Data science in Healthcare: How to change the industry

Specialists are now making use of vast amounts of data to evaluate what works better. The new health data science approach allows applying data analytics that has been aggregating from various fields to boost the health care sector. It is now obvious, the healthcare system is ready for change.

With ERMs, clinical trials, wearable data, and internet research, there is no data processing in healthcare. And with the majority of patients seeking health advice online, and lots of people using tools like Zocdoc to book an appointment, there has never been a more convenient way to centralize data.


Fortunately, the health care industry is seizing the chance to upgrade patient care and follow the latest new data science innovations. To assess progress towards universal health coverage, medicine will need a robust health data-driven system. But what exactly can data science and medicine glean from the colossal batches of data points?

Data science can change the health care sector in so many ways. From health tracking to scheduling nursing shifts, data analysis backs up a value-based data-driven approach. This, in turn, allows to optimize the workforce and throughput, improves care recipients’ satisfaction, and balances the supply. On top of this, if you implement the right use of data science in healthcare, medical organizations can greatly reduce costs and re-admissions.


All of this makes data science medicine one of the most significant advancements made recently. In this article, we will answer the biggest question ‘how data science is transforming health care’ and have a closer look at hospital data science.

data science in healthcare


The role of a healthcare data scientist

The monstrous quantity of data being produced in studies and medicine are transforming our very perception of the basic biogenic process, clinical decision-making, symptomatic, and treatment decisions. It is shifting the way we approach population health in general. A data scientist in healthcare plays a huge role in data management.

By crunching numbers, data scientists in healthcare are exploring opportunities to predict drug behavior and better understand human disease. Data science in healthcare is the key feature of how we approach and use the medicine. Big data hype puts a health data scientist in a prime position. The term ‘data scientist healthcare’ was first used in 2008.

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A medical data scientist can take the data of any size and start developing, implementing, and deploying AI power. Healthcare data scientists use advanced statistical methods to do analytics and get meaningful insights from the data.

In general, the position of a healthcare data scientist entails the following responsibilities:

  • Collaborating with stakeholders to define the goals and the type of statistics needed
  • Accessing, updating, inserting, and manipulating large volumes of data
  • Organizing and coordinating patient data files
  • A hospital healthcare data scientist is the cleaning and managing  data to meet the company’s purpose
  • A public health data scientist is contributing to Public Health Datasets
  • Performing information base audits
  • Healthcare data scientists are carrying out data analytics for apps
  • Coordinating with different dev teams to implement models and monitor outcomes

Since we have answered ‘what is the role of data analytics in healthcare’, let us give insights into how data science and healthcare can become mutually beneficial.

healthcare data science


Top 5 Data Science Applications in Healthcare

There are countless big data use cases in healthcare that are opening doors for future development in medicine. From drug discovery to Python uses in healthcare, healthcare big data use cases are rapidly occupying the healthcare industry.

Data Management & Data Governance in Healthcare Industry

The opportunity for better data management is enormous. Moving towards better use of open standards, and better data sharing at the top level provides actionable insights about the Health Service operation. Machine learning will enable doctors to be more human and deliver better care. Data management is all about making information easily accessible to people who work in the healthcare industry.

As the health industry’s nature is risk-entailing, data crunching has to be ultra-careful to assess the current situation and possible outcomes. Moreover, data analytics for healthcare should remain up-to-date, complete, and profound.

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Data science for healthcare facilitates the process:

  • All medical records can be combined into one dataset (electronic health records), put away in the information distribution center, and effortlessly utilized for resulting model preparation and testing
  • All data can be digitized, collected, and shared over various sets of data and systematized, eliminating excessive office work
  • Extra sources and further analysis can help pinpoint and handle the disparity in clinical data
  • Cloud-based clinical software offers accessibility options and accelerates the process of historical data handling. It means saving time when deciding on the therapy or receiving lab results
  • Collecting and saving patient health information in internal and public health datasets enables medical staff to track conditions over time
  • Machine learning helps gather insights from accessible evidence, such as simplifying the process of drug discovery

While data governance is recognized as a healthcare imperative, opportunities exist for healthcare organizations to hasten the prioritization of data governance as a business imperative. The term encompasses rules, policies, procedures, roles, and responsibilities for managing the lifecycle of data.

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In its essence, data governance provides guidance to ensure that data is accurate, consistent, complete, available, and secure. It is also a key enabler in improving value and trust in information and achieving efficiencies and cost savings. Data governance plays a crucial role in patient engagement, care coordination, and community health. Without it, the data will be released inconsistently by different healthcare data science companies.

This, in turn, will lead to the perception of poor data quality. Therefore, healthcare data science apps ensure a more effective security approach and a more profound analysis of the system.


Workflow Optimization and Process Improvements

It’s a little-known fact that many big decisions are made with human ‘gut instinct’, as there’s little big data analytics in healthcare. Medical data science allows developing a personalized healthcare approach and helps healthcare organizations allocate time and workload more efficiently.

Here’s how data science in healthcare improves the workflow:

  • Information bases and distributed computing features can radically abbreviate the time required for the activity and increment the test outcomes’ precision
  • Less time and exact test outcomes lead to work process effectiveness development
  • Essentially, clinical staff get an opportunity to perform more tasks within a limited time span
  • Better effectiveness prompts higher recuperation rates, faster crisis reaction, and, above all, less deadly results because of sepsis and different elements that require a quick response
  • Health care recipients get digital interaction that is patient-centered

In addition to that, data science tools facilitate a superior structure to the human services framework’s general improvement. Each test, examination, guess, and treatment includes another case for  data science algorithms (machine learning), fortifying the worldwide social insurance framework’s logical limits.


Medical Image Analysis

Medical imaging refers to the process of creating a visual representation of the body for clinical analysis and medical intervention. It offers a non-invasive way for doctors to look inside the human body, or model organs prior to a procedure. With the rapid growth of healthcare and artificial intelligence, applications of data science in healthcare can play a key role in creating new opportunities for treatment and care. Among the various types of medical imaging is tomography or longitudinal tomography.

Its main methods are X-ray computer tomography (CT), PET, and MRI. Anyway, how data is science transforming healthcare in the given area? Well, medical images require accurate images with subsequent meticulous interpretation. Data analysis refines image analysis by enhancing such characteristics as:

  • Modality difference
  • Image size
  • Resolution

Supervised and unsupervised learning eases medical imaging by offering computational capabilities that process images with greater speed and accuracy, at scale. An excellent example of computer science power is a cancer detention case study that used CNN to diagnose melanoma.

The data sets, and their vast libraries, are the cornerstones of the examination. Entering data is contrasted with the accessible datasets, and the gathered bits of knowledge give a superior comprehension of the patients’ diagnosis.

Genetics/Genomics – Treatment personalization

When new technologies come along, whether it be various forms of genomic profiling sequencing or something else, this provides a new look at the genomics world. With huge genetic data amounts today, genetics data is now produced faster than it can be organized or implemented.

Part of this is because the methods for structuring data lag dramatically behind developing the ability to get data. Healthcare data science is a good thing, but you have to be able to make sense of it.

The challenges in the genomics area include the following:

  • Studying human genetic variation and its effect on patients
  • Identifying genetic risk factors for drug response

Thus, DNA Nanopore Sequencer is a tool that helps patients before they suffer from septic shock. It offers genetic sequence mapping, which abbreviates the time span of the information preparing activity. Additionally, the tool recovers genomic information, BAM document controls, and gives calculations.


Predictive Analytics in Healthcare Sector

Essentially, predictive analytics is a technology that learns from experience (data) to foresee a patient’s future behavior. It helps connect health care data science to effective action by drawing reliable conclusions about current and future events. And it allows healthcare to use predictive models to use models found in data science health. This, in turn, makes it possible to identify potential risks and opportunities before they occur.

However, here are some barriers to predictive analytics use. They include the following points:

  • Lack of seamless healthcare information exchange among healthcare systems and staff
  • Shortage of skilled workers to fill knowledge gaps

The following types of databases are required to eliminate these hurdles and facilitate the use of predictive analysis:

  • Medical records
  • Ongoing condition stats of patients
  • Medication databases (mental health)
  • Genetic research and other uses

Combined, data retrieval and deep learning can skyrocket the process:

  • Data Mining techniques pull out usable data from large batches of data
  • The illustrative, exploratory, and comparative calculations can combine numerous viewpoints into one and figure the best option for patients

In general, this type of healthcare analytics can:

  • Provide fast and accurate insights to utilize risk scores
  • Improve operational efficiency
  • Outbreak prediction
  • Control patient deterioration
  • Reduce costs from eliminating waste and fraud
  • Predict insurance product costs by applying data science in health insurance

Therefore, thanks to accurate predictions, patients have the possible benefit of better outcomes. At the same time, it will also allow the healthcare sector to build forecasting models that do not need lots of instances.

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Predictive Analytics & Health Data Science

The healthcare industry is evolving at lightning speed. Its main focus is predictive analytics, creating enormous opportunities to improve patient outcomes and reduce costs. Predictive analytics uses past data to model future results. It will likely help identify patients who are at the highest risk of poor health outcomes. It can also help in delivering personalized care through remote patient monitoring.


Clinicians can target these care recipients with customized health plans to avoid hospitalization and re-admissions. Specialists can leverage innovation like big data analytics, machine learning algorithms, and natural language processing, to draw useful conclusions for disease research. This, in turn, will allow patients to participate in their own care.

At the very least, this type of analytics can help clinicians anticipate problems prior to developing and mitigating health issues before they worsen. When you combine predictive analytics and big data, it proves to be a key competitive advantage for organizations today.

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PODCAST #18. AI’s Influence in Virtual Healthcare and How Product Managers Can Help in the Revolution

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.








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