Health Technology, Digital Healthcare
Article | August 21, 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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Digital Healthcare
Article | November 29, 2023
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.
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Health Technology, Digital Healthcare
Article | August 16, 2023
Workers in the healthcare industry are among the most burned out demographics following the COVID-19 pandemic. In fact, a report by Medscape on physician burnout and depression in 2022 calculated a five-percentage point increase in burnout overall, from 42% in 2020 to 47% in 2021. Critical care physician burnout was also found to increase from 44% to 51% last year, placing them at the top of Maryville University’s list of physician specialties with the highest cases of burnout. This is closely followed by rheumatology physician burnout, which was 50% in 2021. At the bottom of the list, emergency medicine physician burnout still came in close at a rate of 44%.
Burnout can result in, among other things, exhaustion and a loss of concentration, which can be dangerous in healthcare. With that, advancements in technology have been made to help mitigate stress and reduce the chances of burnout in healthcare.
Maximum Tasks, Minimum Efficiency
Reports show that many technological advancements in the healthcare industry actually aren’t appropriate for managing physician workloads. This is due to the range of tasks physicians need to perform, from creating treatment plans to managing EHRs. Our previous discussion on EHR-Generated Messages highlighted how the misapplication of this algorithm had actually led to these inboxes getting clogged. This has primary care physicians spending more than half their workday interacting with EHRs that only remind physicians to order certain tests, instead of dealing with critical messages from patients or colleagues. This has been counterproductive in terms of efficiency, leading to more burnout symptoms and the tendency to reduce clinical work hours. It is therefore important that technology integrations consistently consider the broader picture of the tasks of physicians.
Tech Developments for Reducing Burnout
Shifts in the industry have thus begun to focus on the quality of efficiency and physician assistance, rather than the quantity of technology available. Here are some notable examples of technology that has become finely integrated within the healthcare industry.
Ambient Technology in Clinical Documentation
Ambient computing streamlines the clinical documentation process by using artificial intelligence to respond to human behavior and needs. This provides front-end speech and computer-assisted documentation, reducing the time needed for physicians to work on admin tasks, and thereby minimizing burnout. Smart hospitals have started leveraging this through sensor-based solutions, and experts from Michigan University believe usage must be made easier and simpler to use for the provider if the healthcare industry is to further leverage ambient computing for CDI. As of 2021, adoption has only started to take off, especially in the revenue cycle.
Computer Modeling in Vaccine Development
The traditional process of designing novel vaccines usually lasts 10 to 15 years and can cost between $200 million and $500 million. However, a feature by News Medical highlights the recent development of COVID-19 vaccines, which uncovered the capabilities of computational modeling systems. This showed an ability to predict which parts of a pathogen may be recognized by the immune system’s B cells and T cells. This allows rapid identification of vaccine targets from a genetic sequence, which reduces the years required for preclinical research. Physicians are thus able to respond faster to vaccine developments, and reduce the overload of health systems during any future pandemics or epidemics in the long term.
Patient Placement Technology
The shortage of physicians is a common setback in the industry, one that staff at the Rice County District Hospital in Lyons, Kansas mitigated using patient placement technology. Patient placement technology coordinated care for patients inside the 25-bed, level 4 hospital, as well as those needing to be transferred to another facility. By integrating local EMS and other transport services with health systems, manual telephone calls were no longer necessary. Hence, physicians were able to quickly and effectively get patients the care they needed while managing time-critical diagnoses. This maximizes the limited resources available without stretching out the workforce. Physicians are able to focus solely on their patients, knowing that the time-consuming logistics are being efficiently handled by technology. The industry needs to continue to look into the practices of reducing burnout among physicians, more so as we continue to recover from the effects of the COVID-19 pandemic. By emphasizing physician wellness and efficient technology, we can continue to assure the health and productivity of healthcare workers into the future.
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Health Technology
Article | September 12, 2023
Since ChatGPT’s launch in November 2022, artificial intelligence (AI) tools have become disruptive to nearly every industry. While there's been controversy about whether AI would benefit the healthcare industry, it has proven to be just as capable in healthcare as in other sectors.
In the medical field, there is reason to believe AI tools may be an even more reliable and useful resource than other sectors. Medical students have been panicking over AI's threat to their career prospects. But as these systems mature, the experts increasingly believe that AI may serve as a counterpart to human medical expertise rather than a threat.
How AI Tools Are Expected to Aid Medical Professionals?
Again and again, as the debate over modern AI tools rages on, we encounter the analogy of the calculator. No one feels threatened by calculators, not even professional mathematicians. Instead of throwing up their hands, math experts embrace the power of these now archaic computerized devices. If the experts are correct, this may be similar to the future of the alliance between AI and humans.
According to the designers and programmers who understand how these systems work as well as how information technology tends to progress, AI can be expected to help the medical profession in the following ways:
Cosmetic Surgery Consultations
One of the farthest-reaching applications we see develop is in consultations for plastic surgery and similar applications. Perhaps one of the easiest aspects to understand is hair-loss consultations. In our practice, we use a device known as HairMetrix, which uses an AI-driven analytical system to help determine what is causing a patient to lose their hair and which treatment options would be the most effective.
Because it is AI-driven, it is fully based on visual scans and is completely non-invasive. Just like this, AI can be used in an abundance of other ways to minimize the use of exploratory surgery and improve healthcare outcomes.
Improved Diagnostics
Artificial intelligence is already helping medical providers deliver diagnoses more quickly. These tools can identify anomalies that might otherwise take human hours or even weeks to identify. This has improved the rate of cancer detection, among other things, which will predictably improve survival rates.
Developing New Pharmaceuticals
The development of new medicines is notoriously slow. Not only is testing a painstaking process, but even seeking FDA approval can take years. AI is expected to help the development of pharmaceuticals through simulation on the molecular level, allowing researchers to see how the active mechanisms in a drug will work in the body.
Improved Administrative Efficiency
In the medical field, administrative tasks are notoriously slow. It is believed that generative AI will be able to automate many administrative functions and innumerable office chores. It could streamline sorting patient files, accelerate the interpretation of data, and much more.
Patient Access
In an area where information technology is already improving patients' lives, access to medical advice is still a bottleneck in the system. AI tools have the potential to slowly bridge the gap in health disparities. Combined with the power to diagnose, this could dramatically increase the capability of online patient portals.
Of course, this list of anticipated AI capabilities is far from exhaustive. Researchers and medical professionals have high hopes for these tools, and some are already proving to be more than mere speculation.
In a world where AI is reshaping industries at an unprecedented pace, the healthcare sector stands poised to benefit significantly from this technological revolution. From streamlining administrative tasks to revolutionizing diagnostics, the potential of AI in medicine is vast and diverse. As we witness AI-enabled tools like HairMetrix, enhancing the cosmetic surgery consultations and AI algorithms expediting diagnostic accuracy, it's clear that we are only at the beginning of a healthcare transformation that is set to improve patient care, increase survival rates, and revolutionize medical practices.
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