Health Technology, Digital Healthcare
Article | August 16, 2023
As the cost of care continues to rise, many hospitals are looking for long-term solutions to minimize inpatient services. Learn how technology and health care delivery will merge to influence the future of hospital design and the patient experience across the globe in this report developed by Deloitte US.
Five use cases for the digital hospital of the future
The future of health care delivery may look quite different than the hospital of today. Rapidly evolving technologies, along with demographic and economic changes, are expected to alter hospitals worldwide. A growing number of inpatient health care services are already being pushed to home and outpatient ambulatory facilities. However, many complex andv very ill patients will continue to need acute inpatient services.
With aging infrastructure in some countries and increased demand for more beds in others, hospital executives and governments should consider rethinking how to optimize inpatient and outpatient settings and integrate digital technologies into traditional hospital services to truly create a health system without walls.
To learn what this future of health care delivery may look like, the Deloitte Center for Health Solutions conducted a crowdsourcing simulation with 33 experts from across the globe. Participants included health care CXOs, physician and nurse leaders, public policy leaders, technologists, and futurists. Their charge was to come up with specific use cases for the design of digital hospitals globally in 10 years (a period that can offer hospital leaders and boards time to prepare).
The crowdsourcing simulation developed use cases in five categories
Redefined care delivery
Emerging features including centralized digital centers to enable decision making (think: air traffic control for hospitals), continuous clinical monitoring, targeted treatments (such as 3D printing for surgeries), and the use of smaller, portable devices will help characterize acute-care hospitals.
Digital patient experience
Digital and artificial intelligence (AI) technologies can help enable on-demand interaction and seamless processes to improve patient experience.
Enhanced talent development
Robotic process automation (RPA) and AI can allow caregivers to spend more time providing care and less time documenting it.
Operational efficiencies through technology
Digital supply chains, automation, robotics, and next-generation interoperability can drive operations management and back-office efficiencies.
Healing and well-being designs
The well-being of patients and staff members—with an emphasis on the importance of environment and experience in healing—will likely be important in future hospital designs.
Many of these use-case concepts are already in play. And hospital executives should be planning how to integrate technology into newly-built facilities and retrofit it into older ones.
Technology will likely underlie most aspects of future hospital care. But care delivery—especially for complex patients and procedures—may still require hands-on human expertise.
Laying the foundation for the digital hospital of the future
Building a digital hospital of the future can require investments in people, technology, processes, and premises. Most of these investments will likely be upfront. In the short term, hospital leadership may not see immediate returns on these investments. In the longer term, however—as digital technologies improve care delivery, create operational efficiencies, and enhance patient and staff experience—the return result can be in higher quality care, improved operational efficiencies, and increased patient satisfaction.
These six core elements of an enterprise digital strategy can help you get started as you begin to push your hospital into the future
Create a culture for digital transformation
It is essential that senior management understands the importance of a digital future and drives support for its implementation at all organizational levels.
Consider technology that communicates
Digital implementation is complex. Connecting disparate applications, devices, and technologies—all highly interdependent—and making certain they talk to each other can be critical to a successful digital implementation.
Play the long game
Since digital technologies are ever evolving, flexibility and scalability during implementation can be critical. The planning team should confirm that project scope includes adding, modifying, or replacing technology at lower costs.
Focus on data
While the requirements of data interoperability, scalability, productivity, and flexibility are important, they should be built upon a solid foundation of capturing, storing, securing, and analyzing data.
Prepare for Talent 2.0
As hospitals invest in exponential technologies, they should provide employees ample opportunities to develop corresponding digital strategies.
Maintain cybersecurity
With the proliferation of digital technologies, cyber breaches can be a major threat to hospitals of the future. Executives should understand that cybersecurity is the other half of digital implementation and allocate resources appropriately.
Read More
Health Technology, Digital Healthcare
Article | July 14, 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.
Read More
Healthtech Security
Article | November 29, 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.
Read More
Future of Healthcare
Article | January 28, 2022
As consumers, we crave convenience and simplicity, and across an array of industries, technology has made it increasingly easy to search for and purchase products and services. From getting a pizza delivered to buying a car online, the process often involves entering a few pieces of information, hitting send, and waiting for a confirmation email.
A Changing Landscape
Unsurprisingly, people want this same level of convenience and simplicitywhen they're seeking care. This change in consumer demand for convenience is further compounded by fundamental shifts in the healthcare ecosystem. Among these shifts are cost-sharing models that have increased patient out-of-pocket expenses, healthcare systems that are increasingly shifting toward delivering value-based care, and innovations in digital health solutions.
While patients want to play an active role in managing their well-being, that is often easier said than done in a system that uses a combination of manual processes and non-integrated point solutions to try and meet consumer demand. Disparate and burdensome methods of managing patient engagement often lead to inefficiencies within provider organizations, resulting in missed appointments, increased registration and eligibility-based denials, incomplete payments, higher collections and write-offs, and low patient satisfaction.
Consumer Dissatisfaction
Healthcare consumers today feel like they're fighting an uphill battle. According to Change Healthcare's 2020 Harris Poll Consumer Experience Index, 67% of respondents agreed that it “feels like every step of the healthcare process is a chore.” A similar percentage, 62%, agreed that “the healthcare system feels like it is set up to be confusing.”
Furthermore, if consumers don’t receive the level of convenience and digitization they want from their current provider, they’re more than willing to seek it out elsewhere. In a recent Black Book survey, 80% of respondents indicated they would be willing to change providers for more convenience even if they were receiving good care from their current provider. An even higher percentage of patients,90%, do not think they have to continue seeing a provider if that provider does not “deliver an overall satisfactory digital experience.”
A Patient-Centric Approach
Improving the patient experiencestarts with humanizing revenue cycle management(RCM) —the administrative process that takes the patient from registration and appointment scheduling to the final payment of a balance. Simply making administrative touchpoints self-service and easy to understand throughout the patient’s financial journey can help humanize revenue cycle management for providers.
How is that possible? By thinking about the patients’ side of the administrative process and leveraging innovative technologies like artificial intelligence, robotic process automation (RPA), natural language processing (NLP), and machine learning. The more that providers’ staffs are able to automate repetitive tasks, the more time they're able to spend helping provide a seamless patient engagement journey that is focused on a patient’s specific needs. In other words, reducing human intervention throughout our technologies allows providers to infuse more human interaction with each patient as they navigate their healthcare journey.
According to Change Healthcare’s 2020 Harris Poll Consumer Experience Index, what patients really want is a retail-like shopping experience with modern, streamlined communication, as thevast majority (81%) agreed that “shopping for healthcare should be as easy as shopping for other common services” via a streamlined access point online. A clear majority (71%) also said they want their health insurance and healthcare providers (68%) to communicate with them using more-modern platforms.
Simplified Scheduling and Payment
The entire clinical-care journey is focused on the specific needs of the patient rather than the provider, so why shouldn’t the patient’s financial journey be handled the exact same way? From a patient-satisfaction perspective, patients are not separating their clinical journey from their financial journey, so providers should start viewing it the same way.
It should be easy to schedule an appointment and modify that appointment if needed. Patients should have to (securely) provide their personal and insurance information only once (digitally and in advance), then be squared away when they show up for their appointment with their provider. In addition, because of COVID-19 and the heightened awareness surrounding personal interaction, it’s important to provide patients with no-contact check-in and waiting room options.
By humanizing RCM, providers can achieve a cohesive end-to-end journey that allows patients to quickly and easily get the care they need complete with clear communication, price transparency , and a provider who truly takes the time to understand their unique situations. By putting the patient back at the center of their care journey, providers can improve care outcomes while also driving maximized business outcomes for their organizations.
Read More