medical research, and compromised clinical decision-making. Addressing this challenge necessitates the establishment of clear data-sharing protocols, robust privacy safeguards, and incentivized collaboration. By fostering a culture of trust, emphasizing the collective benefits of data exchange, and implementing interoperability standards, the healthcare ecosystem can encourage reluctant organizations to actively share essential patient data, ultimately leading to improved patient outcomes and more efficient healthcare delivery. 2.4 Balancing Security and Consent This challenge in EHR interoperability revolves around the delicate equilibrium between ensuring patient data security and privacy while enabling the seamless sharing of EHRs across different healthcare systems. Striking the right balance involves addressing concerns about unauthorized access, data breaches, and patient consent preferences. While robust security measures are necessary to safeguard sensitive health information, overly stringent restrictions can hinder the efficient exchange of vital medical data, potentially impeding timely and informed patient care, medical research, and healthcare system efficiency. Potential EHR interoperability solutions to this challenge include implementing a layered security and consent management approach. This involves combining strong encryption, authentication protocols, and access controls to ensure the integrity and confidentiality of EHRs. Moreover, the adoption of standardized and granular consent mechanisms empowers patients to regulate both access to their data and the purposes for which it can be accessed. An integrated framework that employs advanced technologies like blockchain for secure audit trails and data-sharing logs can enhance transparency and accountability. Furthermore, patient education and awareness campaigns can empower individuals to make informed data-sharing decisions, fostering a collaborative environment where security, consent, and interoperability coexist harmoniously. 2.5 Harmonizing Data Standards Across Diverse Software Systems This challenge encompasses integrating and exchanging medical data across various software platforms and applications used within the healthcare industry. To tackle this challenge, a comprehensive solution includes the widespread adoption and adherence to standardized data formats, coding conventions, and communication protocols by developers, healthcare organizations, and EHR integration software. To address this challenge, a comprehensive solution involves the establishment of standardized data formats, coding conventions, and communication protocols widely adopted and adhered to by EHR software developers and healthcare organizations. This could be achieved through industry collaboration, government regulations, and incentives for adopting interoperability standards. Additionally, implementing APIs that translate and map data between different formats can help bridge the gap between diverse software systems. 2.6 Optimizing Training Resources for EHR Interoperability This hurdle involves preparing healthcare professionals, IT staff, and other stakeholders to effectively navigate and implement interoperable EHR systems. Ensuring that healthcare personnel possess the necessary skills and knowledge to seamlessly integrate, maintain, and utilize interconnected EHR systems amidst rapidly evolving technology and standards poses a significant hurdle. This challenge involves understanding the intricacies of interoperability protocols and grasping the broader context of data security, patient privacy, and efficient data exchange among diverse healthcare entities. To address this challenge, developing comprehensive and up-to-date training programs that cover both technical aspects (interoperability standards, APIs, and data formats) and practical considerations (security protocols, data governance) is crucial. Collaborations with vendors, industry experts, and academia can ensure the training content remains aligned and updated with current EHR trends. Integrating EHR interoperability education into medical and IT curricula can also lay a foundation for future professionals. Continuous learning opportunities, including EHR analytics courses, certifications, and knowledge-sharing platforms, can further bolster the continual development of skills and knowledge exchange. This process cultivates a skilled workforce capable of fully leveraging EHR interoperability while upholding the integrity and privacy of patient data. 2.7 Strategizing Costs for Specialist-driven Interoperability Management This challenge pertains to the complex and costly task of ensuring seamless data exchange among diverse EHR systems, mainly when managed by specialists with domain-specific knowledge. These specialists play a crucial role in tailoring EHR interoperability solutions to the unique needs of their medical domains. Still, the financial implications of such endeavors can be substantial, involving customization, integration, and maintenance expenses. Finding an effective solution requires a multi-faceted approach involving standardized interoperability frameworks, modular system design, strategic resource allocation, and collaborative partnerships among EHR vendors, healthcare institutions, and specialists. By optimizing the balance between customization and standardization and leveraging technological advances like APIs and cloud computing, healthcare ecosystems can mitigate costs while achieving efficient and secure data exchange that benefits patients and healthcare providers. 2.8 Navigating Budget Constraints in EHR Interoperability This issue relates to healthcare organizations' significant financial limitations when striving to establish seamless EHR data exchange across disparate systems. As healthcare entities aim to enhance patient care coordination and data accessibility, the cost of implementing and maintaining interoperable EHR systems becomes a substantial hurdle. This challenge necessitates a delicate balance between allocating resources for EHR integration, customization, and ongoing maintenance while ensuring that patient data remains secure and accessible to authorized stakeholders. A possible avenue to deal with the budget constraints in EHR interoperability is the strategic adoption of open-source frameworks. By leveraging open-source solutions, healthcare organizations can reduce licensing fees and development costs associated with proprietary systems, allowing them to allocate resources more efficiently. Additionally, collaborating with industry consortia and governmental initiatives that promote standardized data exchange protocols can foster economies of scale, streamlining the implementation process. Moreover, investing in cloud-based technologies can offer scalable and cost-effective data storage and sharing infrastructure. 2.9 Unifying Patient Identification Standards Across HIEs The crux of this issue involves the need for consistent patient identification methods across different healthcare systems and data-sharing networks. This inconsistency results in errors, data duplication, and compromised patient safety as information is exchanged between entities. Without a standardized patient identification system, accurate matching of patient records becomes a complex endeavor, hindering the seamless exchange of EHRs and undermining the potential benefits of interoperability. To address this challenge, a comprehensive solution involves establishing and adopting a universally recognized patient identification standard that spans all participating HIEs. This standard could include using unique patient identifiers or a combination of demographic, biometric, and cryptographic identifiers to ensure accurate and secure patient matching. Additionally, implementing advanced data governance practices, strong privacy protections, and robust data validation algorithms would enhance the accuracy and security of patient identification. Collaboration between healthcare organizations, government agencies, and technology experts is crucial to developing and implementing this standardized approach, fostering a more interconnected and effective healthcare ecosystem while safeguarding patient privacy and data integrity. 2.10 Advancing Allergy Management to Enhance Patient Care Healthcare providers need help seamlessly sharing allergy-related patient data across different EHR platforms, hindering comprehensive patient care. This lack of interoperability leads to fragmented information, potential medication errors, and compromised treatment decisions, ultimately impacting patient safety and outcomes. One viable solution for addressing this challenge is to establish standardized data exchange protocols alongside a unified health information exchange framework. Implementing FHIR standards can enable the consistent and secure sharing of allergy information among EHR systems. Additionally, incentivizing healthcare organizations to adopt these interoperability EHR standards and invest in compatible technologies will promote a cohesive ecosystem where allergy data can be accurately and swiftly exchanged. Collaborative efforts among EHR vendors, healthcare providers, and regulatory bodies are essential to ensure the seamless flow of allergy-related information, resulting in enhanced patient care, reduced medical errors, and improved healthcare efficiency. 3.Embracing Interoperability for a Connected Healthcare Future With the goal of a cohesive healthcare future in mind, the value of embracing interoperability is immeasurable. This article highlights the essential role of interoperability in overcoming the challenges posed by fragmented data and improving patient outcomes. As healthcare systems continue to develop, the smooth exchange of EHRs becomes crucial, fostering collaboration among diverse stakeholders and facilitating well-informed decision-making. By creating an environment in which EHRs can seamlessly communicate, healthcare providers have the potential to offer more comprehensive, patient-centered care, minimize duplication, and expedite both diagnoses and treatments. Although achieving an interoperable healthcare ecosystem may involve complexity, the benefits of efficiency, precision, and overall quality of care underscore its necessity as a transformative journey.

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Health Technology, Digital Healthcare

Unlocking the Puzzle: Navigating EHR Interoperability Solutions

Article | September 7, 2023

Embark on a journey into the frontier of healthcare innovation in this article. Discover how EHR telemedicine and remote patient monitoring serve as catalysts, driving forward a new era in healthcare. Contents 1. Integration of EHRs in Telemedicine and Remote Patient Monitoring 2. Technical Challenges and Solutions in EHR Integration 3. Financial Analysis: Cost-Benefit Assessment of Integration 4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems 5. Forging Stronger Patient-Clinician Relationships 1. Integration of EHRs in Telemedicine and Remote Patient Monitoring EHR telemedicine and remote patient monitoring have reshaped healthcare delivery by seamlessly integrating electronic health records, allowing healthcare providers and patients to exchange information effortlessly, regardless of geographical barriers. This synergy empowers healthcare professionals to access patients' comprehensive medical histories in real time, facilitating more informed decision-making during virtual consultations. During the spring of 2020, when pandemic restrictions kept most people in the US at home, the use of telehealth rose to about 51%. [Source: Elation Health] Moreover, it enhances the accuracy of remote patient monitoring by providing up-to-date data, enabling timely interventions and improving overall healthcare outcomes. Integrating EHR telemedicine systems enhances efficiency and ensures that patient care remains at the forefront of modern healthcare, transcending traditional physical boundaries. 2. Technical Challenges and Solutions in EHR Integration Navigating telehealth EHR integration and remote patient monitoring solutions uncovers a range of technical challenges, each with its own set of potential remedies. These include interoperability issues, which can be mitigated by adopting standardized data formats like HL7 FHIR. EHR interoperability solutions may involve using data exchange protocols such as HL7's Consolidated Clinical Document Architecture (C-CDA) or developing custom APIs to facilitate seamless data exchange between EHRs and telemedicine platforms. Additionally, the imperative need for data security and privacy is achieved through robust encryption and adherence to regulations like HIPAA or GDPR. Data integration challenges arising from varying EHR data storage methods can be resolved using middleware or integration platforms. Investing in telecom infrastructure and developing offline-capable telemedicine apps can address limited connectivity in remote areas. Ensuring real-time data access involves optimizing EHR databases and creating low-latency systems. Other challenges encompass integrating data from medical devices, ensuring data accuracy, scalability, user-friendly interfaces, regulatory compliance, and cost management strategies. 3. Financial Analysis: Cost-Benefit Assessment of Integration When contemplating the integration of EHR telemedicine and remote patient monitoring systems, conducting a comprehensive cost-benefit analysis is crucial. This assessment covers financial aspects, including initial implementation costs (software development, hardware upgrades, training, and data migration), ongoing operational expenses (maintenance and data storage), and potential efficiency gains (streamlined workflows and improved data accessibility). It also evaluates the impact on patient outcomes, satisfaction, and financial benefits of enhanced healthcare quality, reduced readmissions, and increased patient engagement. Healthcare organizations can estimate cost savings in remote patient monitoring and explore expanding telemedicine services to underserved populations to make informed financial decisions. Additionally, this analysis considers long-term financial viability and alignment with organizational goals, including regulatory compliance costs, risk assessment, scalability considerations, and the competitive advantages of integrated telemedicine services. By calculating ROI and assessing potential risks, healthcare entities can develop risk mitigation strategies, ensuring that EHR integration in telemedicine and remote patient monitoring enhances healthcare delivery and aligns with the organization's financial sustainability and long-term success. 4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems Data privacy and obtaining informed consent are paramount in integrated EHR and telemedicine systems. Patients should provide explicit consent, understanding the data collected and its intended use, with strict encryption protocols safeguarding data during transmission. Access controls and data minimization practices restrict unauthorized access, while patient portals enable individuals to manage their data-sharing preferences and revoke consent if needed. Compliance with regulations such as HIPAA or GDPR is crucial, as is maintaining comprehensive audit trails to track data access. Training, awareness, and robust incident response plans fortify data privacy efforts, fostering trust and transparency in these integrated systems where healthcare organizations and patients share responsibility for secure data handling. 5. Forging Stronger Patient-Clinician Relationships Integrating EHR telemedicine and remote monitoring systems goes beyond mere efficiency and accessibility objectives. It serves as a catalyst for nurturing more substantial and meaningful patient-clinician relationships. This fusion of technology and healthcare has the capacity to bridge physical distances, allowing clinicians to truly understand and engage with their patients on a deeper level. Patients, armed with increased access to their health data, become more active participants in their healthcare, while clinicians, with their comprehensive information, can offer more personalized and informed guidance. The potential of EHR telemedicine reaches far beyond the digital screen; it empowers both patients and clinicians to collaborate in pursuit of improved health outcomes, ushering in a new era of patient-centric care grounded in trust, communication, and shared knowledge.

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Health Technology, Digital Healthcare

Top 10 Best Practices for EHR Ethical Issues

Article | August 21, 2023

It’s no secret now that healthcare is an in-demand field. Today, business leaders need modern and intelligent decision-making solutions for their customers and clients. They must also focus on the right investment areas and learn the tricks for investing, spending, and setting goals for revenue generation to accelerate business. With continuous developments in the healthcare sector, integrating AI into processes can help increase ROI. Therefore, if you, like any other business leader, are looking for solutions to empower your services and products in the healthcare domain, this article will help you through AI’s ultimate use cases and churn out a higher ROI. What’s with AI in Healthcare at Present? AI’s role in healthcare is evolving and enhancing traditional business operations, particularly marketing. According to a study by IBM, 71% of customers expect real-time communication. Thus, global demand is fueling the rising adoption of AI marketing solutions. The effects of AI in healthcare are evident. Gartner reports increased marketing efficiency and effectiveness (86%), improved decision-making (71%), better data analysis and new insights (79%). Global AI spending will rise from $450 million in 2019 to over $28 billion by 2024 is not surprising. Similar and further studies are ongoing on various use cases of AI in healthcare at scale. What are the efficient use cases of AI that will help healthcare businesses boost their ROI? Let’s find out. How is AI Applied in Healthcare? The promising applications of AI in healthcare to improve outcomes are very intriguing. While there is still much to achieve in the AI-dependent healthcare business, there is sufficient potential that tech companies are willing to invest in AI-powered tools and solutions. Let’s examine the potential examples of AI in healthcare to prepare and support business strategies accordingly and foster higher ROI generation. Predictive Analytics AI-based predictive analytics impacts a business by automating administrative tasks, predicting sales outcomes for a year, customers’ behavior and making strategies accordingly. According to a Forbes study, AI-based predictive analytics can save businesses $18 billion in tasks, expenses, and pricing. To understand this, one example of using AI to automate admin tasks is a collaboration between the Cleveland Clinic and IBM. Cleveland Clinic uses IBM’s Watson to mine big data and provides personalized services for customers and clients on marketing deeds. Some of the practical applications of AI and predictive analytics in healthcare are: Monitoring market trends to maximize marketing efforts Organizing datasets Creating marketing campaigns tailored to each demographic-based client Mining collective data for future decision-making Fraud Prevention AmerisourceBergen Corp detects fraud and misleading business operations through AI. A sales account team conducts audits with AI to detect usual lea and queries to prevent hefty expenses for businesses. The example explains that implementing AI in your process will help detect any significant fraud attempts inside your business operation. This will help your business save huge expenditures. Boost Sales By putting down false leads, AI helps in maximizing sales numbers, resulting in significant ROI generation. For example, AI transforms data into personalized data, which reduces the cost of operations. Chatbots Most healthcare businesses leverage chatbots on their websites to engage more and more customers and boost engagement. In this way, businesses tend to gain multiple leads and convert them into clients by providing the best marketing solutions. Chatbots are fruitful for AI start-ups in healthcare—small businesses can deploy AI to their websites. By doing so, they can save millions in administrative costs and attract numerous leads. The most prominent examples of AI in healthcare hail from giant tech titans such as IBM, Amazon, and Microsoft. They are assisting healthcare providers with AI to create and deploy digital-human employees. Segmentation of Marketing Targets Is your target audience not responding to your marketing campaigns (for example, by not clicking a link, subscribing or unsubscribing to a newsletter, or not registering for a medical event)? If that's the case, how should you go ahead? Using AI-based tools allows your marketing to easily identify target behaviors and reactions based on the type of marketing actions to be carried out. Analyzing these actions can help segment targets based on your company's marketing objectives. The most significant development took place in April 2022, when Amazon Alexa became fully HIPAA compliant. It works with health developers and service providers that manage protected information for customers. AI Leads to Data Modernization It’s all about the data—not any data! There’s a precise association between AI and data management, resulting in data modernization. According to a Cognizant research study, healthcare leaders have made significant progress in modernizing their data. In contrast, most upcoming businesses are expecting to do so by 2024. The maximum acceleration of AI in modernizing data will be seen in the manufacturing and marketing of healthcare products and services, respectively. It is because AI helps to churn data easily. The accessibility of data, in particular, becomes simpler with automation than doing it manually, which generates a massive amount of data. Such effects of AI in healthcare can be one of the prime reasons for the higher ROI of your business in the future. “There has never been a greater need for skilled analytic talent in health care. Because AI is becoming more strategic, organizations must ensure access to this skill set, either by growing their analytic teams or seeking out experienced partners." Steve Griffiths, CEO of Optum Enterprise Analytics AI Expenditure is on the Rise McKinsey says that by 2025, the use of AI in healthcare will be widespread, resulting in significant expenditure by global healthcare leaders. AI is a significant concern for healthcare decision-makers, investors, and innovators as customers extensively engage and react to AI-powered services and solutions. AI is constantly bringing improvements to almost all processes, including cost savings, management of services and products, and monitoring of multiple operations. Even small businesses in the healthcare industry are proactively investing in AI applications to match steps with the current wave of innovation in healthcare services. Accelerate ROI Using AI AI in healthcare is becoming one of the prime responsible technologies for accelerating ROI. Technology can eradicate multiple business growth challenges. Let’s find out how. Enhanced Performance As previously stated, use cases of AI in healthcare can relieve stress on employees. This would allow them to devote their time to more value-added marketing activities to churn more ROI. Emphasize Cost-Effectiveness Most of the businesses associated with healthcare are concerned about the costs involved. With AI, they now develop policies to spend less on non-essential activities and necessitate profit-oriented actions. "We believe in the potential of AI to deliver insights and operational efficiencies that unlock better health-care performance." Robert Musslewhite, CEO at OptumInsight Frequently Asked Questions How is AI used in healthcare? AI in healthcare automates and predicts processes by analyzing data throughout. It is used to predict potential customers, improve business management workflows, and manufacture medical products. How does AI drive growth in the healthcare industry? AI drives business growth by improving the ability to understand better day-to-day customer patterns and needs based on services and products. How is AI changing the Healthcare industry? AI applications in healthcare have demonstrated their potential to improve analytics and data management and assist service providers in making timely medical decisions.

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AI

How AI is levelling the playing field when it comes to gender and healthcare

Article | December 21, 2021

Global efforts to tackle gender inequality have grown in recent years. But there is still so much to be done. Figures from the United Nations show that outcomes for women and girls continue to lag across a range of issues, including poverty, education, work and health. And according to the World Economic Forum, at the current rate, it will take 108 years to close the gender gap. Although healthcare is founded in objectivity and science, gender bias is still remarkably common. We wanted to understand more about female perceptions of healthcare, so we undertook consumer research that delved into the experiences of women compared to men. The results pointed to a clear disparity, finding that women are less likely to visit the doctor when they have symptoms of ill health and, in some cases, are taken less seriously when they do seek medical advice. Women being left behind According to our research, a significant proportion of British women feel disappointed in the healthcare they receive, with one in five reporting they weren’t taken seriously when presenting symptoms to a healthcare provider. What’s more, a staggering one in four said they are reluctant to seek medical advice at all for fear of wasting a GP’s time. These statistics suggest that, not only are female experiences of healthcare damaging their relationship with clinicians, but they could be eroding confidence in recognising and acting on warning signs and symptoms too. This sentiment is particularly evident when focusing on cardiac care. One in eight women (13%) feel ignored when presenting symptoms of heart disease to healthcare professionals, compared to just 4% of men. And of UK adults who have received a coronary heart disease (CHD) diagnosis, women experiencing symptoms were 55% more likely than men to visit the doctor multiple times before receiving a referral for further investigation. On top of this, women are five times more likely to receive a false finding from the cardiac stress tests that are traditionally used to assess heart health. “There does appear to be a gender bias in onward referral to secondary care and for diagnostics in the local area, which is influenced by the attending healthcare professionals’ risk assessment. Traditional teaching has led to gender bias, as we are programmed to attribute a lower level of pre-test probability and risk to females. This may have contributed to a general lack of awareness around cardiovascular health in women. For example, in a survey I carried out among more than 600 female employees working within North West Anglia NHS Foundation Trust, 82% said they didn’t feel informed about their cardiovascular health. Considering participants included some of the most medically informed women in the UK, the results speak volumes about how we view cardiac health among women.” - Dr Rebecca Schofield, consultant cardiologist at North West Anglia NHS Foundation Trust These widespread misconceptions around heart disease and heart attacks are often exacerbated by what we see in the media – think of the countless TV stereotypes of male characters clutching their chests and falling to the floor. But given that CHD is responsible for one in 13 female deaths, it appears that public health efforts have failed to make people aware of the risks for women. It is, perhaps, not surprising then that 42% of women with CHD did not immediately recognise their symptoms as signs of heart disease. In short, women are missing out on time-critical diagnoses and treatment due to a lack of awareness and education among both healthcare providers and the public. Technologies making a difference Thankfully, progress is being made to improve healthcare outcomes for women. Innovative technologies are increasingly providing diagnostic solutions that can reduce incidences of human bias and give clinicians greater clarity on the presence or severity of different conditions in their female patients. For example, AI is already being used to detect diseases such as cancer more accurately. Its adoption is facilitating reviews and translations of mammograms 30 times faster, with 99% accuracy, reducing the need for unnecessary biopsies. There’s extraordinary potential for AI and healthcare, and it’s something the NHS continues to recognise, most recently with the launch of its Artificial Intelligence Laboratory (AI Lab) and NHS England’s (NHSE) MedTech Funding Mandate. The latter aims to accelerate the uptake of selected innovative medical devices, diagnostics, and digital products to patients. As part of the NHS efforts, NHSE has mandated the HeartFlow Analysis for use in hospitals across England for patients, male or female, who might otherwise be sent for a cardiac stress test. The HeartFlow Analysis is a gender-neutral technology that takes data from a coronary CT scan of the heart and leverages deep learning (a form of AI) and highly trained analysts to create a personalised, digital 3D model of each patient’s coronary arteries. This then helps clinicians to quickly diagnose CHD and decide the appropriate treatment for patients of any gender. Time spent in hospital is minimised for patients and often layered testing and unnecessary invasive diagnostic procedures can be avoided. Final thoughts While AI is helping us tackle gender bias in certain areas such as oncologic and cardiac testing, healthcare professionals are not absolved of responsibility when it comes to confronting this problem. It remains incumbent upon clinicians to recognise unconscious bias that would deter them from referring women or minority patients for much-needed testing. Outside of the hospital, public health education efforts must expand so that far more of us can recognise shortness of breath, nausea, vomiting, back or jaw pain, and other symptoms beyond chest pain to be indicators of a heart attack in a woman. Knowing what to look for and overcoming personal bias that might lead to these signs being disregarded, may allow us to help one of the more than 100 women who will experience a heart attack in the UK today.

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