Embracing Digital Transformation in Healthcare

Healthcare in digital transformation: digital and connected healthcare

Data-driven digital transformation opens opportunities for healthcare providers, policy makers and patients to move toward personalized healthcare by collecting and sharing new kinds of data. The next wave of productivity gains will not come simply from the delivery of information and messages from one place to another, but from the cross-linked aggregation of a more complete body of information. While the transition requires an investment in new technologies and new ways of doing business, the tools are rapidly maturing, and costs are coming down.

Denmark has been at the forefront of health data exchange for more than two decades. It began in 1994 with the creation of Med Com, a nonprofit organization owned by the Ministry of Health and various local government entities, which designed a range of healthcare data exchange standards. Med Com also enforced a strict policy of compliance, which led to countrywide adoption.

The initiative established Denmark as a global leader in data sharing and in empowering patients to be more involved in their own treatment. A key aspect of the country’s digital health initiative is its web portal, Sundhed.dk (or health.dk), which gives patients secure access to health data, including information on their treatments, visits to their doctors and notes from their hospital records.

Now the rest of the world has caught up, and the old standards are competing with those that have global reach, such as Health Level Seven International (HL7)’s Fast Healthcare Interoperability Resources (FHIR) standard. The new standards offer new data-sharing options and provide a far richer and more impactful set of options to healthcare providers.

While messages to and from clinical applications still have relevance, data sharing capabilities that enable true and actionable insights are growing in importance. The new data sharing models have the potential to transform healthcare, supporting digital transformation and moving healthcare toward progressive business models. The ability to share clear, consistent patient data is integral to driving patient-centric care, since patients now demand that healthcare organizations interact with them through multiple communication channels and have a deep understanding of factors that may affect their health.

But is this paradigm shift from messages toward rich data consumption easy for providers to adopt? Well no, if you still treat your electronic health records (EHRs), radiology information system (RIS) and laboratory information system (LIS) as big monoliths and data repositories where isolated and specific data is shared as messages.

Accelerating digitalisation in healthcare

Let me give an example. When I was a hospital chief information officer and wanted some new functionality in our EHR system, I needed to go through several hospital and vendor approval processes. In the end, it might take 2 years to get the change implemented, since the development roadmap didn’t leave much room for my innovative ideas. What I needed — but did not realize at the time — was access to data outside the applications where the data resided. The problem is that the apps themselves are not built for data sharing purposes and yet they contain vast amounts of invaluable data from across the enterprise — clinical, administrative, logistics, infrastructure, etc. In the past, medical use cases tended to be drawn from a single source, such as the EHR, but today’s use cases draw data from apps, medical devices and perhaps even sensors.

Here’s a problem that hospitals encounter — the outbreak of a contagious disease. To quickly mitigate a health crisis, the hospital needs to know within 12 hours what items and which people have been exposed: medical devices, staff, relatives, etc. To gain that insight, those managing the problem need data from a real-time location system, booking data, clinical data and data from a medical device database. But how do you make sure that the data is accessible outside of those apps? This is what digital transformation is about in healthcare — to set the data free and transform through innovation, with actionable insights, advanced analytics and other cutting-edge capabilities that are built upon your data.

It’s not only hospitals and clinicians who need these advanced insights. Today’s empowered patients require those insights to improve and manage their own care — whether that’s insights from digital devices for remote monitoring of their conditions or communicating with clinicians to help drive personal
health goals.

I believe it is time for health economies to look at how they will integrate and connect their existing systems with new digital technologies and merge the data locked inside to generate meaningful, actionable insights — both to inform personalized, patient-driven clinical care and to push the development of new treatments, pathways or services. Organizations that embrace change and transformation will emerge as winners in a world that demands first-class clinical care, better patient experiences and reduced costs.

While Denmark has led the way on health data exchange, advanced global standards and new digital technologies create the landscape for all health economies to embrace patient-centered care initiatives enabled by connecting data across the broader health ecosystem.

Far off the Clinical Silo: Presenting Comprehensive data

Transportable Silos - Ahrens Rural

For the past 15 years, the electronic health record (EHR) has been the cornerstone of digital hospitals and a primary repository for clinical data. The strategy for most healthcare providers has been to integrate as much data as possible into the EHR so that clinicians need to work with only “one single source of truth” when treating their patients.

However, there is a problem with EHRs. They are simply not designed to incorporate data from other sources, such as medical devices, asset management systems, location services, wearables or any other secondary non-clinical data. These sources of data offer tremendous value for improving health outcomes but, because of the difficulty in incorporating these data sources, are not being used.

In the coming years, most healthcare providers will prioritize aggregating new data sources (and thereby exploring new use cases). They will be seeking to not only implement new business models, but also to leverage the investments they’ve already made in new capabilities and technologies centered around internet of things (IoT) platforms, artificial intelligence, big data and robotics.

Storing such data in a single clinical silo, like an EHR, is neither practical nor efficient. Rather, healthcare providers need to focus on data aggregation and orchestration platforms (DAOP) that can collect data across the ecosystem and deliver actionable insights which will impact the day-to-day delivery of care. These platforms help move healthcare providers away from a reactive approach to managing healthcare data to one that’s more proactive, automated and insights-driven.

This is because the latest generation of DAOPs is architected as a collection of different open, modular and granular services, provisioned using the cloud and delivered as a service, which is enabled thanks to emerging server less designs. As these intelligent layers of software and data continue to be integrated in advances in big data, analytics, artificial intelligence and automation, and root themselves in agile/DevOps practices, they create low-cost, increasingly automated and smart agile workflows.

The EHR remains an important system — but it will be only one source of data feeding the DAOP. It will continue as a digital application focused on documentation, internal work flows and decision support within the hospital, while the DAOP will act as an engine, enabling new and innovative business models across the patient ecosystem.


Data, interoperability and APIs

Accessing and harnessing data across a variety of systems is the core of what the DAOP does. Once the aggregated data has been transformed and ingested into a centralized data lake, it can be consumed as microservices through a structured and well-managed API gateway.

The use of RESTful (Representational State Transfer) APIs, or RESTful web services, combined with the rapidly emerging FHIR (Fast Healthcare Interoperability Resources) standard, is accelerating and standardizing healthcare interoperability. FHIR-based RESTful APIs enable healthcare data-sharing across a federated and fragmented environment without necessitating data migration or locking up data in centralized solutions, such as EHRs. This is important because locking up data in EHRs and other such systems can reduce an organization’s control of the data and lead to prolonged time to value from new ideas and innovations.

apis in health care

Setting a complete healthcare API strategy

FHIR-based RESTful APIs are just the tip of the iceberg. On their own, they do not solve problems like data fragmentation, lack of standardization or sub-optimization across a complex ecosystem. To fully leverage the benefits of FHIR and APIs, more must be done than simply adding an API on top of an existing system of record, because the raw data is typically not in a state or format that lends itself to interoperability without manipulation. Most systems of records, such as the EHR, store data in a proprietary format, which needs to be manipulated before it can be shared. Maintaining a loose coupling of applications is best done outside of the EHR and enabled by a FHIR-based data lake.

To be successful, a complete healthcare API strategy should:

  1. Address the differences between the raw data from the systems of records and the data requirements of FHIR and other interoperability standards. Data transformation and translation against industry standards helps to standardize the data into a canonical form.
  2. Hide the complexity of the underlying data environment to the data consumer. APIs and the platform behind them provide the opportunity to encapsulate the systems of record so the end user doesn’t have to search through multiple systems to find the data.
  3. Consider opportunities to improve and enrich the raw data to maximize its use. Once data is aggregated and standardized behind the API, questions can be asked of the data that could not be asked before. Opportunities exist to enrich the data with provenance, security, privacy, conformance and other types of metadata. Enrichment can also come in the form of new insights gleaned from the data through analytics and evaluation of the data against knowledge bases.

APIs should be the gateway into a robust data processing platform capable of maximizing the usefulness of the data behind it. In this way, the challenges EHRs and other systems of record present with aggregating important sources of data can be overcome without undue burden and cost to the healthcare organization.

Colors in Health

Do you know why doctors wear a green/blue surgical scrub while operating? Have you ever wondered why walls are muted in color at adult patient rooms and brightly colored at children recovery rooms? The reason is colors influence human behavior. They affect our mood and emotions. They can even alter our perceptions or modify our actions.

For instance, blue and green are cool and calming colors. They bring a sense of tranquility and put patients at ease while they are on the operation table. Similarly, white is a color associated with warmth and peace. Colors like white, beige, have a calming effect on patients under intensive care. Likewise, blue, green, yellow and pink are suited for pediatric units where children are recovering.

The choice of colors make an impact in physical spaces as well as digital products.

Colors evoke different emotions in people according to their age, environment and condition. Therefore, it’s important to consider color theory while designing healthcare products.

So, let’s see how we can use the right colors to heal and comfort our end users.

Prescription delivery apps

Anteelo design. Healthcare

Amazon has changed the entire delivery gamut. Most people have a mindset of “why should I go out if I can order it from my mobile phone”. The basic feature of prescription delivery apps is to ensure easy access to medications on need-basis. Users may also need to consult with the staff remotely, or ask for a refill of their prescription.

For such kinds of apps, green and blue are the best choice of colors. They are refreshing, not too harsh on the senses and encourage concentration. These colors also work well for the pharmacists who are processing a lot of prescriptions by the hour. So, these colors help them concentrate on small details and deliver the right medication to intended recipients.


Informational and health communities apps

How mHealth Trends Are Improving Healthcare | Dogtown Media


Mobile apps that share informative healthcare articles have just one aim- to make people more aware of diseases, medications, treatment methodology, and so on. They want to make users in charge of their own health by disseminating information on their portal.

In such applications, we need to give users a control over what content they want to consume. We also have to ensure that the app functions well for all age groups. Including too many colors might hinder readability for elderly users whose vision declines with age.

The primary color of such apps should be white, followed by blue and yellow. White is a color that denotes cleanliness, freshness, and simplicity. You can use shades of blue and yellow to convey depth and power. Yellow is associated with positivity and warmth. These colors add liveliness and encourage people to keep reading.


Patient-engagement apps

How mHealth Technology Supports Patient Engagement Strategies

Patient engagement apps work at various levels- they connect patients with doctors, through online consultation. They remind patients when it’s time to take medications. They encourage patients to exercise regularly. They keep a track of insurance renewal, and so on.

These apps also store sensitive information and personal health data. Therefore, the app colors must invoke a feeling of security as well as encouragement. These patient engagement apps aim to make users accountable for their own health. Therefore, colors that are soft and neutral must be used.

Orange is a color that signifies health, happiness, and encouragement. Green is the color of nature and it denotes freshness, and progress. One can even use shades of pink as they represent kindness and affection. Darker shades of pink or orange can be used in designing medication reminders and pills tracker.


Personal wellness apps

12 Motivating Wellness Apps To Digitise Your Wellbeing

A major shift has happened in the way we manage our health. With apps that can monitor sleep, calories, heartbeats, and even steps, personal wellness apps have an increasing consumer demand.

Personal wellness apps help in motivating people to track their vitals, self-monitor critical signs of illness, and stay fit. Therefore the colors in these apps should give a playful, cheerful and cool vibe. Shades of purple, blue, and pink are the right colors for these kinds of wellness apps.

Purple denotes ambition and devotion which fits well with the mindset to track and improve health. Blue and pink add the coolness and kindness quotient. Pink exudes the message of playfulness as well as tenderness.


Remote consultation and monitoring apps

How remote-patient monitoring will change healthcare | by Robert L. Longyear III | The Startup | Medium

Remote consultation and health monitoring apps help in keeping the patient connected with their doctors. They are especially useful for the elderly or disabled who can’t visit the doctor’s clinic regularly.

In such apps, one important thing to consider is the choice of colors for all age groups. Color perception changes with age and demographics. What works for middle-aged people wouldn’t necessarily work with elderly. So, during the user research phase, identify your user persona and decide who your target audience is. People in their 30’s or 40’s might prefer bright and vibrant colors that are playful and cheery. On the other hand, older people are likely to enjoy softer, soothing colors like beiges and whites.


EHR apps

Role of mHealth Apps in Healthcare Evolution from 1.0 to 3.0

Patients are more informed now than ever. Technology has changed the way healthcare data is accessed and inferred. The end users want to analyze their personal health data and take control of their health. They also want to keep their privacy intact.

This means that the color palette of the application should give a confident, trustworthy and cool vibe. So use neutral colors and a lot of white space so that the users can access important information in a hassle-free way. You can also use warm tones to show graphical information.

In conclusion, I would just say start by reading about the psychology of colors. Understand the color wheel. Deep dive into various factors that affect people’s perception. All this will help you in deciding the best color combinations for your app. Colors that are well thought out after doing research on the demographics of the app, are timeless. They always attract new users and keep them engaged, no matter which trend appears or disappears.

AI’s Possibilities in Healthcare: A Journey into the Future

Artificial intelligence in health care

Artificial intelligence (AI), machine learning and deep learning have become entrenched in the professional world. AI-style capabilities are being embraced and developed globally (over 26 countries/regions have or are working on a national AI strategy) for many different purposes — from ethics, policies and education to security, technology and industry, the scope is broad and multi-faceted. If, like many others, you are unclear as to what this new terminology means, below is a diagram depicting the hierarchy of AI, machine learning and deep learning for you to consider. In healthcare, the opportunities are vast and significant. Just from a financial point of view, AI has the potential to bring material cost savings to the industry.

But where should you start, and where do the opportunities lie?

AI And Human Accountability In Healthcare

Where to start with AI

First, look at where money is invested — in other words, which start-ups are attracting investors and what is their focus. Rock Health (the first venture fund dedicated to digital health) shows that the top four areas for venture capital investment between 2011 and 2017 were research and development, population health management, clinical workflow and health benefits administration. More than $2.7 billion was invested over 6 years, across 206 start-ups.

Another venture capital and digital health community, Startup Health, which also keeps track of global investments, found that funding is doubling every year for companies which use machine learning technology to enhance health solutions. The companies that focused on diagnostics or screening, clinical decision support and drug discovery tools received the largest share of funding for machine learning in 2018 — i.e., $940 million.

Delving into AI’s opportunities

Perhaps the biggest opportunity lies in assisted robotic surgery, with a potential cost saving of US$40 billion per year. AI-enabled robots can assist surgical procedures by analyzing data from pre-op medical records and past operations to guide a surgeon’s instrument during surgery and to highlight new surgical procedures. The potential benefit to the healthcare organization and the patient from this approach is noteworthy: a 21 per cent reduction in length of hospital stay because robotic-assisted surgery ensures a minimally invasive procedure, thus reducing the patient’s need to stay in the hospital longer.

Surgical complications were found to be dramatically reduced, according to one study into AI-assisted robotic procedures involving 379 orthopedic patients. Robotic surgery has been used for eye surgery and heart surgery. For example, heart surgeons have used a miniature robot, called the Heart Lander, to carry out mapping and treatment over the surface of the heart.

Another valuable use of AI is in virtual nursing assistants. One example is Molly, an AI-enabled virtual nurse that has been designed to help patients manage their chronic illnesses or deal with post-surgery requirements. According to a Harvard Business Review article, assistants like Molly could save the healthcare industry as much as US $20 billion annually.

Diagnosis is another exciting development for AI, with some promising findings on the use of an AI algorithm to detect skin cancers. A Stanford University report found that deep convolutional networks (CNNs) performed as well as dermatologists in classifying skin lesions. Other exciting breakthroughs in AI-assisted diagnosis include a deep-learning program that listens to emergency calls, analyses what is said, tone of voice and background noises to determine whether the patient is having cardiac arrest. Astonishingly, a study from the University of Copenhagen found the AI assistant was right 93% of the time, compared with 73% of the time for human dispatchers.

A fourth potential use for AI lies in digital image analysis, which could help to improve future radiology tools. In one example, a team of researchers from MIT developed an algorithm to rapidly register brain scans and other 3-D images. The result reduces the time to register scans with accuracy comparable to that of state-of-the-art systems.

With so much potential to be gained from AI, healthcare organizations will need to enhance their skills in AI and related capabilities. Decision-makers need to inform themselves about the potential and what is required to achieve those objectives, and then ensure that their teams are properly trained. Culture change in understanding how AI can be used to solve current and future problems is paramount to the future of next-generation healthcare and life sciences organizations.

Significance of Data ethics in healthcare

Is medicine ready for artificial intelligence? | ETH Zurich

Over the past few years, Facebook has been in several media storms concerning the way user data is processed. The problem is not that Facebook has stored and aggregated huge amounts of data. The problem is how the company has used and, especially, shared the data in its ecosystem — sometimes without formal consent or by long and difficult-to-understand user agreements.

Having secure access to large amounts of data is crucial if we are to leverage the opportunities of new technologies like artificial intelligence and machine learning. This is particularly true in healthcare, where the ability to leverage real-world data from multiple sources — claims, electronic health records and other patient-specific information — can revolutionize decision-making processes across the healthcare ecosystem.

Healthcare organizations are eager to tap into patient healthcare data to get actionable insights that can help track compliance, determine outcomes with greater certainty and personalize patient care. Life sciences companies can use anonymized patient data to improve drug development — real-world evidence is advancing opportunities to improve outcomes and expand on research into new therapies. But with this ability comes an even greater need to ensure that patients’ data is safeguarded.

Trust — a crucial commodity

The data economy of the future is based on one crucial premise: trust. I, as a citizen or consumer, need to trust that you will handle my data safely and protect my privacy. I need to trust that you will not gather more data than I have authorized. And finally, I have to trust that you will use the data only for the agreed-upon purposes. If you consciously or even inadvertently break our mutual understanding, you will lose my loyalty and perhaps even the most valuable commodity — access to all my personal data.

Unfortunately, the Facebook case is not unique. Breaches of the European Union’s General Data Protection Regulation (GDPR) leading to huge fines are reported almost daily. What’s more, the continual breaches and noncompliance are affecting the credibility of and trust in software vendors. It’s not surprising that citizens don’t trust companies and public institutions to handle their personal data properly.

The challenge is to embrace new technology while at the same time acting as a digitally responsible society. Evangelizing new technology and preaching only the positive elements are not the way forward. As a society we must make sure that privacy, security, and ethical and moral elements go hand in hand with technology adoption. This social maturity curve might now follow Moore’s law about the extremely rapid growth of computing power, which means that — regardless of whether society has adapted — digital advancement will prevail.  But we can’t simply have conversations that preach the value of new technology without addressing how it will impact us as a community or as citizens.

Trust is a crucial commodity, and ensuring that trust means demonstrating an ethical approach to the collection, storage and handling of data. If users don’t trust that their data will be processed in keeping with current privacy legislation, the opportunities to leverage large amounts of data to advance important goals — such as real-world data to improve healthcare outcomes or to advance research in drug development — will not be realized. Consumers will quickly turn their backs on vendors and solutions they do not trust — and for good reason!

Rigorous approach to privacy

Health Data Privacy: Updating HIPAA to match today's technology challenges - Science in the NewsEthics and trust have become new prerequisites for technology providers trying to create a competitive advantage in the digital industry, and only the most ethical companies will succeed. Governments, vendors and others in the data industry must take a rigorous approach to security and privacy to ensure that trust. And healthcare and other organizations looking to work with software vendors and service providers must consider their choices carefully. Key considerations when acquiring digital solutions include:

  • How should I evaluate future vendors when it comes to security and data ethics?
  • How can I use existing data in new contexts, and what will a roadmap toward new data-based solutions look like? How will my legacy applications fit into this new strategy?
  • How will data ethics and security be reflected in my digital products, and how should access to data be managed?
  • How can I ensure I am engaging with a vendor that understands not only its products but can also handle managed security services or other cyber security and privacy requirements before any breach occurs?

Using technology to create an advantage is no longer about collecting and storing data; it’s about how to handle the data and understand the impact that data solutions will have on our society. In healthcare — where consumers expect their data to be used to help them in their journey to good health and wellness — that’s especially true. Healthcare organizations need to demonstrate that they have consumers’ safety, security and well-being at the heart of everything they do.

Design Thinking in Healthcare

Design Thinking, at its core, is a creative process to solve everyday problems with a human-centered approach. While the word ‘creative’ may sound like something do only with designers/artists, the good news is- it’s not. Anyone can implement design thinking. The only thing that you really need is- listen to your customers as people who need your help. Once you understand their needs, their hopes, their fears and the friction they face while dealing with a particular problem- Bang! You are halfway through it.

Let’s hear a story. The story is about a woman named Elisa (yeah! I made that pseudonym). Elisa is an eighty-one-year-old woman suffering from age-related macular degeneration (AMD). When she was told she needs to take an injection in the eye for treatment, she was petrified. And why wouldn’t she? It’s not just any injection on the skin, it’s a needle in the eye. At the age when you are struggling with survival, it’s terrifying to think of ways in which you can go blind.

Why Design Thinking in Healthcare Matters

Apart from this particular case, it’s a fact that many of us dread getting an injection. Diabetic patients go through this painful experience, every day. Sometimes they have to administer these injections themselves, and sometimes they have to deal with a less skilled, less empathetic nurse.

Don’t you think we need a better solution to this? Can’t we develop something which makes this experience less scary? Can we go that extra mile and feel the pain of these patients? Can we somehow make them suffer less than they are already suffering?

An organization called Portal Instruments has now challenged this 160-year-old needle & syringe technology with design thinking. They have created a needle-free computerized injection system which fires a jet of liquid into the human skin. The handheld, low-cost unit is highly precise and accurate. The device is easy to use and its digital health features empower the patients to holistically manage their chronic condition interactively.

Design, particularly in healthcare, is about efficiency, usability, and a better user experience for patients as well as medical practitioners. And Design Thinking is a very powerful approach to solve customer’s problems. So where can you apply design thinking in healthcare?

Design Thinking in Patient Care

How Healthcare Organizations Can Start to Use Design Thinking | PreCheck

Patient care is not just about exchanging pleasantries and moving ahead with the treatment. When you apply Design thinking to this process, you will uncover ways in which care goes beyond the treatment.
A customer empathy map will help you understand your patient’s pain, concerns, fears and go beyond the clinical treatment. For instance, simply by listening to the concerns of expectant mothers, you can help them ease their anxiety. After quality research & brainstorming viable solutions, you can arrive at a proposed solution to help them be better informed about the labor process.

Design Thinking in Clinical Experience

Memorize the last time you were sitting in the emergency-room and recollect your waiting experience. Wait times are difficult to pass. You are in a troubled state of mind. Patients and their families spend a considerable amount of time in waiting rooms, sometimes waiting to be treated and other times waiting to see the doctor.

Design thinking may bring forth innovative ways of helping patients feel comfortable and make their experience bearable. You can start by asking questions and understanding their mindset. Must the patient be left alone while they wait for care? Is there a better way in which family wait time can be utilized? If you can not reduce the wait time, think of ways to utilize it. Once you answer these questions, you’ll be able to elevate the user experience of your users.

Design Thinking in Websites

If you are building a healthcare app/website, then you have to take care of the reliability and accuracy of the information that you provide. A person’s medical records can be critical information while monitoring health patterns or detecting disease symptoms.
Prioritize the most important information & fields for your users. Boil down to basics. Take all age groups into account and design keeping in mind their ailments.
They (might) want more information with less number of clicks, they (might) wish for larger and readable fonts. And while you may get away with frequent ‘small’ updates on social media apps, here it (might) frustrate them.

How to design a great Healthcare Experience

You know why every superhero is veiled behind a mask? Because creators of comic heroes want you to believe that even superheroes are like any other human. Their only superpower is endurance and resilience. They understand people; they want to solve their problems. They put people before anything else.
Much like Spidey! Or Batman.

Design thinking is same. It’s about organizing those mindful scattered ideas that everybody forgot to care about. Design thinking is about subtle differences which make you outshine from the ordinary. Yet it’s not so easy to put yourself in some else’s shoes. It takes a lot of efforts in brainstorming and generating ideas. Then, you should quickly pivot on a prototype and gather user feedback for continuous improvements.

Design thinking has already made it to healthcare. But, as we all are aware of the sad state of product design and innovation in Healthcare, there are still areas where it remains underused, such as patient transportation, the communication gap between doctors and patients, to name just a few. Here’s one approach that might be useful to you-

Research and define the problem statement

If you are dealing in the food business, wouldn’t you start talking to the farmers? So, start with conversations. Talk to patients/families about their problems.
Build customer personas. A persona is an imaginary character that embodies your real customer. Learn about your user’s lifestyle, their goals, their values, the challenges they face. Empathize with your users & their problems.
If you are designing an online appointment experience, you need to involve every single person associated. Right from the doctor to the patient. Even the receptionist. You need to understand their roles and most importantly, where they fit in together. Once you understand their pain points, then you’ll be able to create the experience for patients who need care.


Design Thinking in Healthcare – IDEO U

Enough talking! Time for some action. Gather all that you have talked and use the outcome of Research phase to generate interesting ideas. Not all ideas will be usable; so try and stay close to ‘potential solutions’. Use techniques like high-level drawings, user-mapping and plot a user’s experience map to arrive at innovative solutions.
For instance, while building a SaaS-based mobile engagement platform for one of our client, our design team took conscious efforts to understand the whole journey- health plan benefits, treatment requirements, appointment details, communication medium, medication instructions etc.

Putting down our ideas on paper helped us a lot in working on user workflows. We were able to visualize a smarter workflow which connects with patients through mobile messaging for more effective communication.

Prototype and iterate

Giving your ideas a shape is crucial to the design thinking process. Otherwise, it will just be castles in the air. Prototyping is something that pushes you into making things tangible so that you keep moving forward.
Prototypes will be a proof of concept of your ‘ideation exercises’. They will help you in demonstrating and validating your concepts and understanding. Moreover, they are important because you would want to test your functionalities in a real environment with real users.

Prototypes need not be beautiful. It can be a black and white template of your colorful understanding. It must answer a simple question- as simple as “How would you like to reach out to your members?”

Depending on your application (web/mobile), prototypes can be interactive or static. What really matters is that they must convey the user experience flow.

The advantage of building a prototype is that it’s something substantial and not just some thought process going on in our mind. Once you have pushed that into a real environment, you can take feedback from users and iterate to simplify functionalities.

Designing for healthcare won’t be a joyride. Unlike social media apps like Snapchat, your healthcare platform will grow slowly. And that’s not your mistake. The user base that you are catering to, is not looking for socializing or entertainment. So the only solution lies in applying design thinking to approach problems.

Before aiming for success, first, offer a service that’s valuable. Offer a service that solves a real problem. Offer a service which makes them forget that they are interacting with a machine.
Let’s build a better healthcare experience. Let’s be more human.

AI Chatbots in Healthcare: UX Research

Designing conversational UI is a challenging task. I say this from my experience of designing a conversational AI chatbot in healthcare. From the moment I began working on it, I knew it wouldn’t be an easy feat. Questions like what kind of visual elements would I use, how can I reduce the user’s cognitive load in effort-intensive activities, were always on my mind. Prior to this, I had worked on UI design of many web and mobile apps. But none of them was as challenging as this one.How A Chatbot Can Help Your Healthcare Business | by Michelle Parayil | Chatbots LifeThe most challenging part for me was designing to handle the human-machine interaction. Each user is different. Unlike in websites/apps, where users can simply browse and leave, the chatbot users open the chat window to interact. They come with all sorts of questions- vague /smart /genuine /rogue /irrelevant and (sometimes) absurd. When they type a query, they expect the conversational UI to adapt to their needs–digest questions and construct intelligent answers/follow up questions.

The uncertainty of the usage makes the design process complex. Unlike websites/applications, there are no specific UI design principles for designing conversational interfaces. It might appear as a small thing but the limited knowledge on the UI design patterns for healthcare chatbots ultimately affects the customer experience.

So, what can a designer do to make sure that the conversational AI solution caters to most, if not every, user persona? I can share a few suggestions. Since I worked on a healthcare chatbot, most of my suggestions would be about best design practices for healthcare conversational user interface.

Chatbots are now making their way into healthcare solutions like patient engagement solutions, handling emergency situations or first aid, medication management, and so on. To create a great user experience, it’s crucial to pay attention to the process of designing the chatbot. One of the ways to do this is streamlining the process of UI design through UX research.

When I started working on UX research, I borrowed a few tried-and-tested methods applied in web and mobile apps. However, healthcare is a complex domain where data security is a major concern. Therefore, I modified a few of them to suit my needs.

Let’s talk about a few UX research methods which are important to conduct before deep-diving into UI design of a healthcare chatbot.

AI in healthcare | Artificial Intelligence in the healthcare industry

Discover users’ pain points

To discover users’ pain points, you must first know who your users are. So, the first step to any good UX research is defining your user persona. Your user persona should include demographics profiles, health profiles and task profiles.

The persona diagram must include needs, difficulties, frustrations, motivations, aspirations of your end users. For instance, a 56-year old woman’s persona should include pain points like– “I’m an old woman, I don’t have the patience to repeat the same thing over and over again” or “I am ageing towards dyslexia, I forget conversations I had 15 minutes ago.”

After you’ve defined your user persona, the next step is to figure out how they will interact with the chatbot. For that you can invest in any of the below attitudinal approaches-

  • Gather inputs by rolling out surveys to your target audience and asking them related questions.
  • Conduct interviews and listen to what users say.

Depending on the kind of healthcare chatbot, your choice of the method will change.

For example- if you’re doing UX research for a chatbot that guides people towards better mental health, then you can do anonymous email surveys as people don’t openly talk about issues like depression and anxiety.

How Chatbots Can Help Your Healthcare Industry? | BCC Healthcare

Observe users in their natural environment

Direct observation (a primary research method) is the key to understanding user needs and preferences for a new product. During observations, record verbal comments and the time spent on various tasks.

For example, if you’re designing a chatbot for surgeons, then observe them when they plan the pre and post surgical procedure. If the patient has a lot of complications, what dietary guidance do they offer? How do they perform the surgery? What instruments they use and how its usage differs from surgery to surgery?

By conducting interviews with people while they perform tasks, you can collect valuable inputs from them and use it to recreate a similar experience in a conversational chatbot.

You can also create interactive mock-ups to show artificial interactions. This gives sufficient scope to test the chatbot with users without requiring the engineers to actually build it. This helps in quick iterations with the users after validating the user experience and understanding their perception of the chatbot.

Research through complementary data gathering techniques

A quick and rewarding UX research method is secondary research. This is done by doing competitor analysis to see how others are solving the same problem. This approach isn’t useful if you’re developing something unique. But, if it’s something you are trying to improve, this method is easy and gives quick results.

You can experience real conversations, access failure threads and use data to improve your chatbot’s experience. Secondary research also includes searching through customer service logs, FAQs, online reviews or comments on blogs.

For example- if you’re building a patient registration chatbot, look at other chatbots available in the market. What is the tone and personality of chatbot? How much time does it take to book an appointment on the chatbot? What are the demographics that the chatbot covers?

Study the chatbot as a user and find out things that they are doing right or things where the experience could be improved. You will find some unsolved problems that could become a major feature in your chatbot.

Get help from stakeholders

There may be situations where you wouldn’t get time to do surveys and interviews with end users. In such cases, ask for help from people who want to build this chatbot. Get to the behind-the-scenes of the problem. Go to their sales and marketing calls. Understand the ‘why’ behind building a chatbot and what problems they are trying to solve.

Talk to the sales team to understand what kind of customer support calls/tickets they receive frequently. Interview their marketing team to understand what vision they have for their end users. What motivates their users? What do they need in order to be happy? What’s their idea of a good chatbot?

Gather real quotes/statements from the end users and use them to draw an empathy map and user journeys. This will help you identify major pitfalls and crucial moments.

The UX research phase is a crucial part of developing a great customer experience. When it’s given its fair share of time and effort, UX research helps discover important insights and reduces the number of iterations required to build the chatbot.

However, there’s no surety that your research findings will translate into a flawless user experience. All the research findings must be implemented and tested in real-time for you to discover if your research was right or not. Sometimes, a wrong method of research or the timing of the research may give you erroneous information.

I hope you figure out the right UX research method for your chatbot. If you have worked on a healthcare chatbot, I would love to hear the UX research methods you used.

Secret to Patient Encounter: Never Skip Small Talk!

Small talk is delightful. It’s an easygoing, inconsequential conversation where you’re not running to reach an answer. It flows like wind, from one talker to the next. Small talk appears trivial, but it’s a way for strangers to know each other and for friends to bond over little details.In office, people indulge in small talk during watercooler run-ins, in elevators, and in big meetings where they don’t know each other. Small talk is used to break the ice with casual questions like ‘how are you?’, ‘How’s the weather in your city?’, ‘How was your day?’.In human conversations, small talk serves many purposes. It’s like a bonding ritual. If the two people are meeting for the first time, it acts as a conversation starter. But if they already know each other, it serves as a conversation kickstarter before diving into the real conversation. Small talk is the foundation of good conversations and great relationships.

In a chatbot, small talk enhances the user experience by bringing a feeling of connection. By adding answers to inputs like ‘How are you?’ and ‘Are you really a robot?” in the chatbot’s architecture, we make it less robotic and more humane. We give our chatbot a human character that builds an invaluable connection with the user.

How can small talk help in a healthcare chatbot?

COVID 19: Driving Chatbot's Growth In HealthCare Industry

A good healthcare chatbot is the one that answers users’ questions with accuracy, timeliness and empathy.

But a great healthcare chatbot is the one that understands the moods of users, urgency of the situation and answers accordingly.

For instance, when you’re panicking about waking up to bloodshot eyes, the chatbot calms you down with some first-aid steps. If you’re in a rush to book an appointment, the chatbot sends you proactive information on which doctor is nearest to your location. If you’re feeling low, the chatbot brightens up your mood with some light-hearted conversation. Just as a good friend would do.

The highlight of the above conversation is the personal touch in the conversation. It doesn’t feel like we’re reading a conversation with a chatbot. That’s the magic of small talk. Phrases like ‘that’s dreadful’, ‘wish you a speedy recovery’, add a human element and creates a connection of care between the bot and the end-user.

Small talk in a healthcare chatbot also helps create trust. It eases them through important and time-sensitive tasks— like emergency calls to ambulances — with empathy. Contrary to this, robotic responses in yes or no and apathetic comprehension of chatbots frustrates users and they go elsewhere in search for answers.

But how can chatbots mimic human-like conversations and engage users in delightful conversation? By leveraging the power of AI and NLP.

How to implement small talk?

Small Talk Dataset for Chatbot - Free Dataset List - The Chatbot Business Framework

There are certain guidelines that one can follow to tailor such conversations.

  • Decide the voice and tone of your chatbot. Every response should reflect the personality of your chatbot.
  • Initially, you’ll have to hardcode some of the small talk in your chatbot. So, brainstorm ways in which users can ask questions. Do some research and make some educated guesses to decide on the most relevant questions that your targeted audience may ask. Cover the edge cases as well– from casual questions to genuine queries that users might ask.
  • Write answers to each question as per your understanding.
  • Keep the small talk brief and clear. Don’t go too far from the crux of the conversation.
  • There can be multiple levels of small talk. Each parent question can have a child query. The child query can only be asked when the parent question has been asked by the user and the chatbot has responded to it.
  • Small talk should always be accompanied by a call to action or a solution. Example- You: “Do you really exist?” Chatbot: “Yes I exist as a computer program. An intelligent human created me, so you can trust me. How can I help you?”
  • Train your chatbot to learn from the conversations of small talk.
  • Small talk is language-specific. So give users an option to talk to the chatbot in their native language.

Small talk makes the interactions with chatbot intuitive. By adding a small talk feature, we can increase the number of conversations and engagement levels of the user with the chatbot. So, if you have a healthcare chatbot, consider incorporating small talk. It doesn’t even require you to do a complete overhaul of your chatbot development.

Dynamic Healthcare System: Blurring barriers between payer and provider

 Dynamic Healthcare System

Recent headlines have been full of news about major healthcare mergers and acquisitions, often involving newcomers to the industry, but also creating a convergence of traditional payerprovider and pharmaceutical benefit management companies.

Here are some of the latest examples in the changing healthcare scene:

CVS Health, a large pharmaceutical benefit manager, is purchasing Aetna, a large insurer, while Cigna, another large insurer, is acquiring Express Scripts, another pharmaceutical benefit manager.

Meanwhile, tech giants Amazon and Apple took some giant steps into the healthcare fray. Amazon entered into a joint venture with Berkshire Hathaway and J.P. Morgan Chase in an effort by all three to control employer costs, and Amazon also purchased PillPack, an online pharmacy company, and expects to expand services after obtaining state licenses. Apple showed its commitment to shake up the healthcare status quo by expanding its personal health record system, partnerships with hospitals and A.C. Wellness centers – all with a goal of gaining greater influence on healthcare consumption.

The convergence moves the industry away from the traditional separation of payers (health insurance companies and self-insured employers) and providers. Typically, payers are defined as the organizations that conduct actuarial analysis and manage financial risk by collecting premiums and managing payments for services delivered. Providers, meanwhile have typically been defined as healthcare practitioners and organizations that deliver and bill for services, including inpatient, outpatient, elective and emergent.

Those narrow definitions have been shaken up in the post-Affordable Care Act (ACA) world. In the past, the focus was on fee-for-service and capitated contracts under which HMOs or managed care organizations paid a fixed amount for its members to a provider. But the ACA moved the emphasis to value-based care, pushing more financial risk onto providers and away from payers. That means insurers and providers also need to consider how they manage pre-existing conditions and use risk scoring to determine the likely needs of their patients, as their approach can make the difference between profitable success and unprofitable failure.

In this new and complex environment, mergers and acquisitions are seen as a way for both providers and payers to build up their capabilities and respond to the need to enhance patient care, improve population health and reduce costs.

For traditional healthcare incumbents, we believe this also means using a “secret” weapon non-traditional players already leverage: data analytics.

Better data and analytics life cycle management can yield the insights payers and providers need to balance their priorities and deliver value-based care.

payer and provider

How to balance risk and patient outcomes

But first, what do all of these changes entail, and how do they take providers and payers beyond their narrower definitions?

In the post-ACA world, providers are looking to take more financial risk as their actuarial capabilities improve. This would allow them to negotiate more effectively with payers to achieve care outcomes objectives while balancing reimbursement and risk.

Payers, meanwhile, are acquiring doctors’ offices and other providers, or combining with retail clinics and other points-of-care to combine care delivery with financial risk management. To accomplish these goals, payers need to take a more active role in managing the healthcare professionals that they employ as well as the patients who visit those practitioners. Having access to the care delivery setting also allows for greater accuracy.

Managing these activities – by both the provider and the payer – needs to go beyond just financial management. It needs to include operational excellence, using robust data analytics to communicate with people and organizations delivering care. It also requires having performance-level agreements and bidirectional communication in place to measure and monitor reasonable objectives set by both payer and provider. Indeed, collaboration and communication will be crucial to overcome tensions that are building as providers try to deliver on value-based contracts. Finding a way to integrate insights from the back-end will help to ensure both the payer and provider perspectives are understood.

Use data to your advantage

A balance between the needs of the provider and the payer – while prioritizing the needs of the patient – will require change management and deeper insights on what works, what doesn’t and how outcomes for all stakeholders can be adjusted and improved. Those insights must be based on hard data, which will require more robust data, analytics and IT infrastructure. Organizations will need to deploy data and analytics life cycle management – including input, ingestion, management, storage and data utility. Integrated workflows make it easy to collect better, well-rounded encounter data, improving how providers work and increasing provider and patient satisfaction.

That data needs to encompass all parts of the healthcare continuum, meaning patient experience as well as provider and payer data. For this to happen, payers and providers must ensure better consumer engagement by spurring patients to take charge of their own care and using the data provided by patients to improve insights. Being able to see the end-to-end experience of the patient can affect the pieces accordingly.

Brave new healthcare environment

This brings us full circle to the changing industry dynamics and the entry of non-traditional players into the healthcare arena, since the big tech players such as Amazon, Apple and Alphabet know how to leverage data analytics to gain customer insights. As healthcare incumbents build and acquire assets, they will need to match these capabilities and build on their own strengths to ensure they aren’t left behind in this brave new healthcare environment.

The Internet of Things aiding Healthcare

Internet of things in healthcare

There’s so much talk across healthcare about electronic medical records (EMRs). For many, it seems to be the answer to every question, solving all problems of healthcare. At a recent Health Information Technology WA (Western Australia) conference in Perth, for example, three plenary speakers on the main stage were touting its benefits. Unfortunately, the reality is quite different.

Looking at global trends and the shift to value-based care, I believe there’s ample reason to question whether electronic medical records (EMRs) are actually the right approach, especially when the objective has changed from a hospital-centric approach to a patient-focused model that goes beyond the walls of the hospital. There’s also every reason to question whether it is a sound investment. For example, since 2011, the United States has spent $38.4 billion implementing 30-year-old EMR technology in hospitals, according to a 2018 Centers for Medicare & Medicaid Services report. Yet despite successfully computerising health practices, data is still largely locked into hospital systems, and sharing data across health systems remains difficult.

With the healthcare model shifting towards prevention and personalized care, providers and payers are rethinking their approach, and instead are turning to technologies such as the internet of things (IoT) to engage patients, improve outcomes and bring down the cost of care.

IoT in Healthcare: Benefits, Use-Cases and Challenges

From patient to customer

One healthcare organization that took a truly innovative approach to a customer-centric healthcare model is an academic health centre based in the United States. Renowned for its population health studies, the centre’s former chief executive officer wanted to engage patients as consumers, based on a simple objective — to keep those with chronic diseases out of hospital.

The project began with the creation of an innovation group, headed by a chief experience officer overseeing a multi-disciplinary team from customer-centric industries, such as hospitality, publishing, entertainment and automobiles. Most notably, there are no technologists from EMR/EHR (electronic health records) vendors within this group. To this progressive team, the health center added clinicians, who were given access to over 30 million patient records dating back 30 years to analyze the social determinants affecting chronic illnesses such as hypertension, diabetes, chronic obstructive pulmonary disease (COPD) and heart disease.

Based on a set of algorithms, the team was able to identify three social determinants that have the greatest impact on chronic disease:

  1. Access to transportation – Can you get to and from your job and school easily?
  2. Access to good food – Do you have access to quality produce or is the only store accessible from your house a 7/11 selling “convenience” food?
  3. Access to education – Is there a good school in your area with good teachers?

But how do you get good information from patients/consumers on these issues, given that surveys typically have low participation, with only 30 to 40 per cent of people taking part?

Mobile apps and IoT devices are part of the solution. Unfortunately, most apps are focused on a single condition or health issue, rather than factors that influence the patient’s overall health: socio-economic determinants, your environment, health behavior, as well as the quality of healthcare you receive.

Three months later, the innovation group released a mobile app as a proof of concept.

As part of the programme, patients were given a kit that included a Microsoft wristband, a Bluetooth blood pressure cuff, inhaler and weight scale, all connected to the app. In addition to health monitoring data, the app also captured data on life style, such as whether the patient smokes, exercises, etc.

Scaling outcomes

The pilot was a huge success, but the next step was to scale it to 4,000 patients, which was going to be another significant challenge, considering that the nurse-to-patient ratio is about one nurse for 20 to 40 patients. So, the centre started looking at customer relationship management (CRM) solutions.

Once the digital platform was in place, the innovation group had to redesign a new operating model that would support these 4,000 patients. After testing a few configurations, the team landed on a “pod” model that consisted of one nurse and two health navigators — non-clinical support staff focused on customer relationship management. Because the system works by exception, the care coordinators are notified by the platform when an interaction with the patient is required. The rest is automated by the platform, sending reminders and analysing patterns by using IoT monitoring devices and advanced predictive analytic models.

Success with such a large group of people requires engaging with patients where they are and in a way they can relate to. Thanks to the data gathered, the center knew a lot about these consumers. For example, they knew that most prefer to be contacted by text messages and most were fans of the show, Game of Thrones. With this knowledge, on the evening of the season finale, they reached out to hypertension patients with a simple message: “Tonight is the big night for Game of Thrones, and we know you might get excited, so don’t forget to take your blood pressure before the show, and take your meds if required. Have a good night and enjoy the show!” As trivial as this seems, it is details like this that engage people and empower them to make life style changes.

After 12 months, the new platform and engagement model has given the center huge insights, including enabling providers to predict future chronic disease patients with high levels of accuracy, and it has delivered significant outcomes. Here are a few numbers that I find very compelling: The centre has achieved 95 per cent of customer satisfaction, a 23 per cent reduction in emergency services costs, and reduced the total cost of care by 36 per cent.

Increasingly, no matter the healthcare model, the objective must be to improve health outcomes and keep patients out of hospital as much as possible, not only because it’s better for the patient but also to improve financial outcomes and allow health centers and hospitals to focus on truly innovative, cutting-edge care delivery. That’s not something that can be achieved with an EMR.

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