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):
Clinical communication and collaboration
Next-generation nurse call
Alarms and notifications
Integrated patient room
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.
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.
Article | January 8, 2021
Prioritizing health and managing it, has become highly important because our lifestyle is continuously evolving in ways that take a toll on us mentally, physically, and emotionally. However, the major issue for the patients lies in the inaccuracy of treatment due to the lack of complete health records in any hospital. With the recent changes in privacy legislation and data management, patients are even unable to retrieve their own health records.
For example, someone had an accident and was taken to the emergency room. The first thing they will need to do in their condition is to fill the hospital’s form. Then, for the treatment, if the injured person is conscious enough, doctors ask questions like if they are allergic to some medicines or do they suffer from diabetes or any other disease. Besides, what if the individual denies having allergies or diabetes in their half-conscious state? And the previous hospitals where they have already had treatment before have denied sharing the medical details of the person either due to privacy issues or data corruption. Well, it can create a lot of fuzz.
Solely, to improve the health industry without compromising the security of the individuals, blockchain has remained in the discussion. It has the potential to address the operability challenges present in the healthcare industry. But, what is blockchain, what are its underlying fundamentals, why blockchain, and what are its advantages?
Today’s blog will help in understanding every aspect of blockchain and its impact on the healthcare industry. So let’s get started!
What is Blockchain?
Blockchain is a P2P or peer-to-peer distributed or decentralized ledger technology. It stores a chain of data called blocks of information. These blocks are chained together by cryptographic signatures. These signatures are called hash that is stored in the shared ledger and backed by a connected processes network - node. These nodes reserve a copy of the complete chain and get continually updated by synchronization. Though, to include blockchain in the process it’s necessary to hire a developer who has prior experience and knowledge about its architecture and can work with the components efficiently as blockchain is a designed pattern that consists of three major constituents - a distributed network, a shared ledger, and all the digital transactions.
a. Distributed Network
As discussed before, blockchain is built on peer-to-peer networks. While having no central point of storage, it makes the information on the network less vulnerable to being lost or exploited.
Unlike the traditional client-server model that has a centralized storage point or controlling party, all the information in the blockchain network is constantly recorded and transferred to the participants of the network that are also known as nodes or peers. These peers also own several identical copies of the information. That’s why blockchain is seen as a huge improvement to centralized models and is considered the future of data storage and ownership.
b. Shared Ledger
Each authorized participant in the network records the transactions into the shared ledger. If they want to add any transaction, it is important to run algorithms that evaluate and verify the transactions. If the majority of members agree to the transaction’s validity, a new transaction gets added to the shared ledger. The changes done in the shared ledger is reflected in minutes or even seconds in the copies of the blockchain. Once the transaction is added, there’s no way to modify or delete it. Also, as the copy is shared in the form of a ledger to each member, no single member can alter data.
c. Digital Transaction
Transactions are information i.e. data transmission to one block. During the process of data transmission, each node acts as a central point to generate and digitally sign the transaction. As the nodes connect each other in the network, each of them has to verify the transaction independently for its conflicts, validity, and compliance. Only after the transaction passes the verification, the information is added into the shared ledger. The major element that makes digital transactions successful is cryptographic hashing that encrypts the data for security.
Why Blockchain technology in healthcare?
It has happened so often that the patient remains unable to gather all of their previous medical records in one format from one place swiftly or sometimes cannot even collect the required information at all. Unfortunately, in most cases, the information of critical patients remains scattered across several different institutions of healthcare that too in different formats. Besides, the data management systems along with the security regulations also vary in different institutions making it difficult to trace and fix mistakes.
But, what can blockchain do?
A blockchain is a system used for storing and sharing information with security and transparency. Every block in the chain is an independent unit of its own and a dependent link among the collective chain that creates a network controlled by participants rather than a third party.
As blockchains are managed by network nodes instead of central authority, they are decentralized that prevents one entity from having complete control over the network. With the incorporation of blockchain, the need for a central administrator will be removed by cryptography. Healthcare providers will be able to promote data management processes beyond perception. It will help in collecting, analyzing, sharing, and securing medical records. It will provide the access to healthcare workers for retrieving health records with the cryptographic keys provided by patients from anywhere without creating any privacy or security problems.
Advantages of Healthcare Blockchain
Although applications of blockchain in the healthcare industry are inceptive, some early solutions have shown the possibility of reduced healthcare costs, improved access to information among different stakeholders, and streamlining the entire business process. So, keeping aside the buzz, let’s see the real advantages of blockchain in healthcare.
1. Master Patient Indexes
The master patient index helps in the identification of patients across separate administrative systems. It is often created within the EHR or electronic health record system. As these EHRs have different vendors, there are several irregularities of MPIs. In many cases, the data of a patient between these healthcare systems become mismatched. However, with the nature of decentralization in Blockchain, it possesses the ability to solve the issue. In the blockchain-based MPIs, the data will be hashed to the ledger and content will remain unique as only the authorized nodes of the data can make changes to the hashes while all parties with access can only check the related information.
2. Single, elongated patient records
Blockchain technology is potent to transform health care by placing patients at the center of the system while increasing the security and privacy of health records. It provides a new model for health information exchange by forming electronic elongated patient records secured and efficient. Additionally, the fact that the data is copied among all the nodes of the blockchain network creates an atmosphere of clarity and transparency that enables healthcare providers and patients to know how their data is handled by whom, how, and when. It can also help healthcare from potential frauds, data losses, or security attacks.
3. Supply Chain Management
Supply chain management in healthcare is a challenging aspect. With scattered settings for ordering drugs, medical supplies, and critical resources, there’s an inherent risk of compromising the supply chain that might impact patient safety. Indulgence of blockchain technology in the transactions can tap into the complete process of medicine or drug products movement. As all the transactions will be recorded onto the shared ledger with every block recording and maintaining every transaction, it will become easy to verify the vendor, distributor, and origin of the drug within a matter of seconds. It will also enable healthcare physicians and officials to check the authenticity of the supplier’s credentials.
4. Claims Justification
Currently, the insurance claim processes face difficulties like lack of transparency i.e. most customers don’t even know how insurance works; human errors and inefficiencies i.e. insurances are full of confusion along with human errors that create inefficiencies that lead to the increased cost to customers; higher frauds in claims. But, blockchain technology can simplify and enhance recordkeeping, payment processing, claims registration, contract management, and closure with its immutable ledger.
Interoperability is the capability of distinct healthcare information technology to interpret, exchange, and use data. Due to the privacy issues, the alphanumeric code to identify a patient has been revoked that caused problems in gathering the required record of the patient. Enforcing measurement standards for industry-wide interoperability is also a challenge in interoperability. With blockchain in healthcare interoperability, data can be shared in real-time on the trusted network and provides access to the patient’s record in a secured manner. Moreover, with the pri
Article | December 15, 2020
As medical science has improved rapidly, life expectancy around the world also has risen. Still, as longevity increases, healthcare systems are facing a growing demand for advanced services, increased costs, and a struggling workforce to meet various requirements of patients. Demand is driven by many unstoppable forces: a shift in lifestyle choices, shifting patient expectations, population aging, and the never-ending cycle of innovation are a few among others.
Challenges Faced by the Healthcare Industry
According to Mckinsey, one in four people in North America and Europe will be over the age of 65 by 2050. This shows that, soon, the healthcare industry will have to deal with a larger number of patients with more complex requirements. Catering to these patients is expensive and requires health systems for providing long-term focused and proactive care. To remain sustainable, healthcare systems need major transformational and structural changes.
The industry also needs a larger workforce because according to the World Health Organization (WHO), globally there is a shortfall of approximately 9.9 million nurses, physicians, and midwives. Apart from attracting, training, and retaining these healthcare professionals, you also have to ensure that their time and effort add value to patient care. Utilizing the solutions powered by modern technologies, such as Artificial Intelligence (AI) in the healthcare industry, will add perfection and more value to human efforts.
AI in the healthcare industry has the potential required to transform and revolutionize healthcare by addressing the challenges in the industry mentioned earlier. AI can better the outcomes, improve efficiency, and augment productivity in healthcare delivery. This article takes an in-depth look at the impact of AI in healthcare.
Impact of AI in the Healthcare Industry
In the coming years, AI in the healthcare industry will improve the day-to-day life of healthcare practitioners, augment the patient experience, improve care delivery, and can even facilitate life-saving treatments and revolutionize the industry. Additionally, AI will improve population-health management, operations, and strengthen innovations.
According to Statista, the global AI healthcare market will increase to more than US$28 billion by 2025. Here is a detailed look into the areas where and how AI in the healthcare industry will be impactful.
Chronic Care Management
Chronic diseases, such as cancer, diabetes, kidney diseases, are the leading cause of disability and death in the US and the main drivers of the country’s annual health cost. Effectively managing various chronic diseases is an overarching and long-term process. But with the help of the right tool, healthcare providers can meet the needs of these patients without delay.
Artificial intelligence tools in the healthcare industry can help healthcare providers overcome the complexities of chronic disease management and make it more effective and provide quality treatment. AI in the healthcare industry is increasingly being leveraged by organizations to improve chronic disease management, enhance patient health, and drive down costs, which will also eventually result in data-driven and personalized care. AI in the healthcare industry is expected to move the industry toward proactive care delivery from a reactive one and lead the industry to provide more individualized treatments. This is just one of the ways AI in the medical industry is going to revolutionize chronic care management in hospitals.
Artificial intelligence in the healthcare industry is changing the way care is delivered; it is expected to make healthcare more efficient, accurate, and accessible. Reducing costs and improving health outcomes are the values health systems and hospitals are trying to deliver to patients every day. Hospitals are increasingly incorporating technologies, which are powered by the use of AI in healthcare to meet the challenge.
According to the American Hospital Association (AHA), AI in the healthcare industry has unlimited potential to solve most of the vexing challenges in the industry. They identify AI use cases in the healthcare industry in four broad areas, which are administrative, operational, financial, and clinical areas.
Administrative Use Cases for AI in the Healthcare Industry
• Admission procedures
• Appointment scheduling
• Customer service responses
• Discharge instructions
• Hiring and orientation protocols
• Licensure verification
• Patient check-in procedure
• Prior authorizations
• Quality measure reporting
Operational Use Cases of AI in the Healthcare Industry
• Inventory management
• Materials management
• Supply chain management
• Facilities management
Financial Use Cases for AI in the Healthcare Industry
• Billing and collections
• Claims management
• Insurance eligibility verification
• Revenue cycle management
Clinical Use Cases of AI in the Healthcare Industry
• Predictive technologies
• Interventional technologies
By incorporating and utilizing these scopes with AI in the healthcare industry, the industry can be transformed into a next-gen level in no time. It also allows healthcare practitioners to focus more on patients, which would eventually help in raising staff morale and improving retention.
Clinical Decision Support
Recent advancements in AI in the health industry are capable of enhancing the currently used clinical decision support (CDS) tools to have value-based imaging and to improve patient safety. According to the National Institute of Health (NIH), the synergy between CDS systems and AI in the healthcare industry will be able to:
• Reduce friction in radiology workflows
• Identify relevant imaging features easily
• Generate structured data to develop machine learning algorithms
• Enable an evolution toward decision support for a holistic patient perspective
• Suggest imaging examinations in complex clinical scenarios
• Assist in identifying appropriate imaging opportunities
• Suggest appropriate individualized screening
• Aid health practitioners to ensure continuity of care
AI in the healthcare industry is competent in making CDS a next-gen one, enhancing the experiences of radiologists and providers, and improving patient care.
Slowly but surely, AI is improving almost every aspect of human life with innovations and advancements. The latest is that AI in the healthcare industry is impending a revolution in medical diagnostics by providing accurate risk assessments, accelerating disease detection, and boosting hospital productivity. By automatically prioritizing urgent cases and accelerating reading time, image recognition AI enhances the workflow of radiologists. It even helps in the prevention of diseases by the early detection of diseases.
In medical images such as x-rays, MRIs, and CT scans, AI-driven software can efficiently be used to accurately spot signs of many diseases, especially in detecting many chronic diseases such as cancer. According to the NIH, AI will be widely applied in the healthcare industry especially for various tasks such as patient engagement and adherence, diagnosis, and treatment recommendations. So, there is no doubt that AI in healthcare will revolutionize the diagnostic process in the approaching years by detecting diseases, classifying diseases, and improving the decision-making process. The application of AI in the healthcare industry will make people live longer.
Triage and Diagnosis
AI can be effectively used to automatically triage cases. AI algorithms will analyze the cases and forward cases to pathologists after determining the priority based on the probability of cases according to the criteria set by labs. This makes the workflow of pathologists easier and efficient. Through the process the algorithm will be able to:
• Verify the digital images attached to the case belong to that case
• Validate the tests ordered and match the specimen type
• Identify cases marked as stat
• Determine the cases, which can be positive or are most likely to be negative
Moreover, AI technologies in the healthcare industry also can be effectively used to provide more accurate and faster diagnoses. This speeds up the entire process of triage and diagnosis and is expected to revolutionize the healthcare industry soon.
The Future Outlook for AI in the Healthcare Industry
Over the next few years, AI in the healthcare industry has the best opportunities in hybrid models to support clinicians in diagnosis, identifying risk factors, and in treatment planning. This scope will result in faster adoption of AI technology in healthcare, which will show measurable improvements in operational efficiency and patient outcomes.
With a plethora of issues to overcome, which are driven by documented factors such as growing rates of chronic diseases and the aging population, it is obvious that the healthcare industry needs new innovative solutions. AI-powered solutions in the healthcare industry will achieve a clear impact on the global healthcare industry in a short time.
Frequently Asked Questions
Which is the best application of AI in the healthcare sector?
Cognitive surgical robotics is the best application of AI in the healthcare sector as it helps practitioners collect data from real surgical processes, which would help in improving existing surgical approaches.
Why is artificial intelligence important in healthcare?
Artificial intelligence in healthcare is vital as it can help make decisions, analyze and manage data, and have conversations. So, AI will drastically change the everyday practices and roles of clinicians.
When was AI-first used in healthcare?
The term, Artificial intelligence (AI) was first described in 1950, but the limitations of the term prevented its acceptance. In the 2000s, these limitations were overcome and people started to accept the term.
Article | April 11, 2020
Over the past twenty-five years most businesses have been revolutionized by the easy availability of cloud and mobile-based computing systems. These technologies have placed power and access into the hands of employees and customers, which in turn has created huge shifts in how transactions get done. Now the companies with the highest market value are both the drivers of and beneficiaries of this transition, notably Apple, Facebook, Amazon and Alphabet (Google), as well as their international rivals like Samsung, Baidu, Tencent and Alibaba. Everyone uses their products every day, and the impact on our lives have been remarkable. Of course, this also impacts how businesses of all types are organized. Underpinning this transformation has been a change from enterprise-specific software to generic cloud-based services—sometimes called SMAC (Social/Sensors/Mobile/Analytics/Cloud). Applications such as data storage, sales management, email and the hardware they ran on were put into enterprises during the 80s and 90s in the client-server era (dominated by Intel and Microsoft). These have now migrated to cloud-based, on-demand services.