Welcome to this three-part series examining healthcare interoperability in the United States and the reasons for its slow progress. In Part 1, we delve into the fragmented healthcare landscape and the technical challenges that impede seamless data exchange.
The Fragmented Healthcare Landscape
The need for interoperability in healthcare can be attributed to the fragmented landscape of the industry. In the United States, the healthcare ecosystem comprises a wide range of organizations, including hospitals, clinics, and specialty practices. Each operates with different electronic health record (EHR) systems, infrastructure, and data models, creating significant interoperability gaps.
The fragmented landscape hinders the seamless exchange of patient data and poses a barrier to efficient care delivery.
Technical Challenges
Technical challenges play a crucial role in impeding healthcare interoperability. One of the major obstacles is the diversity of data formats and standards used by different healthcare systems and EHR vendors. Historically, proprietary data formats were implemented by vendors, resulting in compatibility issues and difficulties in exchanging information between systems.
We have made strides in improving interoperability but are not where we need to be. It’s a complex issue that requires collaborative efforts among stakeholders to overcome technical and policy challenges. These technical challenges include data standardization, data exchange protocols, and semantic interoperability.
Legacy systems also contribute to the lack of interoperability. Many healthcare organizations still rely on outdated systems not designed with interoperability in mind. These legacy systems may need more capabilities or face compatibility issues with newer technologies, making sharing and exchanging patient data challenging.
The impact of legacy systems on interoperability remains a significant hurdle in healthcare. The lack of standardized data exchange and the presence of proprietary systems hinder the seamless flow of information, impacting care coordination and patient outcomes.
Electronic health records (EHRs) have revolutionized the healthcare industry, streamlining patient care, improving efficiency, and enhancing communication among healthcare providers. Here, we explore the history of EHRs, highlighting the obstacles overcome throughout their development and implementation, as well as the challenges that persist for this transformative technology.
The Emergence of Electronic Health Records
The concept of electronic health records began to emerge in the 1960s with early attempts to digitize medical records. However, it wasn’t until much later in the century that technological advancements paved the way for the modern EHR systems. Standardization of medical terminologies and coding systems played, and continue to play, a crucial role in enabling data exchange and interoperability.
Overcoming Obstacles
Technical challenges
One of the initial obstacles in developing EHRs was the lack of interoperability among various systems. Different healthcare organizations and providers used numerous technologies and formats from various vendors, making it difficult to share and exchange patient information. In some cases, this is still the case.
Efforts have been made to standardize medical terminologies and coding systems, such as the International Classification of Diseases (ICD-10) and the Systematized Nomenclature of Medicine (SNOMED), to facilitate and streamline data exchange. For those new to medical coding, there are many “ICD 10 coding explained” resources available online. These resources are invaluable in understanding how these systems work. However, additional efforts are still required to achieve full interoperability.
Privacy and security concerns
The digital nature of EHRs raised concerns about patient privacy and data security. Protecting sensitive health information and ensuring data integrity became critical challenges. Legislation, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, was introduced to address these concerns and impose strict privacy and security regulations.
Robust encryption methods, access controls, and audit trails were implemented to safeguard patient data, among other protocols.
By Baraka Floyd, MD, pediatrics physician at Stanford Medicine Children’s Health and Clinical Assistant Professor at Stanford School of Medicine and Lindsay Stevens, MD, pediatrics physician at Stanford Medicine Children’s Health and Clinical Professor at Stanford School of Medicine
The emerging focus on social factors that influence health outcomes has necessarily transformed the approach clinicians take in patient care. These factors, otherwise known as “social determinants of health (SDOH),” include food insecurity, housing and utility instability, transportation access, and personal safety, among others. These have a tremendous impact on long-term health outcomes and quality of life.
When problems arise in these areas, patients and families struggle to focus on medical needs. As a clinician, identifying and keeping track of these social determinants of health help create the opportunity for patient-focused solutions. This could be the difference between a food insecure family gaining access to resources and that same family not being aided appropriately.
Food insecurity as an example of a social determinant of health
More common among families with children, food insecurity impacts more than 34 million people, including nine million children in the United States. It is a lack of consistent access to food for every person in a household to live a healthy life. Influenced by several factors that includes income, employment, race, ethnicity, and disability status, food insecurity has been exacerbated by the COVID-19 pandemic.
As the pandemic left many households with limited food access and financial opportunities, numerous families were forced to rely on food assistance programs for the first time. As even more families faced a lack of access to sufficient food and nutrition, health care providers had to ensure that they addressed food insecurity as a key determinant that impacts their patients’ long-term health.
In a world where our online privacy is constantly threatened, many individuals are now opting for VPNs as an effective solution. However, what exactly is a VPN, and why has it become so important? A Virtual Private Network (VPN) is a technology that creates an encrypted connection between your device or computer and the server you want to access via the Internet. This secure link ensures that all data traveling through this channel – including browsing history, private information, and online activity — remains confidential and immune to external threats.
VPNs have gained massive popularity due to their ability to circumvent geo-restrictions set by governments or streaming services. They also allow users to access blocked content on particular websites or social media platforms while protecting businesses, remote workers, and individuals’ privacy. In today’s digital era, where cyber-attacks are rampant across cyberspace corners, comprehending how VPNs can ensure maximum protection has never been more crucial. Therefore, in this piece, we will delve deeper into these three advantages of utilizing VPNs: improving online security measures, dodging geo-blocking restrictions, and unrestricted internet access to restricted data blocks.
What Is a VPN?
In these times of remote work and online transactions, safeguarding sensitive data from cyber-attacks is crucial. This is where a VPN (Virtual Private Network) comes in handy, allowing users to access private networks securely via public internet connections. VPN for iOS, VPN for Androids, and VPN for Windows are necessary.
The technology behind VPNs entails encrypting traffic between devices and servers, making it challenging for unauthorized persons to intercept or decode information transmitted. Its effectiveness has made this tool increasingly popular among businesses with remote workers and individuals seeking content in regions with restricted access. Aside from providing an extra layer of security against cyber threats by masking IP addresses, Virtual Private Networks offer uninterrupted connectivity across different geographic locations while maintaining anonymity.
However, like any other technological innovation, there are drawbacks when using a VPN; the primary one is slower connection speed due to encryption processes during transmission. Overall, VPNs remain a critical asset for ensuring online privacy while enjoying unrestricted access without fear of hacking attempts or potential data breaches – something everyone who values their digital security should consider employing.
By Alexandra Weiss, nonprofit strategy, patient organizations, IQVIA.
The past few years ushered in a historic wave of innovation in the therapeutic development landscape, heightening the demand for high-quality, real-world data that captures insights from across the entire patient journey. Advancements in government regulations and data technologies are dramatically improving access to electronic health information, presenting unique opportunities for patient advocacy organizations to serve as trusted data stewards.
As regulations, technology and vendor offerings continue to evolve, patient data strategies and data technology capabilities will equip patient advocacy organizations with the ability to advance research through the lens of real-world patient-centric data. In harnessing the full value of advanced data registries to collect, analyze and share information, patient advocacy organizations can better engage with patients and their communities. These tools also present an unprecedented opportunity for patient organizations to collaborate with the research and life sciences community to drive research advancements using patient-centered data that places patient needs and priorities front and center.
Navigating Changes in the Clinical Research Landscape
When discussing the value of research-grade data registries, it is critical to first consider the ways in which the clinical research industry has evolved. Landmark regulatory advancements such as the 21st Century Cures Act and draft FDA guidance on real-world evidence have accelerated the ability to access interoperable health data.
The Cures Act empowers patients with ownership of their own health data, which they can share with advocacy organizations. This provides patient advocacy organizations with access to insights for a broader scope of patients, disease experiences and treatment journeys, equipping them to play a more active role in regulatory decision making and the advancement of drug approval.
In addition to regulatory advancements, the technology and vendor ecosystems have vastly expanded. Data exchange standards such as Health Level 7, Fast Healthcare Interoperability Resources and data models such as Observations Medical Outcomes Partnership are simplifying analytic product delivery by enabling growth in the automation of data capture and data processing. There is also a broader range of commercial partnership opportunities available to help expand patient engagement and generate evidence related to care delivery and treatment responses.
Finally, patient centricity is transitioning from a general aspiration to an immediate operational priority. As patients become increasingly involved in the clinical research and care process, patient advocacy organizations are better equipped in their roles as trusted data stewards.
These advancements present new opportunities for patient organizations to build a cohesive data strategy and expand registry capabilities to capture a 360-degree view of patient perspectives while supporting patient centricity, data ownership, privacy and security.
With the right patient data registry, patient advocacy organizations can accomplish the following:
Address research gaps and accelerate clinical developments.
Equip patients with the information they need to advocate for themselves in their care
Improve provider understanding of patient treatment needs.
Help investigators ask more relevant questions throughout the clinical research process.
Cultivate more strategic partnerships with other stakeholders in the biomedical research, medical specialty society and therapeutic development spaces.
Facilitate more effective long-term follow-up initiatives to drive positive care outcomes.
By Wonil Gregg, vice president of customer engagement and experience, DCPerform.
Whether you’re a customer or a worker, chances are you’re all too familiar with the persistent challenges of stockouts and overstocks across the healthcare industry. Stockouts can lead to missed opportunities or incomplete treatments, while overstocks cause unnecessary financial waste. The cost of unused drug waste in the healthcare industry costs $2.8 billion of medication per year.
Managing inventory in the healthcare industry requires striking the right balance between supply and demand and using great supply chain services. Fulfilling customers’ demands must balance maintaining an adequate yet cost-effective supply of goods and materials. Learn more about minimizing stockouts and overstocks to improve patient care and increase savings.
Mastering Forecasting and Inventory Analysis
Forecasting and inventory analysis have become essential tools for ensuring optimal stock levels in the ever-changing healthcare industry. Inventory forecasting uses data to drive decision-making. This is the application of information and logic to ensure you have enough products to meet customer demand without overstocking.
Analyze past trends and consumer behavior to accurately predict future demand
Consider complexities such as seasonality and trend forecasting
Ask questions to identify demand patterns, such as growth and spikes during specific seasons
Take into account planned marketing campaigns and other events that may impact sales
Utilize inventory management software for sophisticated demand forecasting
Prevent stockouts and backorders by optimizing inventory levels
Maximize profits by reducing excess inventory and minimizing storage costs
Continuously monitor and adjust inventory levels based on real-time data and insights
Constantly improve forecasting and inventory analysis processes for better accuracy and effectiveness.
Whether it is a small or large corporation, paying attention to these crucial metrics is key to staying competitive in today’s fast-paced market.
Harris Data Integrity Solutions, the leading provider of best-in-class patient data integrity services and software, has released a white paper that undertakes an in-depth examination of the healthcare industry’s chronic people matching problem. In addition to dissecting the challenges and impacts patient misidentification has on care safety, outcomes, and costs, People Matching in Healthcare: Challenges, Impact and Solutions explores efforts underway on multiple levels to identify a system-wide resolution to the problem.
Lora Hefton
“The tumultuous state of patient matching exposes patients to duplicative and unnecessary testing and services and care delays, exacerbates fraud risks, impacts public health emergency response, and costs the U.S. healthcare system over $6 billion annually in denied claims,” said Lora Hefton, executive vice president of Harris Data Integrity Solutions. “But the future is not as grim as the present might indicate, as our research also found that efforts to identify the right path forward are finding a foothold as stakeholders from across the healthcare continuum come together to remove obstacles and implement effective solutions.”
In People Matching in Healthcare: Challenges, Impact and Solutions, Harris Data Integrity Solutions’ patient identity experts highlight the efforts of Patient ID Now to eliminate legislative barriers hindering exploration of a unique patient identifier, and the collaborative’s work to establish the framework of a national strategy for effective patient identification and matching. They also look at the work undertaken by the Project US@ collaboration, spearheaded by the Office for the National Coordinator (ONC), that resulted in a technical specification for collection of patient addresses, and AHIMA for its related companion guide with operational guidance and best practices.
Hospitals routinely collect vast amounts of data, including information about patients’ health, care delivery, and organizational performance. This data could theoretically be utilized to drive huge improvements in health outcomes and operational efficiency.
Rather than this massive amount of health data being an advantage, it’s most often considered a burden as there are inconsistencies in documentation, aggregation methods, organization of, display of, and most importantly, how it’s used. There’s also a lack of resources required to effectively manage this overwhelming amount of information. The data gap not only leads to lost opportunities to improve healthcare but is a major contributor to some of healthcare’s current biggest issues like burnout and staffing. Hospitals are always striving to better leverage healthcare data. Revised processes that reduce manual inputs, eliminate redundancy, and include central EHR systems are a few goals that come to mind. However, some of the biggest wins will only be gained once we tap into more advanced tools that leverage artificial intelligence (AI). With so much medical information already available, including large complex data sets, sophisticated AI systems are just what the doctor ordered to get these data sets organized and in use. To unlock data, hospitals must incorporate more data science and use artificial intelligence (AI) methods to operationalize their learnings. Data science is an umbrella term for statistical techniques, design techniques, and development methods. It involves pre-processing analysis, prediction, and visualization, whereas AI is the implementation of a predictive model to foresee events. Advanced data science and AI can not only help organize all of this information, but also generate trends and insights so that hospitals can deliver more precise care, identify operational hazards, and create a more optimized approach for managing their current workload.
Many Sources of Information
The problem, as it stands, is too much information from disparate sources and in different formats. Hospital data comes from a variety of places, such as electronic health records, administrative systems, insurance providers, patient-submitted forms, local HR systems, hospital medical devices, and, remote monitoring services.
This information also comes in various forms, including structured digital data, physical documents, photographs, charts, and more. The volume and diversity of data make it difficult to store in conventional databases and format it for use across multiple frameworks.
By 
