PODCAST #14. How to Excel in Strategic Planning for Effective Product Management: Tips from an Industry Expert

During this episode of our Careminds podcast, we discuss the complexities of product management and go-to-market strategies with our guest, Donna Cichani. Donna has a background in product management, A/B testing, and data analysis, and has worked with notable organizations such as Johns Hopkins Medicine, KPMG US, and JP Morgan. Currently, she is the lead product manager at Heal.

Our conversation with Donna covers topics like data analysis and strategic product planning, the differing mindsets between 0 to 1 and one to end product development, and methods to increase user engagement and product optimization. Drawing from her diverse experience in industries like healthcare, technology, banking, and finance, Donna shares her thoughts on the importance of strategic planning in product management.

Defining Success Criteria for Product Stages

When determining the success of a product, you consider both the user perspective and the business perspective. Using the example of an RPM solution called Pulse, designed for chronic disease management at Heal, we can explore the key performance indicators (KPIs) and metrics that matter most.

Firstly, there are patient-centric KPIs that focus on adoption and usage. Monitoring how often users engage with the solution to record their vitals and biometrics is crucial. The main goal is to encourage patients to stay proactive in managing their chronic conditions by using the solution more frequently.

User centricity is key, focusing on how you are improving life and the experience for the end user.

Secondly, clinical outcomes are also important. By tracking improvements in specific health measures, such as A1C levels for diabetic patients or maintaining healthy blood pressure ranges for hypertensive patients, we can gauge the effectiveness of the solution in promoting better health.

Also, business KPIs, such as attribution, play a significant role. For the RPM solution, it is important to know what percentage of patients using the solution are attributed to Heal for their primary care doctors.

Defining the best approach for optimizing a product depends on the specific product and its maturity curve. Take, for example, the RPM solution mentioned earlier. The primary goal of any RPM solution is to encourage users to engage with it consistently and measure their biometrics routinely.

At one point, the team behind the RPM solution considered expanding its features to include medication refill reminders, envisioning a more comprehensive ecosystem for patient monitoring. However, they quickly recognized the importance of perfecting their core RPM capabilities before adding secondary features. By maintaining focus on their core competency, they ensured they wouldn’t dilute the solution’s main purpose.

Optimization often involves considering the user experience, especially when it comes to healthcare solutions. In the case of the RPM solution, refining its core features contributed significantly to increased patient engagement. This example highlights the importance of prioritizing the optimization of a product’s primary functions before expanding its scope.

When to Focus on New Features or Enhancements in Product Development

You should invest heavily in user research as it’s crucial for driving customer adoption and engagement. During the discovery phase, our team spent considerable time observing patients in their natural environments, using existing products like glucometers, and capturing their day-to-day experiences. This research also included understanding how nurses, doctors, and other providers utilized data points during home visits.

By conducting ethnography studies, user research, and interviews, we were able to identify key pain points, which we then translated into enhancements and feature opportunities to drive engagement. To ensure customer adoption, it’s essential to focus on understanding users’ pain points, observe their interactions with your product or similar products, and avoid relying solely on secondary sources or high-level questions.

I don’t think that user research for usability testing ends during the discovery phase.

It’s important to note that user research and usability testing don’t end during the discovery phase. After creating our first prototype, we went through two additional rounds of usability testing to validate our assumptions, identify any flaws in our user flow, and refine the solution iteratively. This process continued up until the launch of the minimum viable product (MVP).

The ability of product managers to remain detached from their original plans, even after investing significant time and effort, is fascinating. When real data no longer supports the initial plan, it’s crucial to let it go, find a new direction, and create a better product that serves users more effectively. This adaptability is an essential aspect of successful product management.

Effective Optimization Techniques & The Best Ways to Apply Them

Optimization techniques focus on understanding existing processes, examining them through the lens of various stakeholders involved in the end-to-end flow, and identifying opportunities for efficiencies. For instance, by analyzing a process that takes 10 days and involves five stakeholders, you can uncover ways to reduce the number of stakeholders or the time each takes to complete their part.

Process mapping, a technique that visually represents the steps involved in a process, helps identify bottlenecks, redundancies, and areas for improvement. A/B testing is another valuable technique, where two different versions of a feature or product are tested with the target audience to determine which performs better.

In my experience, one of the keys to successful optimization is to involve the entire team in the process.

Involving the entire team, including product, engineering, design, sales, and marketing, leads to a more holistic view of challenges and opportunities, ultimately driving better optimization decisions. Keeping the end user in mind is crucial, as the goal is to enhance their experience.

It’s important to acknowledge that the rapid growth of product management as a career has led to a mix of undisputed go-to practices and those still being defined through trial and error. Sharing experiences and learning from others in the community can help navigate this evolving field and contribute to its development.

What Drives a Product Manager: The Exciting Facets of a PM’s Career

Effective management in product management involves three key aspects. First, tailor your approach to the needs of each individual on your team, recognizing that there is no one-size-fits-all solution. Second, invest in the long-term career growth of your team members, extending beyond the scope of your organization, by providing mentorship and opportunities for personal and professional development.

The third aspect involves being able to oversee the work of your team without micromanaging, while still being prepared to jump in and help when necessary. Balancing trust and autonomy with support is essential for successful management.

It’s an exciting time for all the PMs because we are focusing on doing good and building impactful products and services that can make people’s lives better.

In terms of current excitement in the field, AI and machine learning are opening many doors in product management. There’s a rewarding shift in focus in both healthcare and fintech industries. In fintech, increased emphasis on financial literacy and access to banking products for the unbanked population is driving positive change. Meanwhile, healthcare is moving towards value-based care, focusing on preventative measures and overall population health, which reduces costs and the burden on the healthcare system. This is an exciting time for product managers as they work on building impactful products and services that improve people’s lives.

Wrapping Up

As product managers continue to navigate this rapidly evolving field, learning from industry experts like Donna and sharing experiences within the community will be invaluable in driving growth and creating impactful products that make a difference in people’s lives. Key takeaways from our conversation include:

  • Defining success criteria for product stages: It’s crucial to consider both user and business perspectives when determining the success of a product.
  • Focusing on core competencies in optimization: Prioritize optimizing a product’s primary functions before expanding its scope or adding new features.
  • Conducting user research and embracing adaptability: Engage in user research, usability testing, and iterate on your product based on data and feedback, and remain open to change when necessary.
  • Effective management and exciting developments in the field: Tailor your approach to individual team members, invest in their long-term career growth, and maintain a balance between autonomy and support. Embrace the exciting opportunities in AI, machine learning, and the shifting focus of various industries.

WATCH ALSO:

PODCAST #13. The Psychology of Product Management: Unlocking Human Insights & OKRS

PODCAST #12. THE PRODUCT MANAGER’S PATH TO HAELTH TECH INNOVATION: PRODUCT STRATEGY, LEADERSHIP & OKRS

PODCAST #11. THE SKEPTICAL IDEALIST: HOW PRODUCT MANAGERS NAVIGATE HEALTH TECH CHALLENGES

PODCAST #10. WEB 3.0 AND HEALTHCARE: OPPORTUNITIES FOR GROWTH AND COLLABORATION

PODCAST #9. HOW TO SUCCEED IN PRODUCT DEVELOPMENT: ADVICE FROM A PRODUCT MANAGER

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The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.

Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?

Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.

Listen to our podcast to get some useful tips on your next startup.

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Healthcare Chatbot: Improving Telemedicine & Enhancing Patient Communication

The healthcare industry is constantly evolving to meet its customers’ needs. A noteworthy trend that is emerging is the use of chatbots. These computer programs, which use artificial intelligence to automate customer service, make it easier for medical providers and patients to communicate.

Chatbots in healthcare are gaining traction, and research suggests that by 2032, the global market for healthcare chatbots will be worth $944.65 billion. The increase in internet penetration, smart device adoption, and the demand for remote medical assistance drive this market forward.

healthcare chatbot market size

In this article, we’ll cover the three main types of healthcare chatbots, how they are used, their advantages and disadvantages, and which one is right for your organization.

BEST CHATBOT DEVELOPMENT TRENDS AND BUSINESS APPLICATIONS

Primary Categories of Medical Chatbots

Chatbots can be broadly divided into three main categories: clinical support, decision support, and healthcare. Let’s take a closer look at each one.

  • Decision-support chatbots provide medical advice based on the data collected from the patient. They can be used to remind patients of drug interactions, suggested doses, and so on.
  • Clinical support chatbots are developed to offer professional medical advice to doctors, helping them make more accurate diagnoses and treatment plans.
  • Healthcare-focused chatbots are used to promote communication between health providers and patients. These chatbots are commonly employed in healthcare to respond quickly to common queries and provide general medical advice.

How Exactly are AI Chatbots being used in Healthcare?

Chatbots, powered by artificial intelligence, are used in various ways to improve the patient experience and simplify medical procedures. To get a better handle on the application of AI bots in healthcare, check out these examples: 

  • Appointment Booking: 

Chatbots can be integrated with online booking systems, making it a cinch for patients to set up or change visits with their medics. 

  • Virtual Health Guides: 

Chatbots use natural language processing (NLP) to comprehend and answer patient queries. For example, they can give information on common medical conditions and symptoms and even link to electronic health records so people can access their health information.

WHAT IS THE BEST WAY TO CREATE A CHATBOT: PLATFORM VS. CUSTOM 

  • Clinical Studies: 

AI chatbots can assess patients for clinical trial eligibility and supply information about ongoing trials, accelerating the process of enrolling participants and collecting data.

  • Prescription Refills:

Chatbots make it quicker than ever to get refills on prescriptions – no more waiting around.

Chatbots specially designed for mental health are invaluable for those struggling with depression, anxiety, and other issues. They provide a secure outlet for communication and lessen feelings of loneliness.

  • Remote Monitoring:

Thanks to AI chatbot healthcare, remote patient health status monitoring is easier than ever. In addition, wearable devices can now supply data to healthcare providers to keep tabs on potential problems.

HOW MENTAL HEALTH CHATBOTS HANDLE STRESS?


It’s important to note that chatbots are never meant to supplant healthcare professionals – they make their jobs more straightforward and accessible to patients.

 

The Role of Intelligent Chatbots in Healthcare [2023 New Applications]

Health organizations are increasingly turning to chatbots, and this tendency will continue to gain momentum in 2023 and beyond. Some of the novel and creative approaches include the following:

Making a splash in the world of telemedicine is one of the most promising areas of application. Healthcare chatbots provide patients with virtual medical consultations and advice so they can avoid leaving the coziness of their homes to get professional assistance.

Chatbots can also be handy in managing and administering medication. These bots can remind patients to take their meds, give info regarding drug interactions, and alert them if there are any issues with their treatment.

5 CHALLENGES OF CHATBOTS FOR BUSINESS AND HOW TO OVERCOME THEM

Medical data analysis is another area where chatbots can prove useful. AI bots assist physicians in quickly processing vast amounts of patient data, enabling healthcare workers to acquire info about potential health issues and receive personalized care plans.

healthcare chatbot for routine diagnostic tasks

A healthcare chatbot can link patients and trials according to their health data and demographics, boosting clinical trial participation and accelerating research.

Chatbots can manage mundane tasks like scheduling appointments and providing simple answers about treatments and insurance.

The medical chatbot can assist as an interpreter for non-English speaking patients. The bot can then interpret during consultations and appointments, eliminating language issues.

AI chatbots are also being used to uphold and teach people about their well-being. It will give advice on healthy eating, offer lifestyle modifications, and remind them of other important activities.

Suicides are a growing epidemic, so let’s tackle it head-on with technology. We can design an app and chatbot with mental health resources that deliver tailored Cognitive Behavioral Therapy. AI tech can help those in need by reminding them of appointments, offering tips for treatment, and providing invaluable assistance in tackling their mental health issues.

BENEFITS OF USING CHATBOTS FOR YOUR BUSINESS

The Pros and Cons of Healthcare Chatbots

There are benefits and drawbacks to using chatbots in medicine, just as with any new technology. So why don’t we briefly talk about some of them below?

According to Statista, by 2022, the market size of customer service from artificial intelligence chatbots in China will amount to around 7.1 billion Yuan. AI can be a real “plus” for the healthcare industry too. 

ai market

Some of the many rewards it offers include:

Chatbots can help the health sector save an estimated $11 billion annually! Automating some tasks and quickly responding to basic questions result in reduced medical service expenses and free up doctors to tackle more complex issues.

Chatbots can be used to streamline and make healthcare services more efficient.

In addition to saving money, medical bots can offer faster access to healthcare services. According to a survey, 78% of people prefer using bots for medical services. 

AI-powered chatbots are able to provide comprehensive support and advice to patients and follow-up services.

Harnessing AI capabilities, chatbots can provide thorough aid and counsel to patients, as well as follow-up consultations and treatments.

THE HEALTHCARE ECOSYSTEMS: A TOUR THROUGH THE HEALTH-INTERCONNECTED WORLD 

advantages of ai chatbots in healthcare

A further benefit of a medical chatbot is that it can furnish individualized healthcare services, guidance, and assistance to patients. Utilizing the power of AI, these chatbots can provide every patient with personalized advice and reminders tailored to their requirements.

On the opposite side of the coin, there are a few obstacles to consider when contemplating the development of healthcare chatbots. Let’s take a gander at the downsides. 

Putting together an AI that can handle delicate medical information can be pretty intricate and take longer than expected.

One major disadvantage is that, for the time being, chatbots cannot deliver thorough medical counsel. Thus, these should be employed in conjunction with the direction of certified medical experts and not as a substitution.

Also, ethical and security problems may appear when bots access patient records. Some chatbots may not include the necessary safety measures to securely store and process confidential patient data, thereby risking patient privacy. Health services that employ a chatbot for medical reasons must take precautions to prevent data breaches.

TOP 10 HEALTHCARE TRENDS EXPECTED TO BE IN 2023

The stellar performance of healthcare chatbots is only as good as the info they’re fed. Feed them incorrect details, and their misdiagnoses or shady treatments flummox you. To ensure accurate results, keep patient data up-to-date and current!

cloud computing for ai chatbots

Chatbots may not be able to provide the full scope of mental health support, so healthcare organizations must pair them with dedicated medical professionals for comprehensive aid.

How to Choose an AI Chatbot for Your Healthcare Organization

When choosing an AI chatbot for your healthcare organization, there are several factors to consider.

  • Type

The first step in developing a healthcare chatbot is determining its purpose. Specifically, do you need one that can help you make decisions and support you clinically or one that focuses on providing general medical guidance to patients?

  • Features 

Think about what the chatbot can do and what features it has. Askings questions like “can I get specific recommendations and reminders from the chatbot?” “Can patient information be safely stored and processed?” can help you make the right choice.

HOW MUCH DOES IT COST TO DEVELOP AN APP: DETAILED FEATURE BREAKDOWN

  • Costs

Also, take into account the cost of the chatbot. They can be expensive, so you should consider the price and make sure it fits your budget.

Costs of implementing a healthcare chatbot
  • Security

Pay close attention to the chatbot’s security settings and how to protect patient data is essential. Ensure that it has the right security measures to keep sensitive patient information from getting into the wrong hands.

WHAT IS ARTIFICIAL INTELLIGENCE IN HEALTHCARE?

Chatbots and Their Place in Healthcare

Chatbots could help improve health care by providing information, answering patients’ questions, and helping to sort out symptoms. A chatbot can tell you about general health or how to deal with a certain condition, for example. They also help healthcare providers by answering patients’ frequently asked questions and directing them to the right care. 

Healthcare facilities must use chatbots in a responsible and protected manner. They can’t replace doctors and nurses, so that’s something to remember. For the best results in patient care, hospitals, clinics, and other organizations should integrate bots with medical professionals and psychologists.

HEALTHCARE APPS DEVELOPMENT: TYPES, EXAMPLES, AND FEATURES

Outcomes

Healthcare chatbots have the potential to revolutionize the health industry. They are a powerful and cost-effective way to provide medical advice and support to patients and health providers. They also provide personalized advice and reminders tailored to the individual patient’s needs.  

Technology is still in its early stages, and chatbots still need to be built, tested, and regulated based on their usage in medical care. It is important, though, that healthcare organizations use these bots safely and responsibly. Nevertheless, we are excited about the future!

Do you need a team of specialists who will work with you to create a healthcare chatbot for your app and protect against cyberattacks?

The APP Solutions is a leading healthcare technology company that creates innovative products to improve patient outcomes and streamline healthcare processes. Our talented developers and designers work hard to give our clients the most advanced, secure, and effective solutions to improve patient outcomes and streamline healthcare processes. 

We have a proven track record of delivering high-quality, user-friendly, and scalable healthcare technology solutions. Our expertise includes developing electronic health records (EHR) systems, telemedicine platforms, patient portals, and chatbots for mobile health, among other things. Our solutions are designed to comply fully with HIPAA and HITECH. Contact us today, and you will be glad you did.



How to leverage Big Data and Machine Learning for business insights

Big data and Machine Learning are hot topics of articles all over tech blogs. The reason is that businesses can receive handy insights from the data generated. The main tools for that are machine learning algorithms for Big data analytics.  But how to leverage Machine Learning with Big data to analyze user-generated data? Let’s start with the basics.

What is Big data?

Big data means significant amounts of information gathered, analyzed, and implemented into the business. The “Big data” concept emerged as a culmination of the data science developments of the past 60 years.

How to understand what data could be useful for business insights and what data isn’t? To find this out, you need to consider the following data types: 

  • Data submitted. When the User creates an account on the website, subscribes to an email newsletter, or performs payments, for example.
  • Data is a result of other activities. Web behavior in general and interact with ad content in particular. 

Data Mining and further Data Analytics are the heart of Big data solutions. Data Mining stands for collecting data from various sources, while Data Analytics is making sense of it. Sorted and analyzed data can uncover hidden patterns and insights for every industry. How do you make sense of the data? It takes more than to set up a DMP (Data Management Platform) and program a couple of filters to make the incoming information useful. Here’s where Machine Learning comes in.

What is Machine Learning (ML)?

Machine Learning processes data by decision-making algorithms to improve operations. 

Usually, machine learning algorithms label the incoming data and recognize patterns in it. Then, the ML model translates patterns into insights for business operations. ML algorithms are also used to automate certain aspects of the decision-making process.

What is Machine Learning in Big data?

ML algorithms are useful for data collection, analysis, and integration. Small businesses with small incoming information do not need machine learning. 

But, ML algorithms are a must for large organizations that generate tons of data. 

Machine learning algorithms can be applied to every element of Big data operation, including:

  • Data Labeling and Segmentation
  • Data Analytics
  • Scenario Simulation

Let’s look at how businesses use Machine Learning for Big Data analytics.

Machine Learning and Big data use cases

To give you an idea of how businesses combine both technologies, we gathered examples of big data and machine learning projects below. 

Market Research & Target Audience Segmentation

Knowing your audience is one of the critical elements of a successful business. But to make a market & audience research, one needs more than surface observations and wild guesses. Machine learning algorithms study the market and help you to understand your target audience. 

By using a combination of supervised and unsupervised machine learning algorithms you can find out:

  • A portrait of your target audience 
  • Patterns of their behavior
  • Their preferences

This technique is popular in Media & Entertainment, Advertising, eCommerce, and other industries.

To find out more about ML and Big data, watch the video. 

Source: Columbia Business School

User Modeling

User Modeling is a continuation and elaboration on Target Audience Segmentation. It takes a deep dive inside the user behavior and forms a detailed portrait of a particular segment. By using machine learning for big data analytics, you can predict the behavior of users and make intelligent business decisions. 

Facebook has one of the most sophisticated user modeling systems. The system constructs a detailed portrait of the User to suggest new contacts, pages, ads, communities, and also ad content.

facebook big data

[Source]

Recommendation engines

Ever wondered how Netflix makes on-point suggestions or Amazon shows relevant products from the get-go? That’s because of recommender systems. A recommendation engine is one of the best Big data Machine Learning examples. Such systems can provide a handy suggestion on what types of products are “bought together.” Moreover, they point out the content that might also be interesting to the User who read a particular article.

Netflix recommendations

[Source]

Based on a combination of context and user behavior prediction, the recommendation engine can:

  • Play on the engagement of the User
  • Shape his experience according to his expressed preferences and behavior on-site.

Recommendation engines apply extensive content-based data filtering to extract insights. As a result, the system learns from the User’s preferences and tendencies.

Predictive Analytics 

Knowing what the customer needs is one of the foundational elements of retail. That’s market basket analysis in action. Big data allows calculating the probabilities of various outcomes and decisions with a small margin of error. 

predictive analytics

[Source]

Predictive Analytics is useful for:

  • Suggesting extra products on eCommerce platforms
  • Assessing the possibility of fraudulent activity in ad tech projects
  • Calculating the probabilities of treatment efficiency for specific patients in healthcare

One example is eBay’s system that reminds about abandoned purchases, hot deals, or incoming auctions.

Ad Fraud, eCommerce Fraud 

Ad Fraud is one of the biggest problems of the Ad Tech industry. The statistics claim that from 10% to 30% of activity in advertising is fraudulent.

Machine Learning algorithms help to fight that by:

  • Recognizing the patterns in Big data
  • Assessing their credibility
  • Blocking them out of the system before the bots or insincere users take over and trash the place

Machine learning algorithms watch ad track activity and block the sources of fraud.

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Chatbots

Conversational User Interfaces or chatbots are the most use case of Big data & machine learning. By leveraging machine learning algorithms, a chatbot can adapt to a particular customer’s preferences after many interactions 

The most well-known AI Assistants are Amazon’s Alexa and Apple’s Siri.

To find out, how does Alexa uses ML algorithms, watch the video. 

[Source: Data Science Foundation]

In Conclusion

Big data is an exciting technology with the potential to uncover hidden patterns for more effective solutions. The way it transforms various industries is fascinating. Big data has a positive impact on business operations. Machine learning eliminates routine operations with minimum supervision from humans. 

Both Big data and Machine Learning have many use cases in business, from analyzing and predicting user behaviors to learning their preferences. If you have selected the use case of  Big data Machine Learning for your business, do not hesitate to hire us for ML development services.