Launching a new mobile application project presents numerous challenges for the entrepreneur. This category aims to answer the most popular questions about app development outsourcing, promotion, and thrive on the market.
Our new episode features Lindsay Dornfeld, MBA, Sr. Manager Veradigm – Bachelor’s in Rehabilitation Psychology, Masters in Business Administration, and she’s also a certified health information technology specialist. It’s hard to find a more appropriate education for what she’s doing now. Lindsay began her journey in medicine, working with children with autism. Even though her work is no longer directly related to patients, their happiness with the product is always at the forefront of her mind.
Lindsay Dornfeld’s Journey in Healthcare Management
One of her first jobs as a manager rather than a therapist was involved in leading platform migration training for end users for the climate education process. Here Dornfeld not only taught but also learned how to listen to users and try to consider their wishes and further improve the results.
When you know what you’re doing or why or how you are doing it, but to be able to explain it to someone else is a really invaluable skill.
In her next position, Lindsay is already managing program KPIs, better performance, patient progress metrics, and client portals: “Driving greater transparency starts with everyone agreeing that it’s okay to have roadblocks, it’s okay to refine the data if you’re working on project management, but you’re stuck in thinking it has to be a certain way.”
To account for KPIs, Lindsay uses the colors of a traffic light, where green means all is well, yellow means you may be at risk and red means you are already at risk (of deadlines, budgets, etc.) And green all year round doesn’t mean that by the end of the year, red won’t suddenly show up somewhere, so it’s better to use yellow as a safety net, and you don’t end up in the red zone. After all, always being on your guard is justified for work and your personal life.
Lindsay stresses the importance of communicating with end users to clearly understand their work: “If I’m talking to someone in a nursing home, I might want to speak to a charge nurse. I wasn’t even aware of that detail that would impact her workflow, so looking for that strategic partner and getting the whole team’s buy-in and responsiveness will be beneficial for the whole team needed.”
If you say: “I need this signature by the end of the month because it is 2-5 million dollars,” and it’s critical to the success of our product, that’s a little bit easier to say: “I need this now, I need this right now” all the time.
If I can’t explain why it’s important and urgent, then it probably is something that can wait until tomorrow.
Lindsay shared how she selects people for her team, what a stopgap is, and why. She also shared her vision for her role: “My team is smarter than me. So I’m here as a manager and a leader to break down those barriers for them to get the roadblocks out of the way so that they can come through and do their job, and they do it the best.”
Our guest also explained why she considers NPS (not profit or margin) the main indicator of success. You can’t think you’re doing well if people don’t recommend your product to people they know.
I want to be able to make a doctor’s appointment in less than one minute, and that’s my measure of success.
This means paying attention to the critical errors that get in the way of making a doctor’s appointment in one minute. At the same time, don’t focus on the flaws that don’t affect the inconvenience of the appointment in any way. In this way, the 20 root causes eliminate 80 percent of errors. Involve people, talk, ask questions, and interrogate. So you end up with a car that can go from point A to point B, and whether it has heated seats and a sunroof is secondary.
I keep the business lens, and I see the dollars and the numbers, but I also see the people and the families. The joy, the pain, the reason you know why we do what we do.
The APP Solutionslaunched 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 –
Today our guest is Estee Goldschmidt, VP of Product Management at Parsley Health. Her experience varies from launching a startup and fundraising and understanding everything about that culture to managing multi-international product development teams. Estee came to the US at 17 and immediately got involved in the student community, holding responsible positions.
Interestingly, Estee’s first job was a position in the marketing department of a cosmetics company… Estee Lauder. Such concurrencies sometimes happen! One of her most important decisions involved a strategy for distributing free product samples. Instead of making everyone go through the headache of planning samples twice a year, Estee offered to match this with an actual sales pipeline, and it resulted in huge savings for the company.
Even if you’re sort of one person out of thousands, there’s still a way to have an impact and improve things.
Not all of Estee’s decisions were winnable. But she was not discouraged; on the contrary, she made more effort. In particular, she was fired up by the idea of creating a startup. Estee spent a summer in Israel at a startup gas pedal, watching other founders do it. Instead of writing business plans, founding companies, assembling teams, pitching contests, and trying to find funding, they just started creating a product and offering it to customers. And when they got customers, they started saying, how do I make something official out of this? How do I get funding to launch this idea?
Getting people to pay for a product that’s essentially free is brilliant.
Esti then went back to the US and got 4,000 active users within two months-that was the result of creating a sales map, where people open it and see what events are happening near you. Esty shares the experience of reaching goals, “We didn’t have millions of meetings where we were trying to make a lot of different parts of the group happy, it was like whether or not we were doing it, and if we were, we all agreed, and we were working on it.”
The ability to move fast is something that has to be maintained.
In a fast-moving company, there’s a powerful alignment of mission and what needs to be done. In big companies, most of the work that needs to be done is already completed, everything is stable, the processes work, you know it’s a well-oiled machine, so if you bring in a new person or someone comes in, first of all, there are a lot of cases where there’s no work to justify team X, so many teams start working to understand what their purpose is, and that kind of white space can provide opportunities to discover new areas or what can be done better, but it can also lead to places where another big.
You could have the smartest people in the room, the most educated people, the most experienced people, but if everyone’s focused on their own thing or rowing against each other, it’s very hard to accomplish anything.
About the experience at Cerebral: I was working on what needed to be fixed, so in the beginning, I just made sure that our offering was relevant and that everything worked as it was meant to, and that was the first step and the second step was to strategize the following steps, what we offer, how to make what we offer better and stronger by listening to our customers and understanding where their pain points are, what we can fix, what we can take away, so the customer experience is better. The product manager doesn’t have to tell team members how to work, the developers and designers are professionals in their field, and they know better.
When it comes to making product decisions, it seems to me that the answer should always be: what’s best for the customer?
If you follow that path and use that as a guiding star, you’ll have a good strategy. There’s a precise balance in management between “get away from me with your micromanagement” and “I care about feeling your support”-the key is establishing trust.
The last thing you want as a manager is for your team to be afraid of you.
And if something goes wrong and you don’t know about it and then things blow up in your face, you want them to feel safe and be able to say, hey, this is going wrong, and then I’ll help them solve it, because I end up hiring people for work that I don’t want to do for them.
You have the financiers, you have the marketers, you have the doctors, but who represents the customer, and everything is done for the customer, so the job of products is to sit at the table and be that customer, be that voice of the customer.
Three examples of these qualities the most talented professional product managers have been. The most important is an obsession with customers as a guiding star. Another quality is the ability to execute. A third is an ability to step back when necessary and let your associates do their job.
I have a work style and an idea of how to impact things, and ultimately, I want to be in a place that “wants” those skills and recognition instead of hiding and trying to conform to something I am not. You can be great at one place and wrong at another.
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.
Digital biology, also known as biotechnology and digital biotech, gives bioengineers, medication producers, agricultural companies, and industrial businesses excellent opportunities. Biotechnicians can turn biomaterials – living systems and organisms – into a digital data format, organize it, discover hidden patterns, and store it in databases.
Why does it matter?
Such an approach streamlines the research, development, and test stages of biology projects that previously took bio technicians months or years. Moreover, medical specialists apply digital biology to diagnose health conditions, such as cancer and sepsis, within several hours and suggest the most appropriate treatment based on patient samples.
Digital biology took a leap in development by applying Artificial intelligence and machine learning algorithms that automate biological data analysis and research. Thus, bioengineers generate more data in shorter terms, compared with the analog study methods they used previously.
In this article, you’ll find the current state of digital biology and the fields it serves. You’ll also read about biotechnology areas that benefit the most from other intelligent technologies, such as AI, machine learning, and cloud computing.
The current state of the Digital Biology market
Digital biology is a cross-disciplinary field that combines both biological and technological components. It includes exploring and analyzing living organisms with new intelligent tools.
Recognizing the considerable potential of biotechnology, governmental organizations, such as the National Institute of Biomedical Imaging & Bioengineering in developed nations and the National Center for Biotechnology Information, increased their investments into the research and development activities in biotechnology fields.
The market research from Global Market Insights (GMI), a global market research and management consulting company, says an increased interest from governmental organizations is expected to make biotechnology the largest and the fastest-growing market, projected to reach $729 billion by 2025, compared with $417 billion in 2018.
The research also includes a forecast of revenue increase for the following technology segments:
Fermentation
Tissue engineering and regeneration
PCR technology
Nanobiotechnology
Chromatography
DNA sequencing
Cell-based assay
And others.
In particular, the fermentation segment is the most prominent sub-niche of biotechnology that received an 11% revenue share of the whole biotechnology market in 2018.
The report predicts substantial progress for fermentation technology during the next few years. Fermentation is a process that changes organic substrates on the chemical level by enzyme action and micro-organisms.
Such growth of fermentation technology is explained by excessive use in the food and beverage industry. The food and beverage industry’s key business players will increase investments in biotechnology to improve the research and development activities to produce more fermented products.
The expected growth of biotechnology opens new opportunities for biotech startups, well-established companies, and research institutions. Another reason for biotechnology’s rise is various applications in medication, agriculture, and other industries.
Biotechnology Application Outlook
Digital biology, or biotechnology, includes several categories of applications. Biological technicians and other scientists apply digital biotechnology for solving scientific problems with living organisms across various industries – from healthcare and agricultural to industrial processing and bioinformatics.
Let’s see how each category benefits from artificial intelligence and machine learning.
Health
In medical biotechnology, scientists receive information from living cells to get a clearer picture of human health, thus producing the most appropriate drugs and antibiotics.
Bio technicians dig into the smallest details to achieve these goals – study DNA and manipulate cells to predict beneficial and vital characteristics.
The most useful tech solutions used in medical biotechnology are Artificial Intelligence and machine learning that enable scientists to improve their drug discovery process by reaching small molecules and their target structures they need to treat.
Machine learning algorithms also perform great for patient testing and diagnostics. The algorithm can detect damaged tissues and other abnormalities via medical images, patient samples, and even sounds. For example, intelligent algorithms can detect cancer cells in X-rays, sepsis via DNA sequencing, and define whether the patient has COVID after hearing one’s cough.
In this way, doctors provide more timely and accurate treatment for better outcomes.
Moreover, artificial intelligence and machine learning are used in electronic health record (EHR) systems and clinical decision support systems to help doctors suggest a patient’s personalized medical treatment and accurate medication management.
Food and agriculture
Agricultural biologists apply biotechnology to increase crop yields, genetically modified plants, and identity infected crops before the harvest. For these purposes, scientists use DNA sequencing devices and databases with DNA samples of already sequenced genes. Once new DNA samples are sequenced, scientists can change their structure, learn more about the plant origins and potential issues typical for one or other plant.
[Increasing application of cell line engineering will drive the overall market expansion]
Food and agriculture biotechnology companies apply AI-algorithms to harvest crops, watch crop health, and find AI-powered tools more effective than humans.
Such an application requires food and agriculture businesses to integrate autonomous robots or drones, computer vision algorithms, and deep learning technologies. While drones and robots carry cameras, algorithms analyze crop pictures they receive, compare data captured with crop images in their database, and define whether crops and soil are healthy or not.
Industrial processing
Industrial biotechnology includes research on biopolymer substitutes, inventions of vehicle parts, alternative fuels, new chemicals, and the process of production. In this area, intelligent technologies and the Internet of Things (IoT) devices help industrial producers analyze their machinery to predict outages, optimize equipment, and even reduce human worker numbers with automated warehouse management.
One example is Ocado Technology, an online grocery retailer that automated its warehouse with 3500 robots to process 220,000 online orders a week for grocery delivery.
To learn more about AI and machine learning applications in industrial processing and supply chain, check out our previous article about top AI applications in supply chain optimization.
Bioinformatics
Bioinformatics is a subdiscipline of digital biology that combines biology and computer science to acquire, store, analyze, and disseminate biological data, such as DNA and amino acid sequences. Scientists understand biological information using mathematics, data science, and different digital biology tools by organizing it in large biological pools.
Bioinformatics also receive benefits from AI and machine learning. Artificial intelligence and machine learning help biologists sequence DNA from the massive data crunch, classify proteins, protein catalytic roles, and their biological functions. Leveraging intelligent technologies, scientists can automate gene expressions and gene annotation and identify the location of genes required for computer-aided medication design.
In digital biology, biotechnologists base their research on digital data, generated from life samples or DNA sequencing devices and stored in a thousand databases, both private and public.
So we can conclude that the growing biotechnology industry will heavily rely on AI-algorithms, machine learning, and data analytics. But the development of biotechnology across all segments depends on biotechnology researchers’ ability to master their skills for the useful contribution of their findings and researcher results. Not only AI makes biotech engineers more efficient, but also for a bunch of other reasons.
Let’s check them out.
Top 3 advantages of using AI in the biotechnology industry
PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution says AI will contribute to the global output of $15.7 trillion by 2030. By this time, 44% of pharma and life sciences experts expect to adopt Artificial Intelligence in their laboratories and R & D centers and replace analog tests.
But why do scientists prefer digital biology to the old-but-gold analog approach?
Development and research projects often require scientists to deal with numerous amounts of data and large sample sizes, such as genome sequencing. In such cases, biological test digitalization allows researchers to produce more data than analog study methods. And by applying digital biology, scientists can receive real-time insights into biological functions which have taken them days and weeks when using an analog approach.
The adoption of AI and machine learning by biology specialists make the digital biology approach even more useful. And here is how:
Crucial predictions
Artificial intelligence and machine learning algorithms help bio technicians make more precise predictions than standard approaches used for decades. Successfully applied in supply chain and logistics, predictive analytics drastically reduce the time biotech companies spend to launch new products to market.
To make data-based decisions and forecast outcomes, data scientists train algorithm models with historical databases. Then, such algorithms are effectively used for pattern recognition, despite the data type.
As Nature online resource highlighted, intelligent algorithms’ ability to analyze large amounts of data in datasets helps drug-producing companies make new pharmaceuticals quicker and more effective. Soon, medication specialists will provide more personalized treatments, based on the disease’s cause, hidden deeply in biological structures. In this way, pharmaceutical companies can reduce the medicine development process from the $2.6 billion price tag and decrease the 90% failure rate of new medication created.
In her article, Melanie Matheu, Ph.D. and founder at Prellis Biologics, Inc. the human tissue engineering company predicts the new generation of therapeutics entering drug pipelines empowered by AI screening for selecting targets will reduce clinical trial failure rates for small molecules by 86%.
Effective decision-making
Clinical trials used to be manual and a very time-consuming process – they included inviting participants to the clinic during the in-person visit, recording their symptoms, prescribing them treatments, and analyzing side effects. Moreover, to get the right sample size, medication companies heavily invested in marketing resources for recurring right patients and treating rare conditions.
Now, intelligent algorithms and cloud technologies digitized clinical trials and enabled biotech organizations to test medication on more patients within less time.
One example is Invitae, a medical genetic company. In November 2019, the company launched a trial in collaboration with Apple Watch to bring together biometric data from wearables and genetic tests and determine genes that cause cardiovascular disease. In this way, the company made the trial available to many people and excluded Apple Watch users who didn’t meet the trial criteria.
Biotech companies make clinical trials even more effective by leveraging machine learning algorithms that analyze data from current trials and use it for forecasting treatment effectiveness in the future, down to a molecular level. ML also helps scientists revise information from previous tests to find gaps and new applications for existing medications.
Cost-effectiveness
Modern devices, cloud databases, data analytics pipelines, and machine learning algorithms reduced the cost of genome sequencing from $2.7 billion for the Human Genome Project to less than $300 by now. It is expected to cost even less – $100 in the future. Bioengineers receive more extensive screening of trial participants and targeting of interventions. They also see the future in personalized treatment plans and targeted therapies that provide therapies at genetic and molecular levels of patient genes.
The main area for targeted therapy is cancer treatment – the treatment of blood cancer such as leukemia, where a treatment called CAR T-cell therapy, according to the National Cancer Institute, the immune system will “attack tumors,” so we’ll soon witness more cancer survivors.
Biotech organizations also use cloud computing to host and run computations and no longer need to buy expensive computer hardware for their research. This fact is a substantial benefit for early-stage startups with limited funding to enter the market with their research and medications. Cloud computing is also handy for established medical corporations, allocating resources for new projects cheaper and more manageable.
What is the future of AI and machine learning in the biotechnology industry?
Biotechnology is an innovative industry that effectively solves scientific problems with living organisms. But new issues continuously arise and require biotechnologists applying modern methods to be solved.
Thus, to remain relevant, biotech specialists must make room for improvements. Fortunately, there are many solutions they can apply – AI, data analytics, deep learning, and others we’ve already listed in this article.
Thus, AI, machine learning, and robotics play critical roles in pushing the boundaries of possibilities in medical, industrial, or agricultural biotechnologies, and will remain relevant for subsequent decades.
The APP Solutions has experience developing and integrating AI functionality into biotech projects. You can learn more about our expertise in creating a real-time DNA sequence analysis application during our partnership with the Google Cloud Platform and the Queensland University of Technology. Don’t hesitate to contact us if you need experts to advise and develop intelligent software for your biotech project.
Terminology is a tricky thing. In various organizations, outsourcing and outstaffing can mean very different things. In Ukraine, the majority of small and mid-size IT companies call themselves IT Outsourcing.
On the other hand, large and public companies tend to position themselves as in IT outsourcing, but only in the Product Development Services and Managed services areas. As time goes on even small companies reject “outsourcing” positioning using “custom software development” instead.
So let’s look under the covers of outsourcing and product development services based on what we know about the IT sphere and IT market. We’ll also go over the outstaffing practice further in the article.
Outsourcing vs. Outstaffing: What Is The Difference And What To Choose?
I enjoyed working in several companies, including startups, small private IT development Shops, and large enterprises with tens of thousands of people on board.
Playing different roles on a different level, I have had multiple challenges regarding the differences between outstaffing vs outsourcing, as well as what differentiates Managed Services. In the end, each business practice has its advantages and disadvantages, so there’s no universal answer to the “outsourcing or outstaffing” question.
Let’s take a look at the following summary table, and then go one by one starting with outstaffing and building our way up.
Outstaffing
Outsourcing
Product Development Services
Purpose
Team Extension
Business Function
Technological Partner
Managements
Client
Client/Vendor
Vendor
Quality Ownership
Client
Client/Vendor
Vendor
Revenue
Low
Low-Medium
Medium-High
Source of revenue
Workforce
Operational Excellence
Intellectual Product
Outstaffing services
Software outsourcing and outstaffing are sometimes used interchangeably. However, those two practices are completely different. Basically, outstaffing is the most straightforward model to understand. Many companies are shorthanded and need a development team to help their house developers. Outstaffing comes in handy when the client’s needs mean increasing the software development team’s capacity and jumpstarting the development process. In the case of outstaffing, offshore employees for remote software development are officially employed by another client to speed up the development process.
Management
With the outstaffing approach, the client has full control over management. The outstaffing software development team inherits the existing methodology, processes, tracking tools, and communication approaches. The repository, environments, and automation tools are usually on the client-side or administered by the client. This factor is often considered among the main outstaffing pros.
Quality ownership
The outstaffing projects are also frequently called Staff Augmentation.
The outstaffing company augments their remote employees with the client’s existing house team.
The manager is responsible for the terms, product, and budget.
The outstaffing manager is an employee of the client’s company.
Revenue
The outstaffing model is a time for money trade. So, there are no cons of outstaffing from this perspective. You can imagine it in the form of the hourly rates or cost+ model. Clients buy an hour of work; the vendor gets their profit from hours sold.
Consider that the required organizational complexity here is low. There are many competitors, and competition is based on the price. Many vendors are competing around small-sized clients, so the market’s invisible hand keeps the costs low.
One of the primary sources of revenue is the workforce. The key to making more revenue in the outstaffing area is to sell more hours. The more people – the more money. That’s what the outstaffing model is about.
Within the outsourcing vs outstaffing paradigm, software outsourcing means when companies delegate one of the business needs to a third-party service provider. Apart from software development, a company can turn to outsource to help with their manufacturing, accounting, software development, testing, support or call center, and other digital activities.
Speaking about the tech area, a development team can be employed by another company to perform testing, product management, maintenance, and support, along with software development outsourcing. The outsourcing model also applies to different technologies, and an integrated software development function. Let’s continue with the outsourcing vs outstaffing comparison related to three core points.
Management
Outsourcing project management may have a complex structure. Usually, management is done on the client-side (Product management, Program management), and the outsourcing provider is responsible for proxy management (Project Manager, Team Leader, proxy product ownership).
Quality ownership
Depending on the management structure, the outsourcing vendor commits to the quality of the outsourcing function. Quality standards and formal development methodologies are applied to measure quality and results. Having managerial positions allows contractor companies to use standards and measurements and perform control over them. Furthermore, it brings an obligation to comply with standards and achieve the KPIs.
Processes, web development guidelines, prebuilt solutions and pipelines, and much more – are the solution accelerators. By keeping standard things standard, the company can avoid reinventing similar wheels for multiple projects. Lower costs with improving operational excellence give extra margin. Therefore, revenue is among the major outsourcing pros.
In other words: with the right attitude for the client, it won’t matter if developer John switched the whole project and QA Pete automated his tests. As long as the dedicated software development team produces the results they are committed to, no one cares whether it’s software outsourcing or any other hiring practice.
Apart from outsourcing – outstaffing models, there are also product development services. When I got to know the PDS term for the first time, I worked in a 10+ tech outsourcing company. The company initiated a series of events to educate its personnel about the PDS Delivery model. One example for developers was, “you stop thinking that the business wants this button painted in red: You start thinking, how would the end-user like to see this button.”
The main difference between the approaches mentioned above and PDS is that the latter means, first of all, a shift in mindset. Mature companies realize that, besides technical assignments, the company can provide their expertise and advice. The evolution from doers to thinkers to become a technological partner with the client’s business. Commit to the product and give the product more than just the hours spent.
To provide the Service of Product Development, the provider company shall acquire the competencies of Business Analysis, Project Management, Product management, and domain knowledge of the industries their clients are working at.
The PDS project is usually expected to have a Project Manager, Product Owner, Stream, and group leaders among the team members. In some cases, those roles can interface or mirror the respective roles on the client’s side. In other cases, they can be positioned as provider-side roles only. Additionally, the company establishes competency centers and practices – the non-production people responsible for acquiring and growing the competency and domain knowledge.
This gives the vendor much more autonomy and freedom. Dedicated software development teams can arrange convenient processes and adapt the methodology of choice.
Quality ownership
Having Product and Project Management on the provider side also means having Ownership and Responsibly on software end product delivery so that the quality ownership is entirely on the vendor side.
Revenue
On PDS projects the company still makes more money with more people. But that becomes a side-effect. The PDS source of revenue is an intellectual product that can take different forms. The form of higher hourly rates for engineers with higher qualifications, and additional knowledge of industry and business. The conditions of phases for generating intellectual outcomes: Solution Design, Technical/Process Audit, Marketing research, etc.
Generally, as the vendor takes more risks and responsibility, the more revenue it generates.
What solutions can we offer?
Client View
As an IT company, we are a supplier for our clients; an IT Service Provider. There is a traditional categorization of suppliers.
Let’s go through the classification:
Commodity: for suppliers who provide low-value or easily available products or services, which could be alternatively sourced relatively easily.
Operational: for suppliers of functional products and services. These relationships would usually be managed by junior operation management and would involve infrequent but regular contact and performance reviews.
Tactical: for those relationships involving significant commercial activity and business interaction. These relationships would usually be managed by middle management and apply for regular contact and performance reviews, often including ongoing improvement programs.
Strategic: for significant ‘partnering’ relationships that involve senior managers sharing confidential strategic information to facilitate long-term plans. These relationships would usually be managed and owned at a senior management level.
Being in outstaffing, the vendor falls into the Commodity category. No significant risks are associated, and alternatives can be found and engaged fast enough.
Outsourcing can be either Operational or Tactical suppliers, depending on the importance and impact. Taking more ownership, and providing more value, is the key to progress toward strategic partnership.
By practicing the PDS approach, the vendor company can achieve a strategic partnership with a client.
Staying in ‘commodity’ keeps prices (and in turn salaries) limited by the market. Too many Outstaff service providers competing to maximize the revenue can exhaust the labor market and lower the entry criteria. That’s pretty much what is happening in the Ukrainian IT labor market. And like anything else in this world, all approaches have their pros and cons.
To bid for providing services as a strategic partner, the company must understand the client and contribute to the client’s business success better than other competitors. That requires investments into:
Management – improve governance, consistency of deliverables, communication, and reporting
Practices and competencies – acquire unique selling points in the form of a group of people with “know-how”
Sales – change of focus from “selling standard services at a low price” to “provide complex technological consultation and analytics”
People – attraction and retention of talents. Utilization of bench programs, internal and external education, improved work conditions
Value of Moving Ahead
Better sales, dedicated remote team, more extensive projects, more considerable challenges and impact, higher salaries, and much more room for professional growth in almost any direction; All of these at the cost of ownership and responsibility.
I hope you liked the article. It’s challenging to cover outsourcing or outstaffing the development process and its pros and cons fully in one piece. Please share your experience, questions, and comments. It’s always exciting and motivating to get feedback.
In this latest episode of CareMind’s podcast, we delve into the fascinating intersection of psychology and product management. We had the privilege of interviewing Shane Blackman, the Director of Growth at Noom, who brings a unique perspective to the field thanks to his Ph.D. in psychology and social policy.
Shane’s background allows him to provide deeper insights into human relationships in product development and management. Join us as we uncover valuable tips and actionable advice that you can apply to your own career, and learn how understanding the human element can lead to more successful products.
From Psychology to Product Management: Shane Blackman’s Unique Journey
Shane’s path into product management began unexpectedly after getting his Ph.D. in 2014. A colleague from grad school introduced him to the world of user research at Priceline.com, where he eventually started running experiments on the website using his psychology background. Working with designers and developers, Shane found his passion for product management, despite not knowing much about it initially.
Every product manager has a unique story, emphasizing that there is no one-size-fits-all path into the field. Shane’s experience in investigating people’s perception of objectivity, beliefs, opinions, and decision-making within social groups aligns well with the day-to-day responsibilities of a product manager. By chance, during his time at Priceline.com, Shane transitioned from a product manager to the head of product analytics, leveraging his expertise in analytics.
The Importance of Growth Opportunities in Product Management
According to Shane, he began working on opaque hotel booking products, focusing on the front-end user experience. He ran experiments to improve the booking process and achieved success in this area. Shane attributes his accomplishments to his background in statistics and experiment methodology, which allowed him to understand the components of a good experiment and how to interpret the data.
I believe there’s a big opportunity for product managers to apply psychological concepts to their organizational practices, including recruitment, structure, feedback methods, leadership representation, and more.
Shane admits that by embracing opportunities and leveraging his expertise in his passionate area, he discovered the challenges and limitations of the A/B testing infrastructure. This piqued his interest in addressing the problem of determining what was actually good or bad when testing, and how much data was enough to make quick decisions. An opportunity arose to rebuild the core A/B testing infrastructure, which led to collaboration with Booking.com and learning from their advanced experimentation approach.
As more data-oriented opportunities emerged, Shane found his path eventually led to the role of Head of Product Analytics. In this position, he oversaw the A/B testing program, system, and a team of analysts generating insights from product data. He believes the core themes of his journey include curiosity, willingness to leverage strengths, and openness to new opportunities.
Shane emphasizes the importance of amplifying one’s own strengths within the organization and saying yes to opportunities, which allowed for diversified experiences and growth within the product management field. He admits that his focus on data analysis provided valuable insights for decision-making and overall success.
Is Data Necessary for Balancing Objectivity and Ambiguity in Product Management?
Determining good data for a particular outcome involves following the scientific method, starting with a clear hypothesis and then designing experiments to test that hypothesis rigorously.
Good data is data that helps you make a decision and understand whether your hypothesis is true or not.
Avoiding confirmation bias is crucial; be open-minded and willing to change your hypotheses based on the data. Collaboration with stakeholders such as engineers, designers, and data scientists is essential for collecting the right data and interpreting it accurately.
Having a clear and specific core hypothesis protects against inferential muddiness or noisiness that can occur when looking at a set of data. If results outside the core hypothesis emerge, consider discounting, replicating, or generalizing them in new situations.
The key to using data effectively is to establish protections, guardrails, and norms when examining data for the first time and deciding what actions to take based on that data. This approach ensures a more holistic understanding of the problem and better decision-making.
The Product Manager’s Role in Clarifying Hypotheses
It’s important to have a very clear hypothesis and to be clear about what you’re trying to learn from the data, and then to design your experiments, design your tests in a way that will give you the data that you need to make that decision.
Product managers, often seen as the CEOs of their products, are ultimately responsible for various aspects of the product, including setting hypotheses and driving experimentation processes. However, this responsibility doesn’t mean they should work alone. Collaboration with teammates in user research, design, and data science can help refine hypotheses and improve the overall approach.
One of the hardest things to do in product management is identifying the fundamental assumption in a product that must be tested.
To maximize the product’s success, product managers must be open to iterating and learning as they go. Creating a culture of experimentation and learning within the team is also vital. Product managers should facilitate discussions, encourage team members to contribute ideas, and develop ways to test these ideas systematically and rigorously.
Understanding the user’s emotional journey and the psychology behind their experience can significantly improve product development. For instance, when asking for sensitive information from customers, product managers must ensure they can provide an emotional outlet that reassures users about the security and necessity of the information. Additionally, dividing the process into smaller, manageable steps, starting with the easiest, can help build user comfort and commitment.
During a period of rapid growth, product managers may also need to scale agile teams to handle increased workloads and maintain efficiency. By leveraging their skills and working closely with their teams, product managers can effectively navigate the challenges and opportunities that arise in product development.
How Can Product Managers Help Scale Agile Teams?
Scaling a team at high velocity can be a challenging yet exciting time in one’s career. To ensure a successful transformation, you should have clear counterparts in engineering and design, and to empower these team leaders to make decisions independently. Also establishing strong communication and collaboration between team members can help temas grow.
Another thing is maintaining sprint retrospectives even under deadline pressure so that emerging issues can be timely spotted, ensuring that the team continues learning and adapting. Quarterly meetings, such as “persevere versus pivot” sessions, can help teams evaluate their performance, goals, and strategies, and decide whether to continue, pivot, or spin down a team.
The collective experience of team members is invaluable in making informed decisions about the direction and opportunities available to a team. In the context of a health-focused company like Noom, leveraging behavioral science can empower people to take control of their health and manage conditions like stress, anxiety, diabetes, and hypertension through weight management programs.
Managing Team Perspectives During Product Development
Shane’s research suggests that people are predisposed to attribute bias to others who disagree with them, even in subjective domains. This holds true in product management organizations as well. When presenting experiment results and interpreting data, it’s important to be aware of our own biases and how they might affect our reactions to conflicting hypotheses. To counteract this, organizations should cultivate a culture that encourages open discussion, acknowledges biases, and fosters an understanding of how biases can influence decision-making.
Integrating psychology and product management can lead to a deeper understanding of human behavior and collaboration, ultimately resulting in more effective and successful products. Key takeaways for product managers include:
Embracing growth opportunities and leveraging one’s strengths in areas of passion allows for diversified experiences and career growth within the product management field.
Good data is crucial for product managers to balance objectivity and ambiguity; collaboration with stakeholders ensures accurate data collection and interpretation.
Product managers must be open to iterating and learning, fostering a culture of experimentation and collaboration within their teams to maximize product success.
During periods of rapid growth, product managers should focus on clear communication, collaboration, and decision-making processes to effectively scale agile teams and manage challenges
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.
In this Careminds podcast episode, our conversation with Ran Shaul, Chief Product Officer and Co-Founder of K Health and Hydrogen Health, explores virtual healthcare and the influence of AI on patient experiences.
The discussion extends to data-driven decision-making, entrepreneurship within the healthcare sector, and Ran’s unique perspective on the central role product managers play in health tech.
How to Know When a Career Path Makes Sense
After a late start in his career post a five-year service in the Israeli Army, Ran pursued industrial engineering and computer science in Israel, driven by a passion for data science. Upon graduation, he used his skills to tackle complex problems using data, with a particular fascination for employing mathematics in business contexts.
“That’s really the theme of everything I’m passionate about. I don’t know why I’m attracted to the concept of using mathematics to solve business problems.”
Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health
This led him to start his first business after only a few years of experience in a company working with data warehouses in the early days, which involved managing large databases and local machines before the advent of the cloud. This step into entrepreneurship was motivated not just by a desire for creative freedom, but also by a conviction that data science was poised to become highly influential. This conviction proved true as Ran navigated the growing fields of data mining and natural language processing.
Ran started three companies in total, with the first one being in the health sector. The other two were either acquired or sold, and his focus eventually settled on a company he had founded 6.5 years prior. This company represented a matured perspective in entrepreneurship and offered the chance to tackle a significant problem.
Driven by personal experiences with healthcare and a desire to contribute to something mission-driven, Ran aimed to use data to empower people to make better decisions, particularly in the field of medicine. Six years prior, accurate online medical information was scant and he saw potential in creating an online system for medical advice that was as easily accessible as booking a flight or finding a restaurant.
When asked about the nature of his company, K Health, Ran explains that it’s an AI company, a virtual company, and a doctor’s clinic all in one. Traditional doctor visits often have negative expectations, including long wait times, short consultations, and unforeseen costs. K Health aims to alleviate these issues by offering a more flexible and comprehensive experience.
Patients can consult a doctor on their own terms, at any hour of the day. This flexibility caters to those with busy schedules who might only find time for a doctor’s appointment late in the evening. The wait time is minimal, and the consultation is more in-depth as patients can discuss their symptoms at length with an AI before meeting a physician. This enables the physician to understand the patient’s condition quickly and thoroughly.
The company offers multiple modes of consultation, including video and text-based conversations. Unlike traditional doctor visits, their service doesn’t necessarily end after a single consultation. Patients have the freedom to return to the app and continue discussing their condition or ask further questions about their treatment. This fosters a long-term relationship with the physician rather than a series of transactional interactions.
What Does It Take to Align Innovation and Market Perception?
In healthcare, you should adopt an approach that is conservative, avoiding the typical tech mindset of “move fast and break things”. This principle is even more important when navigating the intricacies of healthcare regulations, which often contain gray areas. Despite these challenges, it’s vital to always prioritize safety and adhere strictly to regulations.
On the question of balancing innovation with regulation, especially as patients share their information with an AI, Ran believes that their approach in summarizing a patient’s situation to provide efficient and personalized care is an innovative and useful feature. He indicates that users are in full control of their experiences, which makes this combination of virtual primary care and personalized AI a truly innovative healthcare solution.
For instance, while there are companies who have chosen to adopt a more aggressive approach by prescribing potentially addictive medications online, this might not always be the best course of action. Such decisions should be made with the patient’s health and safety in mind. Restrictions to service areas that guarantee high-quality and safe care should be seriously considered.
Now, the medical decision-making process primarily lies in the hands of qualified physicians. As an entrepreneur or a tech professional, one should respect and adhere to these decisions without any judgement or influence. The guiding principle in digital health should always be thinking about the long-term outcome for the patient rather than a fast-paced growth model.
While this approach might not conform to conventional business growth models, in the field of healthcare, patient outcomes should always take precedence. It’s important to steer clear of cases that might jeopardize patient safety or the reputation of digital healthcare. By considering these aspects carefully, one can successfully navigate the complexities of designing user-centric, innovative, and safe healthcare solutions.
What Are the Key Challenges in Creating Unreplicated Workflows?
“It’s fine to be an AI company or a virtual clinic individually, but integrating both presents a significant challenge”.
Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health
Envious glances might be cast towards AI companies that develop an algorithm and simply provide an API for use, or services that offer “doctor in a box” solutions via video call. However, without a connection between the two, real change can’t occur.
So how do you apply AI safely for the benefit of physicians and patients within a clinical care environment? It’s not just about building an AI system that’s accurate and continually learning, but also about making it understandable for patients and beneficial for physicians.
Often, questions arise about how such an accurate machine was built, one that knows everything about primary care conditions and can diagnose people. However, the main question isn’t just about how it was built, but also about how it’s explained to patients. How do patients understand what the results actually mean? How are these results handed over to physicians? And how is the experience continued such that when a patient has consulted with the AI, the physician has the ability to seamlessly take over and make the actual medical decision?
These considerations represent the major challenge. In the end, the service needs to be something people enjoy using and are satisfied with. It’s a blend of art and science, requiring a combination of different domains. A meeting at a company like this could involve five different domains in the same room: physicians, engineers, mathematicians, regulatory and operational experts, and product designers.
The second part of the challenge is how to build an accurate algorithm. This is where reinforcement learning comes in. Regardless of how simplistic the initial iteration might be, if the model is trained rapidly enough and consistently given feedback about its performance, it will learn and deliver the desired results over time. This concept of a machine constantly learning from humans, a continuous loop of diagnosis, feedback, and improvement, is at the core of the AI’s development and refinement.
These two aspects – multidisciplinary collaboration and constant machine learning – are instrumental in overcoming the challenges that come with blending AI and healthcare in an effective and meaningful way.
How to Define Product Success in Your Organization
“If you have people using the product and come back for more, that is when you know, you have a good product in the market.”
Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health
Reflecting on leadership style and how it has evolved over the years, there is a need to balance personal opinions and passion with the success of the company. In the early stages, when the company is small, you might be doing a little bit of everything. However, when the company grows – as it did during the COVID-19 pandemic from a 50-person company to a 300-person company – the need for vision and leadership becomes more pronounced.
Using techniques like providing hints rather than direct instructions and allowing people to discover things themselves can be very effective in larger settings. As the company grows, the leadership role becomes more about providing vision and inspiration rather than direct, hands-on guidance.
The establishment of a strong leadership layer is critical to the impact and success of the company. This strong leadership group, composed of leaders in different domains, has the ability to execute efficiently and effectively. Creating alignment with this group is key. It’s important to maintain the right to go into the details – to look at the code, the algorithms, the design – but to do it in a consultative way rather than authoritative, to avoid disrupting the work of others.
Maintaining a strong leadership team at the top, ensuring they have the capacity and willingness to execute, while occasionally diving into the lower levels to get your hands dirty, is vital. It’s a balance of leading by example and supporting those executing the work.
Tough Jobs, Tougher Candidates: The Ideal Profile for a Product Manager
“You need to have a belief, you need to have a vision. They need to be able to basically say no to the naysayers and say no.”
Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health
Ultimately, someone needs to connect the dots. There’s a necessity for someone to sit in a room, hear all the arguments from various sides, and then stitch it all together. This task is complicated because product managers may not have a background in medicine, nor might they fully understand all the regulatory aspects of their decisions. Despite this, they suddenly need to merge data science, the accuracy of algorithms, and the provision of high-quality clinical care. This makes the role of a product manager incredibly complex, given that they likely aren’t a data scientist nor a physician.
There are two dimensions that are important here: curiosity and the ability to make decisions. Surprisingly, many people prefer to stick to what they know. If they’ve worked in an e-commerce company, for instance, they might be comfortable with selling a new product using the same basic user funnel principles. However, the role here requires learning new domains, understanding the considerations of a physician, the considerations of an algorithm, and integrating those. This requires an eagerness to learn, to read and to understand beyond what one already knows.
The second dimension is decision-making and trade-offs. There’s rarely a perfect solution or an exact minimum viable product (MVP) in every aspect. So, you have to make decisions and execute them in such a way that you’re making small progress with each step. It’s not about one or two decisions; it’s about thousands of micro-decisions that build the big picture and result in a cohesive product. This combination of curiosity and trade-off handling makes for a very strong product manager or product owner.
How Often Do Product Managers Influence the Company’s Vision?
“A product manager needs to kind of ignore the noise and follow the data and, but that’s the task when you actually have a running product with your own data.”
Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health
It can be challenging to know which feature to implement, and sometimes you have to rely on A/B testing and observing what works. This requires a product manager to cut through the noise and follow the data. However, this mainly applies when you already have a running product with your own data.
The situation changes when you don’t have this data, for instance, when you want to start a completely new feature or even a new company. While surveys can provide some feedback, consumers may not be as good at giving feedback for a product that doesn’t exist yet. It’s difficult for consumers to envision using a product that doesn’t exist.
In these situations, the product manager needs to rely more on gut feeling, belief, and vision. They need to have the courage to say no to the naysayers and to believe that they are innovating something that people will want to use. This is where many interesting things happen and where new features are born.
For instance, with K, we didn’t initially know if people would be interested in a single screen showing them a differential diagnosis. Some suggested that people wouldn’t want this feature and that it would only confuse them. However, we went ahead, implemented that screen, and iterated around it. It turned out to be a moment of success, with users spending four minutes answering questions just to know what K thinks about their condition. This was despite initial feedback that people wouldn’t want to spend that much time providing information.
So, the toughest part of being a product manager is to break through the “nos”, follow your vision, and build something that you believe people will like. Then, you put it in their hands and see how they respond. Despite the rules and guidelines, sometimes you need to see past them, invent new things, and rethink the existing order.
In conclusion, if you have a good idea, just go ahead and do it. While gaining experience in big companies and working in different environments is valuable, there’s something uniquely rewarding about pursuing your own idea. Entrepreneurship and leadership aren’t for everyone, but if you enjoy the excitement and have something you want to pursue, go ahead and do it. Put it out there.
The key points are thus:
Passion, persistence and the right skills can create meaningful entrepreneurship ventures, even in complex fields like healthcare.
The integration of data science, AI and real-world medical expertise is key to providing a more accessible and efficient healthcare service.
Regulatory compliance, safety, and patient-first approach are paramount in navigating the challenges of digital healthcare innovation.
Success in health-tech depends on multidisciplinary collaboration and constant machine learning, aiming for a blend of accuracy, transparency, and patient-physician interaction.
The role of a product manager in this setting is multifaceted, requiring curiosity, sound decision-making, and the ability to navigate both familiar and unfamiliar terrains.
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.
