Future of healthcare

Future of Healthcare

Article | January 10, 2022

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 Addressing EHR Interoperability Challenges: Mapping Effective Paths 2.1 Upgrading from Outdated Legacy Systems 2.2 Managing Inconsistent Information Across Multiple Sources 2.3 Overcoming Organizational Resistance to Sharing Data 2.4 Balancing Security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challenges to accomplishing this aim, along with their corresponding interoperability solutions: 2. 1 Upgrading from Outdated Legacy Systems One of the significant challenges in achieving EHR interoperability is the need to transition from outdated legacy systems.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.

Future of Healthcare

Article | December 27, 2021

Smoking has a lot of consequences to one’s health. It can lead to cancer, heart disease, and chronic obstructive pulmonary disease—all of which are chronic diseases. This is part of the reason why the Health and Human Services agency reports that 70% of adult smokers want to quit. As a medical provider, adults looking to stop smoking will come to you for advice and treatment. One alternative smoking product you might want to recommend is an e-cigarette, given their prevalence in recent years. In this article, let’s take a deeper look at whether e-cigarettes’ should be recommended for smoking cessation and what other treatment options to endorse to patients. Are e-cigarettes approved for smoking cessation? Electronic cigarettes, more commonly known as e-cigarettes, are devices that vaporize nicotine-based liquid to be inhaled by its user. It almost replicates the experience of smoking a cigarette due to the device’s shape and the vapor it produces. However, the FDA has yet to approve e-cigarettes for smoking cessation because there is currently limited research on their effectiveness, benefits, and risks for the human body. Additionally, scientists at the University of California found harmful metals in the vapor from tank-style e-cigarettes. These e-cigarettes are equipped with high-power batteries and atomizers to store more liquid. These result in high concentrations of metals like iron, lead, and nickel in the vapor. Exposure to and inhaling metallic particles may impair lung function and cause chronic respiratory diseases. As such, medical providers should not recommend e-cigarettes for smoking cessation. What can medical providers recommend for smoking cessation? Smoking cessation medication Presently, two FDA-approved prescription medicines for smoking cessation are Bupropion and Varenicline. Bupropion is an antidepressant that decreases tobacco cravings and withdrawal symptoms. It does this by increasing the brain chemicals dopamine and noradrenaline. This comes in a pill and can be used alongside other smoking cessation aids. Varenicline also reduces cravings and nicotine withdrawal symptoms. It blocks nicotine receptors in the brain, decreasing the amount of enjoyment one gets from smoking. One thing to note about this is that it will take several days for Varenicline's effects to take place. Therefore, it's best to prescribe these pills 1-2 weeks before the patient quits smoking. Like Bupropion, Varenicline may be used simultaneously with other quit-smoking products. Nicotine Replacement Therapy Nicotine replacement therapy (NRT) is a treatment involving nicotine consumption at gradually decreasing levels. This reduces the patient’s desire to smoke without them having to quit cold turkey. NRT involves using nicotine alternatives that don’t produce smoke, such as nicotine pouches and nicotine gum. Nicotine pouches are oral products containing ingredients like nicotine, flavoring, and plant-based fibers. These are placed between the lip and gum, where nicotine is absorbed into the bloodstream. Different variations have different strengths. On! pouches come in different strengths: 2mg, 4mg, and 8mg. Patients may start from 8mg variants and gradually decrease this dosage as their NRT progresses. Pouches also come in a wide range of flavors—including citrus, mint, and berry—to entice users. Meanwhile, nicotine gum is chewing gum that contains nicotine. It is chewed a few times before being parked between the gums and cheek for nicotine absorption. The nicotine gums by Lucy are a significantly better alternative for tobacco users. Like pouches, this gum comes in several flavors, such as cinnamon, mango, and wintergreen, and different strengths ranging from 2mg to 6mg. Counseling The recommendations mentioned above—medication and NRT—are more effective when coupled with counseling. A Primary Care Respiratory Medicine study revealed that successful smoking cessation is best attained through pharmacological treatment and counseling. Sessions typically involve a patient meeting with a counselor and they discuss their smoking habits, possible causes, and how to mitigate them. Medical providers should include counseling in addition to medication and NRT. E-cigarettes have yet to be approved by the FDA as smoking cessation aids. For now, medical providers should provide medication, NRT, and counseling to patients who want to quit smoking.

Future of Healthcare

Article | December 8, 2020

Health technologies range from devices, systems, and procedures to vaccines and medications that help deliver high-quality care, reduce costs for hospitals and patients, and streamline operations. It can be any software or IT tool that improves administrative productivity, eases workflow, and enhances the quality of life. New technology in healthcare includes supportive, educational, information, organizational, rehabilitative, therapeutic, preventive, and diagnostic solutions that improve patient access and healthcare provider capabilities. Virtual concierge, artificial intelligence, voice search, and virtual and augmented reality are promising emerging technologies for 2021.

Article | September 4, 2020

A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is a model that mirrors a unique physical object, process, organization, person or other abstraction. For healthcare providers, digital twins provide an abstraction of the healthcare ecosystem’s component characteristics and behaviors. These are used in combination with other real-time health system (RTHS) capabilities to provide real-time monitoring, process simulation for efficiency improvements, population health and long-term, cross-functional statistical analyses. Digital twins have the potential to transform and accelerate decision making, reduce clinical risk, improve operational efficiencies and lower cost of care, resulting in better competitive advantage for HDOs. However, digital twins will only be as valuable as the quality of the data utilized to create them. The digital twin of a real-world entity is a method to create relevance for descriptive data about its modeled entity. How that digital twin is built and used can lead to better-informed care pathways and organizational decisions, but it can also lead clinicians and executives down a path of frustration if they get the source data wrong. The underlying systems that gather and process data are key to the success for digital twin creation. Get those systems right and digital twins can accelerate care delivery and operational efficiencies. Twins in Healthcare Delivery The fact is that HDOs have been using digital twins for years. Although rudimentary in function, digital representations of patients, workflow processes and hospital operations have already been applied by caregivers and administrators across the HDO. For example, a physician uses a digital medical record to develop a treatment plan for a patient. The information in the medical record (a rudimentary digital twin) along with the physician’s experience, training and education combine to provide a diagnostic or treatment plan. Any gaps in information must be compensated through additional data gathering, trial-and-error treatments, intuitive leaps informed through experience or simply guessing. The CIO’s task now is to remove as many of those gaps as possible using available technology to give the physician the greatest opportunity to return their patients to wellness in the most efficient possible manner. Today, one way to close those gaps is to create the technology-based mechanisms to collect accurate data for the various decision contexts within the HDO. These contexts are numerous and include decisioning perspectives for every functional unit within the enterprise. The more accurate the data collected on a specific topic, the higher the value of the downstream digital twin to each decision maker (see Figure 1). Figure 1: Digital Twins Are Only as Good as Their Data Source HDO CIOs and other leaders that base decisions on poor-quality digital twins increase organizational risk and potential patient care risk. Alternatively, high-quality digital twins will accelerate digital business and patient care effectiveness by providing decision makers the best information in the correct context, in the right moment and at the right place — hallmarks of the RTHS. Benefits and Uses Digital Twin Types in Healthcare Delivery Current practices for digital twins take two basic forms: discrete digital twins and composite digital twins. Discrete digital twins are the type that most people think about when approaching the topic. These digital twins are one-dimensional, created from a single set or source of data. An MRI study of a lung, for example, is used to create a digital representation of a patient that can be used by trained analytics processes to detect the subtle image variations that indicate a cancerous tumor. The model of the patient’s lung is a discrete digital twin. There are numerous other examples of discrete digital twins across healthcare delivery, each example tied to data collection technologies for specific clinical diagnostic purposes. Some of these data sources include vitals monitors, imaging technologies for specific conditions, sensors for electroencephalography (EEG) and electrocardiogram (ECG). All these technologies deliver discrete data describing one (or very few) aspects of a patient’s condition. Situational awareness is at the heart of HDO digital twins. They are the culmination of information gathered from IoT and other sources to create an informed, accurate digital model of the real-world healthcare organization. Situational awareness is the engine behind various “hospital of the future,” “digital hospital” and “smart patient room” initiatives. It is at the core of the RTHS. Digital twins, when applied through the RTHS, positively impact these organizational areas (with associated technology examples — the technologies all use one or more types of digital twins to fulfill their capability): Care delivery: Clinical communication and collaboration Next-generation nurse call Alarms and notifications Crisis/emergency management Patient engagement: Experiential wayfinding Integrated patient room Risks Digital Twin Usability Digital twin risk is tied directly to usability. Digital twin usability is another way of looking at the issue created by poor data quality or low data point counts used to create the twins. Decision making is a process that is reliant on inputs from relevant information sources combined with education, experience, risk assessment, defined requirements, criteria and opportunities to reach a plausible conclusion. There is a boundary or threshold that must be reached for each of these inputs before a person or system can derive a decision. When digital twins are used for one or many of these sources, the ability to cross these decision thresholds to create reasonable and actionable conclusions is tied to the accuracy of the twins (see Figure 2). Figure 2: Digital Twin Usability Thresholds For example, the amount of information about a patient room required to decide if the space is too hot or cold is low (due to a single temperature reading from a wall-mounted thermostat). In addition, the accuracy or quality of that data can be low (that is, a few degrees off) and still be effective for deciding to raise or lower the room temperature. To decide if the chiller on the roof of that patient wing needs to be replaced, the decision maker needs much more information. That data may represent all thermostat readings in the wing over a long period of time with some level of verification on temperature accuracy. The data may also include energy load information over the same period consumed by the associated chiller. If viewed in terms of a digital twin, the complexity level and accuracy level of the source data must pass an accuracy threshold that allows users to form accurate decisions. There are multiple thresholds for each digital twin — based on twin quality — whether that twin is a patient, a revenue cycle workflow or hospital wing. These thresholds create a limit of decision impact; the lower the twin quality the less important the available decision for the real-world entity the twin represents. Trusting Digital Twins for HDOs The concept of a limit of detail required to make certain decisions raises certain questions. First, “how does a decision maker know they have enough detail in their digital twin to take action based on what the model is describing about its real-world counterpart?” The answer lies in measurement and monitoring of specific aspects of a digital twin, whether it be a discrete twin, composite twin or organization twin. Users must understand the inputs required for decisions and where twins will provide one or more of the components of that input. They need to examine the required decision criteria in order to reach the appropriate level of expected outcome from the decision itself. These feed into the measurements that users will have to monitor for each twin. These criteria will be unique to each twin. Composite twins will have unique measurements that may be independent from the underlying discrete twin measurement. The monitoring of these key twin characteristics must be as current as the target twin’s data flow or update process. Digital twins that are updated once can have a single measurement to gauge its appropriateness for decisioning. A twin that is updated every second based on event stream data must be measured continuously. This trap is the same for all digital twins regardless of context. The difference is in the potential impact. A facilities decision that leads to cooler-than-desired temperatures in the hallways pales in comparison to a faulty clinical diagnosis that leads to unnecessary testing or negative patient outcomes. All it takes is a single instance of a digital twin used beyond its means with negative results for trust to disappear — erasing the significant investments in time and effort it took to create the twin. That is why it is imperative that twins be considered a technology product that requires constant process improvement. From the IoT edge where data is collected to the data ingestion and analytics processes that consume and mold the data to the digital twin creation routines, all must be under continuous pressure for improvement. Recommendations Include a Concise Digital Twin Vision Within the HDO Digital Transformation Strategy Digital twins are one of the foundational constructs supporting digital transformation efforts by HDO CIOs. They are digital representations of the real-world entities targeted by organizations that benefit from the advances and efficiencies technologies bring to healthcare delivery. Those technology advances and efficiencies will only be delivered successfully if the underlying data and associated digital twins have the appropriate level of precision to sustain the transformation initiatives. To ensure this attention to digital twin worthiness, it is imperative that HDO CIOs include a digital twin vision as part of their organization’s digital transformation strategy. Binding the two within the strategy will reinforce the important role digital twins play in achieving the desired outcomes with all participating stakeholders. Building new capabilities — APIs, artificial intelligence (AI) and other new technologies enable the connections and automation that the platform provides. Leveraging existing systems — Legacy systems that an HDO already owns can be adapted and connected to form part of its digital platform. Applying the platform to the industry — Digital platforms must support specific use cases, and those use cases will reflect the needs of patients, employees and other consumers. Create a Digital Twin Pilot Program Like other advanced technology ideas, a digital twin program is best started as a simple project that can act as a starting point for maturity over time. Begin this by selecting a simple model of a patient, a department or other entity tied to a specific desired business or clinical outcome. The goal is to understand the challenges your organization will face when implementing digital twins. The target for the digital twin should be discrete and easily managed. For example, a digital twin of a blood bank storage facility is a contained entity with a limited number of measurement points, such as temperature, humidity and door activity. The digital twin could be used to simulate the impact of door open time on temperature and humidity within the storage facility. The idea is to pick a project that allows your team to concentrate on data collection and twin creation processes rather than get tied up in specific details of the modeled object. Begin by analyzing the underlying source data required to compose the digital twin, with the understanding that the usability of the twins is directly correlated to its data’s quality. Understand the full data pathway from the IoT devices through to where that data is stored. Think through the data collection type needed for the twin, is discrete data or real-time data required? How much data is needed to form the twin accurately? How accurate is the data generated by the IoT devices? Create a simulation environment to exercise the digital twin through its paces against known operational variables. The twin’s value is tied to how the underlying data represents the response of the modeled entity against external input. Keep this simple to start with — concentrate on the IT mechanisms that create and execute the twin and the simulation environment. Monitor and measure the performance of the digital twin. Use the virtuous cycle to create a constant improvement process for the sample twin. Experience gained through this simple project will create many lessons learned and best practices to follow for complex digital twins that will follow.