Health Technology, Digital Healthcare
Article | September 8, 2023
“Health care is different, the data here is emotional! If you tell me you were buying a fishing rod online and were emotional about it, I’d say you are lying. But I do frequently see people helpless and confused when it comes to receiving health care, managing its costs, making sense of its data.”
- Senior Product Leader inOptum Global Solutions Pvt. Ltd.
Yes, health care is different, and so is product management in it. This piece highlights the top 4 product management trends that are specific to health care and serve beyond being just a list of technologies making their way into health care.
Health care consumerism
Lance broke his ankle in a bicycle accident and is now in hospital waiting for surgery. Which of these words would describe him more aptly— a ‘patient’ or a ‘health care consumer’? The fact that Lance holds a high-deductible health plan, manages an interactive relationship with his primary doctor, keenly monitors his fitness through his smartwatch, and learns about healthier diet plans and recipes online — I can say he isn’t just receiving health care, but making active choices on how to pay for and manage his health. This choice and responsibility that people demand, is ‘health care consumerism’. This trend has been growing since 2015 when value-based care started picking up in the US.
What does this imply for products/PMs?
These are challenging and exciting times to be a product manager (PM) in health tech. This is because people are now demanding an experience equivalent to what they’re used to from other products in their lives, such as e-commerce, streaming platforms, and digital payments, to name a few. Any consumer-facing product (a mobile app, a web-based patient portal, a tech-enabled service) needs to meet high expectations. Flexible employer-sponsored health plans options, health reimbursement arrangements, price transparency products for drugs and medical expenses, remote health care services, and government's push to strengthen data and privacy rights — all point to opportunities for building innovative products with ‘health care consumerism’ as a key product philosophy.
Wellness
COVID-19 has tested health care systems to their limits. In most countries, these systems failed disastrously in providing adequate, timely medical assistance to many infected people. Prevention is of course better than cure, but people were now forced to learn it the hard way when cure became both inaccessible and uncertain. With lockdowns and social isolation, prevention, fitness, diet, and mental wellbeing all took center stage.
Wellness means taking a ‘whole-person approach’ to health care — one where people recognize the need to improve and sustain health, not only when they are unwell, but also when they’re making health care decisions that concern their long-term physical and mental health. A McKinsey study notes that consumers look at wellness from 6 dimensions beyond sick-care— health, fitness, nutrition, appearance, sleep, and mindfulness. Most countries in the study show that wellness has gained priority by at least 35% in the last 2–3 years. And wellness services like nutritionists, care managers, fitness training, psychotherapy consultants contribute 30% of the overall wellness spend.
So, what do health-tech PMs need to remember about wellness?
The first principle is, “Move to care out of the hospital, and into people’s homes”. A patient discharged after knee surgery has high chance of getting readmitted if he/she has high risk of falling in his/her house, or is unable to afford post-discharge at-home care with a physiotherapist. This leads us PMs to build products that recognize every person’s social determinants of health and create support systems that consider care at the hospital and care at home as a continuum.
The second principle is, “Don’t be limited by a narrow view of ‘what business we are in’, as wellness is broad, and as a health tech company, we are in health-care, not sick-care”. Wellness products and services include — fitness and nutrition apps, medical devices, telemedicine, sleep trackers, wellness-oriented apparel, beauty products, and meditation-oriented offerings, to name just a few. Recent regulations in many countries require health care providers to treat behavioural health services at par with treating for physical conditions, and this is just a start.
Equitable AI
Last month, WHO released a report titled “Ethics and Governance of Artificial Intelligence for Health”. The report cautions researchers and health tech companies to never design AI algorithms with a single population in mind. One example I read was, “AI systems that are primarily trained on data collected from patients in high-income settings will not perform as effectively for individuals in low or middle-income communities.” During COVID-19, we came across countless studies that talked about the disproportionate impact on minorities in terms of infections, hospitalizations, and mortality. A student at MIT discovered that a popular out-of-the-box AI algorithm that projects patient mortality for those admitted in hospitals, makes significantly different predictions based on race — and this may have adversely moved hospital resources away from some patients who had higher risks of mortality.
How should I think about health equity as an AI health-tech PM?
Health equity means that everyone should have a fair chance at being healthy. As a PM, it’s my job to make sure that every AI-assisted feature in my product is crafted to be re-iterative and inclusive, to serve any community or subpopulation, and is validated across many geographies. To prevent any inequitable AI from getting shipped, it is important to ensure that the underlying AI model is transparent and intelligible. This means knowing what data goes into it, how it learns, which features does it weigh over others, and how does the model handles unique features that characterize minorities.
Integrated and interoperable
In every article that I read on topics such as digital platforms, SaaS, or connectivity with EMRs, I always find the words: ‘integrated’ and ‘interoperable’ therein. Most large and conventional health tech companies started by offering point-solutions that were often inextensible, monolithic, and worked with isolated on-prem servers and databases. To give a consistent user experience, leverage economies of scope, and scale products to meet other needs of their customers, started an exodus from fragmented point-solutions to interoperable, integrated solutions. The popularization of service-oriented architectures (SOAs) and cloud vendors like AWS, Azure, and GCP has also helped.
The what and how of integrated-interoperable solutions for PMs:
Integrated solutions (IS), as I see them, are of two kinds — one, in which as a health tech company, we help our customers (health systems, insurance companies, direct to consumers) accomplish not just one, but most/all tasks in a business process. For example, a B2B IS in value-based care contract management would mean that we help our customers and health systems by giving an end-to-end solution that helps them enter into, negotiate, plan for, manage, get payments for their value-based contracts with health plans.
In the second type of IS, we offer products that can be easily customized to different types of customers. For example, a health management app that people can subscribe to for different programs such as obesity, diabetes, hypertension, cholesterol management, as needed. The app works with different datasets for these programs and uses different analyses and clinical repositories in its backend, but still delivers a consistent user experience across programs to a user who enrolled in multiple programs, say diabetes and weight management.
‘Interoperable’ simply means that one product should be able to talk to other products both in and out of the company. For example, if product-A can alert a doctor about any drug-drug interactions or allergies a patient might have, while she is writing prescriptions for the patient in product-B (an EMR), then product-A does talk to product-B, and hence, is interoperable. This trend is picking up further with the growth of IoT devices, and industry-wide participation in adopting common standards for data exchange.
Conclusion
Though the article derives much of its context from US health care, I have tried to keep a global lens while choosing these topics. For developing economies like India, digitization is the number one trend as much of the health system is still moving from manual records to digitally store patient and medical data in EMRs. The good news is that India is booming with health-tech innovation and that is where consumerism, wellness, and equitable AI make sense. Once companies develop enough point-solutions for different health system needs and use-cases, Indian health tech will see a move towards creating integrated, interoperable (IGIO) systems as well.
There are some other trends such as — use of non-AI emerging tech such as Blockchain in health information management, cloud infrastructure for health tech innovation, big data and analytics to improve operational efficiency in areas such as claims management and compliance reporting, Agile product management for co-developing with and continuously delivering to clients etc. — but I see them either as too nascent, or too old to feature in this list.
Finally, as a health tech product manager, you can use the following questions to assess your products against the above trends — (Consumerism) do the products that I manage, empower consumers with choice, information, and actionability? (Wellness) Does my product emphasize keeping them out-of-hospitals and healthy in the first place? (Equitable AI) Am I sure that my product doesn’t discriminate against individuals belonging to underserved populations? (IGIO) And finally, is my product scalable, integrated and interoperable to expand to a platform, in the true sense?
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Health Technology, AI
Article | July 18, 2023
Affordable healthcare is the need of the hour and interoperability is the means to that end. The healthcare ecosystem is looking into ways they can enable this affordability as soon as possible, and that is where FHIR comes in. FHR promises an on-demand exchange of secure healthcare information. It has become an increasingly popular protocol, thanks to its commitment to ensuring interoperability in the app economy, via apps.
The privilege enjoyed by consumers and participants in most industries is the ease of accessibility of information. With most of it being on the cloud, a URL ensures access to the same information regardless of where it is being accessed from or which internet enable device. This is the privilege FHIR aims to introduce in healthcare, over the current document-based approach where forms are either faxed, emailed, or electronically exchanged.
FHIR is for
Building new healthcare apps
Develop cloud-based health apps that integrate with social networks
Providing a simple to use standards-based API for cloud-based health integration services
The government looking to implement a national EHR
FHIR Aims to Reduce Cost
The medical expenses of an average American in a year are north of $12k and rising. The reasons are the unnecessary complications in the healthcare IT infrastructure. With FHIR, HL7 aims to reduce the burden on providers in sharing and accessing healthcare data at the point of care, thereby reducing the administrative expenses spent on moving data back and forth. It also promises to grant on-demand access to patients – enabling them to make better-informed healthcare decisions.
What is part of FHIR?
Pre-defined Resources and API
A common way to represent data as building blocks and rules for connecting them
Target support for common scenarios
Implementer Friendly
Familiar tooling and technologies using web standards
Multiple Libraries available for faster implementations
Mobile Friendly
Concise and easily understood specifications, RESTful API and JSON
Leverages cross-industry web technologies
Multi-paradigm
Thick client, browser, or mobile devices
Supports human readability as the base level of interoperability
Large Community for Support
Heaps of open-source software and training events, webinars, and connectathons
Specification feedback welcomed, including update requests-tracker
Out-of-the-box Interoperability
Base resources can be used as it is, can also be adapted for local requirements
Seamless exchange of information using messages or document
Start The FHIR
Health plans are jumping on the FHIR bandwagon faster than ever thanks to the Interoperability and Patient Access rule as well as the latest proposals. Do not get left behind or sustain the ramifications of non-compliance with CMS regulations.
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Health Technology, Digital Healthcare
Article | September 7, 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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Future of Healthcare
Article | February 19, 2022
Anesthesia groups face major challenges in the aftermath of the pandemic: Financially strapped hospitals are increasingly unwilling or unable to pay anesthesia subsidies, and a shortage of qualified anesthesiologists and CRNAs is making recruitment extraordinarily competitive.
The good news is that anesthesia opportunities are plentiful in the ambulatory surgery center (ASC) market. As more inpatient procedures migrate to ASCs, anesthesia practices can help meet demand by working with hospitals and ASCs. A dual-contracting approach can help increase revenue, reduce operational risk, enhance recruiting leverage, and present opportunities for equity investments in ASC ventures.
Expanding ASC Case Mix
Multiple factors are driving increased ASC volume.Consumers have long been attracted to the convenience andfast turnaround timesASCs offer, and as the pandemic began to take hold and patients worried about becoming infected in hospitals, theirpopularityincreased.
But even before the pandemic hit, theuse of ASCs was growing,with the number of centers increasing 7.1% annually since 2016.1No doubt this was in part driven by Medicare restricting fewer surgeries to the inpatient only (IPO) setting. This year alone, Medicare is adding 11 orthopedic procedures to the ASC-approved list, including total knee arthroscopy (TKA) and total hip arthroscopy (THA).2Commercial payersare alsofuelingASC volume by promotingthis venue as a lower-cost option to members.Lastly, with more than 90% of ASCs at least partially owned by physicians,providers themselvesare driving moreprocedures to this setting.
Hospitals Become ASC Buyers
For years, hospitals viewed ASCs as direct competition and discouraged or even prohibited inpatient anesthesia practices from contracting with them. But that dynamic is changing as more hospitals become buyers or majority investors.
According to a recent survey, the percentage of hospitals and health systems planning to increase their investments in ASCs rose from 44% in 2019 to 67% in 2020, with 75% of 200-plus-bed hospitals already owning more than one ASC.3Hospitals view these investments as a way to enhance physician relationships and increase surgical capacity.
The Benefits of Practice Diversification
For anesthesia practices that elect to contract with both hospitals and ASCs, a key benefit is improved profitability, since average ASC case reimbursements are higher than average hospital cases due to better payer mix and more efficient room turnover. Groups that work with multiple organizations also reduce their institutional or operational risk by limiting their exposure to potential financial problems associated with a single contracted entity.
Practices likewise gain an edge when it comes to recruiting in today’s highly competitive anesthesiologist and CRNA market. One of the chief benefits of ASC involvement is being in a position to offer a better work-life balance by spreading call responsibilities across a larger physician call pool. The math is simple: If a hospital group has seven physicians, each must provide call coverage once a week. But if the group also contracts with five ASCs and brings on five additional doctors to staff the facilities, individual call responsibilities are reduced to once every 12 days.
The importance of mitigating call duties to improve the work-life balance for both experienced clinicians and new hires can’t be overstated, particularly as hospitals work to streamline OR throughput by increasing the number of surgical procedures. Groups can also explore a range of creative compensation approaches, including essentially selling call opportunities to newly hired or recent graduate anesthesiologists as additional avenues to attract qualified clinicians while easing the burden on senior anesthesiologists.
Equity Opportunities
Among the most intriguing aspects of ASC involvement is the potential for becoming an equity stakeholder in the business. Surgeons traditionally have been the primary drivers in creating ASCs, but new opportunities exist for anesthesiology groups, particularly if their hospital is buying an existing ASC or developing a new ASC venture and looking to diversify the ownership group.
The idea of anesthesia ownership isn’t as crazy as it might sound. Like surgeons, anesthesiologists are integral to the success of an ASC, and like surgeons, they get there early and stay late. It’s no secret that joint ownership can greatly improve relations between the practice and the hospital, since both are now working toward the same objectives.
Groups can also make more money. I met with a surgical group not long ago with a 49% ownership stake in a hospital. That equity generated an additional $80,000 per year for each physician partner. How much you can make, of course, depends on your specialty, your level of ownership, and the volume of business. But you’ll never know until you try.
Outside Expertise
The pandemic has unleashed numerous changes throughout healthcare, and where the dust will eventually settle isn’t entirely clear. But what is certain is that for organizations to remain viable, they’ll need to be flexible and look hard at nontraditional business opportunities. Contracting with both hospitals and ASCs represents one such approach for anesthesia groups.
If you’re interested in exploring this and other business possibilities but don’t know where to start, Change Healthcare can help. Our team of expert anesthesia practice-management consultants have an average of 18 years’ experience in the specialty. We can be engaged on a per-project basis or we can provide our consultant services as part of our turnkey anesthesia-billing solution.
Our anesthesia revenue cycle management services can be deployed either on our own proprietary anesthesia-billing platform or on your hospital billing system. Either way, we’ll provide seamless, end-to-end service.
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