Health Technology
Article | September 12, 2023
Artificial Intelligence or AI has attained continuous evolution over the years and witnessed widespread adoption across major industries of the globe. The Forbes report of December 2021mentions that the number of AI startups since 2000 has increased 14 times, and investments in AI startups have grown six times. It underlines the fact that the AI industry, powered by its path-breaking developments and innovations, has always been an attractive and trending option in the market.
Within a very few years, AI has taken over different segments of healthcare like wellness, early detection, diagnosis, decision making, treatment, research, training, public health functions (surveillance and outbreak response), virtual care etc. A study by Accenture claims that AI-enabled devices and gadgets meet 20% of the clinical demands, and this has reduced the unnecessary visits to hospitals by a great number.
Applications of AI in healthcare is broadly categorised into 3 segments, namely, Patient-oriented AI; Clinician-oriented AI; and Administrative-oriented AI. The transformative role of AI in healthcare is undeniable, as it scripts new journey for patients and practitioners, alike.
According to Healthcare IT News, 63% of the research subjects agree to the observation that the devices and machinery enabled by AI have provided excellent value to the specialty healthcare divisions like radiology, generic pharmacy, pathology, etc.
The rapid growth of AI in highly delicate domains like healthcare calls for great promise to accelerate diagnosis and treatment. At the same time, it also puts ethics, patient safety and privacy concerns at the heart of it; thereby calling for a framework of governance. Gartner report of July 2019 predicted the application of AI in more than 75% of the healthcare delivery organizations (HDOs) around the globe.
Since most of these HDOs are new to adopting and applying AI-enabled machinery and services, AI governance is crucial to prevent the actions that may lead to errors, misjudgements and further chaos. Moreover, the degree of variance in the application of AI is high, and therefore it is not advised to implement the AI mechanisms without proper guidance or governance.
From AI-enabled smart bands to pacemakers, the range of devices and gadgets offered by the AI industry is simply remarkable. The implementation of AI in the healthcare sector has proven to be highly effective in drastically reducing the scope of slipups. Moreover, AI has also facilitated early detection of illness with the help of daily use gadgets and devices in a smart way.
At this juncture, it is equally important to create data governance framework that ensure ethical principles are applied to patient, providers and payers’ data. Further, AI initiatives by healthcare providers should be created using transparent protocols, auditable methodologies and metadata. These technologies should do no harm, reduce biases and help patients make informed decisions about their care.
A significant part of AI governance also lies in change management. To build trust towards AI’s adoption across the healthcare ecosystem, there should be a dialogue between clinicians, scientists, technologist and end-users. Such discussions will address the opportunities, value and investment, including concerns across the stakeholders.
In fact, prominent think tanks suggest healthcare providers to establish an AI Governance Council to monitor the value, investment and use of strategic AI capabilities. Some of the crucial roles and responsibilities for the Council include addressing legal and regulatory compliance; clinical evaluations; ethical usage guidelines and organisational deployment of AI across the system.
AI is indeed a revolutionary technology that has huge surprises up its sleeves for the future. But exploring new frontiers comes with its fair share of challenges. Establishing appropriate governance over AI implementation and initiating a conversation around the ethical implications and regulations as well, will play a fundamental role in the introduction and scale-up of AI in healthcare.
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Health Technology, Digital Healthcare
Article | September 8, 2023
Do you know a friend or loved one who suffers from fear, anxiety, and depression and do not know what to do to help them?
It can be frustrating to watch someone you know struggle with their mental health and not be able to do anything to relieve their suffering.
With this in mind, here are seven ways to help the person cope in these kinds of situations.
1. Learn as much as you can in managing anxiety and depression: There are many books and information that will educate you on how to successfully overcome fear and anxiety. Share this information with the individual who is struggling with their fears. The key is to get your friend to understand how important it is to seek some guidance when it comes to their mental health.
2. Be understanding and patient with the person struggling with their fears: Maintaining depression and anxiety can be difficult for the individual so do not add more problems than what is already there. Do not get into arguments with your friend who may be having a difficult time with their anxieties. Make an effort to listen to the person rather than making judgements.
3. Talk to the person instead of talking at them: It is important not to lecture the individual who’s having a hard time with anxiety and depression. Talk to the person about their issues without being rude. Most people will listen if you approach them in a proper manner. Remember to treat others the way you would want to be treated if you were the one who was struggling.
4. Ask for some ideas: Seek advice from a professional who can assist the person you know with their mental health issues. A counsellor can give you some ideas on how to overcome anxiety, fear, and depression. Getting help from a therapist is the number one priority in getting the individual to do something about their problems.
5. Find out why the person won’t get assistance: Address the issues on why he or she will not seek treatment. Many people who are struggling are fearful and frustrated. Try to find out the reasons why your friend won’t get the help they need and then try to find the ways that will overcome their resistance of seeking some guidance.
6. Remind the person on the consequences of not getting help: Another way to convince the individual who is struggling with fear and depression is to tell them what may happen if they don’t get some counselling. Anxiety and depression can make things worse and usually won’t go away by themselves.
7. You can’t manage your mental health all by yourself: A person’s fears and anxieties can be difficult to manage and more than likely he or she will need some help. Many people think that they can overcome their mental health problems on their own. This is a mistake. The individual should admit they have a problem and then seek treatment to get their life back on track.
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Health Technology, AI
Article | July 18, 2023
Healthcare is top of mind as the coronavirus hits hard everywhere. The inefficiencies of the system itself are on full display during the pandemic — where testing is hard to come by, diagnoses and treatments are reactive rather than proactive, and many people do not get the care they need, when they need it. Adrian Aoun, CEO and founder of Forward, a tech-driven healthcare startup, told Karen Webster that it’s possible to build a completely new healthcare ecosystem, beginning with primary care — and the overhaul needs to leverage data and artificial intelligence (AI).
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Digital Healthcare
Article | December 18, 2021
“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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