In Lung Cancer Diagnosis and Care

| May 31, 2019

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Early diagnosis of lung cancer is linked to achieving better outcomes for patients, and improving rate of survival1 . Over the last twenty years significant technological advances, developments in research and evolutions in specialised care have had a significant improvement to both the diagnosis of lung cancer and lung cancer supportive care.

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Innovation Insight for Healthcare Provider Digital Twins

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.

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Advanced Healthcare Supply Chains: Why It’s All in the Data

Article | February 10, 2020

During the past decade, the healthcare industry has undergone an unprecedented technological transformation. The industry, once defined by manual processes, has moved squarely into the digital age. As patients, we’ve all become accustomed to seeing physicians as well as clinical staff use laptops during office visits. And behind the scenes, hospitals and health networks have made substantial investments in financial and HR systems, among others. One of the more significant digital advancements has been the industry’s focus on applying greater levels of automation to supply chain processes. In doing so, provider and supplier organizations have improved the efficiency of their supply chains, driven out millions of dollars in cost and waste, all while keeping patient care front and center.

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COVID-19: How do we get out of this quagmire?

Article | July 17, 2020

The COVID-19 virus (C19) pandemic is turning out to be the event of the century. Even World War seems timid in comparison. We are in the 4th month of the virus (in non-China countries) and have gone past the lockdown in many places. Isn’t it time we re-think the approach? What if there is another wave of C19 coming soon? What if C19 is the first of many such events in the future? Before we get into analysis and solution design, summarizing the C19 quirks: While a large section of the affected population is asymptomatic, for some it can be lethal There isn’t clarity on all the ways C19 spreads It’s known to affect the lungs, heart, and kidneys in patients with weak immunity It has been hard to identify a definitive pattern of the virus. Some observations in managing the C19 situation are: With no vaccine in sight, the end of this epidemic looks months or years away Health care personnel in hospitals need additional protection to treat patients Lockdowns lead to severe economic hardship and its repeated application can be damaging Quarantining people has an economic cost, especially in the weaker sections of society If one takes a step back to re-think about this, we are primarily solving 2 problems: Minimise deaths: Minimise the death of C19 and non-C19 patients in this period Maximise economic growth: The GDP output/growth should equal or higher than pre-C19 levels One needs to achieve the 2 goals in an environment of rising number of C19 cases. Minimise deaths An approach that can be applied to achieve this is: Data driven health care capacity planning Build a health repository of all the citizens with details like pre-existing diseases, comorbidity, health status, etc. The repository needs to be updated quarterly to account for patient data changes This health repository data is combined with the C19 profile (disease susceptibility) and/or other seasonal diseases to determine the healthcare capacity (medicines, doctors, etc.) needed The healthcare capacity deficit/excess needs to be analysed in categories (beds, equipment, medicine, personnel, etc.) and regions (city, state, etc.) and actions taken accordingly Regular capacity management will ensure patients aren’t deprived of timely treatment. In addition, such planning helps in the equitable distribution of healthcare across regions and optimising health care costs. Healthcare sector is better prepared to scale-up/down their operations Based on the analysis citizens can be informed about their probability of needing hospitalisation on contracting C19. Citizens with a higher health risk on C19 infection should be personally trained on prevention and tips to manage the disease on occurrence The diagram below explains the process Mechanism to increase hospital capacity without cost escalation Due to the nature of C19, health personnel are prone to infection and their safety is a big issue. There is also a shortage of hospitable beds available. Even non-C19 patients aren’t getting the required treatment because health personnel seek it as a risk. This resulted in, healthcare costs going up and availability reducing. To mitigate such issues, hospital layouts may need to be altered (as shown in the diagram below). The altered layout improves hospital capacity and availability of health care personnel. It also reduces the need for the arduous C19 protection procedures. Such procedures reduce the patient treatment capacity and puts a toll on hospital management. Over a period, the number of recovered C19 persons are going to increase significantly. We need to start tapping into their services to reduce the burden on the system. The hospitals need to be divided into 3 zones. The hospital zoning illustration shown below explains how this could be done. In the diagram, patients are shown in green and health care personnel are in light red. **Assumption: Infected and recovered C19 patients are immune to the disease. This is not clearly established Better enforcement of social factors The other reason for high number of infections in countries like India is a glaring disregard in following C19 rules in public places and the laxity in enforcement. Enforcement covers 2 parts, tracking incidents of violation and penalising the behaviour. Government should use modern mechanisms like crowd sourcing to track incidents and ride on the growing public fear to ensure penalty enforcement succeeds. The C19 pandemic has exposed governance limitations in not just following C19 rules, but also in other areas of public safety like road travel, sanitation, dietary habits, etc. Maximise economic growth The earlier lockdown has strained the economy. Adequate measures need to be taken to get the economy back on track. Some of the areas that need to be addressed are: One needs to evaluate the development needs of the country in different categories like growth impetus factors (e.g. building roads, electricity capacity increase), social factors (e.g. waste water treatment plants, health care capacity), and environmental factors (e.g. solar energy generation, EV charging stations). Governments need to accelerate funding in such projects so that that large numbers of unemployed people are hired and trained. Besides giving an immediate boost to the ailing economy such projects have a future payback. The governments should not get bogged down by the huge fiscal deficit such measures can create. Such a mechanism to get money out in the economy is far than better measures like QE (Quantitative Easing) or free money transfer into people’s bank accounts Certain items like smartphone, internet, masks, etc. have become critical (for work, education, critical government announcements). It’s essential to subsidise or reduce taxes so that these items are affordable and accessible to everyone without a financial impact The government shouldn’t put too many C19 related controls on service offerings (e.g. shops, schools, restaurants, cabs). Putting many controls increases the cost of the service which neither the seller not buyer is willing or able to pay. Where controls are put, the Govt should bear the costs or reduce taxes or figure out a mechanism so that the cost can be absorbed. An event like the C19 pandemic is a great opportunity to rationalise development imbalances in the country. Government funding should be channelized more to under-developed regions. This drives growth in regions that need it most. It also prevents excess migration that has resulted in uncontrolled and bad urbanisation that has made C19 management hard (guidelines like social distance are impossible to follow) Post-C19 lockdown, the business environment (need for sanitizers, masks, home furniture) has changed. To make people employable in new flourishing businesses there could be a need to re-skill people. Such an initiative can be taken up by the public/private sector The number of C19 infected asymptomatic patients is going to keep increasing. Building an economy around them (existing, recovered C19 patients) may not be a far-fetched idea. E.g. jobs for C19 infected daily wage earners, C19 infected taxi drivers to transport C19 patients, etc. In the last 100 years, mankind has conquered the destructive aspects of many a disease and natural mishap (hurricanes, floods, etc.). Human lives lost in such events has dramatically dropped over the years and our preparedness has never been this good. Nature seems to have caught up with mankind’s big strides in science and technology. C19 has been hard to reign in with no breakthrough yet. The C19 pandemic is here to stay for the near future. The more we accept this reality and change ourselves to live with it amidst us, the faster we can return to a new normal. A quote from Edward Jenner (inventor of Small Pox) seems apt in the situation – “The deviation of man from the state in which he was originally placed by nature seems to have proved to him a prolific source of diseases”.

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HIMSS Canceled Amid Coronavirus – What Can Healthcare IT Take Away?

Article | March 9, 2020

With the announcement of the HIMSS conference cancellation the healthcare IT industry is hit hard. Healthcare Information and Management Systems Society (HIMSS) is the super bowl of healthcare tech. 45,000 attendees from 50 countries were scheduled to fly into Orlando and spend 5 days immersed in innovative healthcare tech – and of course, shop around on the Expo floor. For many companies, the HIMSS event is the lifeline of their sales pipeline for the entire year – we’re talking tons of leads. Not having HIMSS means major threats to healthcare technology company revenue. We need to be more prepared to deal with pandemics no matter what. And as an industry, we need to be focused on developing technology solutions for disease detection, tracking, and prevention. And we need greater emphasis on data interoperability. Every time a crisis happens, the world is reminded of why it’s so important for systems and entities to share information. We learned this lesson when 9/11 happened. Data interoperability can help us identify problems earlier and get a handle on mitigation before issues get out of control.

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Spotlight

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We're not your typical healthcare business, we’re all about the long-term partnership approach. As specialists in healthcare, we invest in and develop primary care centers, care homes, retirement villages, private hospitals and specialist schools, creating brilliant new environments to improve people’s health and wellbeing experience, for the better.

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