Healthcare Analytics Roadmap : Actionable Intelligence from Data

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Healthcare Analytics : The talk of the town

Healthcare analytics is a robust field that has many different factors at play. From insights to intelligence, the journey is long and arduous, but many companies are already mastering their approach. They’re creating greater value in the domain by affecting the way that data has an impact on the institution as a whole.

They’re also designing better systems that incorporate various data points into the final algorithm while creating value from the get-go. It’s essential for all companies to have a real intelligence-focused approach when it comes to the healthcare industry. It’s not necessary to know how many patients get a specific treatment done. It’s more insightful to understand what the success rate is a few years after and how well they’re doing holistically. Insights like these are why analysts are predicting huge events in the healthcare analytics space.

The problem of understanding the PROBLEM

It's essential for companies to work on identifying the issue at hand. This comes when you have a clear understanding of what impact data can make for the healthcare space and where you need to add higher value. The journey from data to insight takes place when you have a clearer vision. The entire industry has the same data tools available. What makes your healthcare firm unique is how you define the core problem.

Whether that’s interoperability or managing robust data systems, the problem should be captured in one value-oriented statement. That statement should be comprehensive enough to win the entire ecosystem in one go. That being said, there are more significant advantages of involving multiple stakeholders in the problem statement. They can add value in discussions and create a collaborative environment around the subject area. They can also generate exciting insights that emerge organically and define areas where ideas need to be uncovered.

Healthcare firms may find value in discovering the impact of a specific treatment facility or the resources necessary to take care of Alzheimer’s patients. It takes data to understand the core problem, after which the journey is much easier for data analytics. At that point, companies have already defined the core problem and made the role of data analytics much more comfortable at that event.

Healthcare analytics can be integrated back to that core problem, which is now well defined. This also leads to a robust framework that incorporates the core problem determining. It also enables us to be leaner in our approach and involve a multitude of professionals from different fields into the overall discussion.

The Healthcare analytics market is expected to grow at (CAGR) of 15.8% to nearly $14.9 billion by 2022. This is largely driven by a core problem area that companies define from the beginning. Whether that is gaining greater traction or reducing redundancies in the system, there is greater scope involved in problem defining.

Developing a Healthcare specific Data Intelligence Infrastructure

AI Data visulization use risesUsing emerging technologies in the space of integrated data intelligence, healthcare companies can create higher value for all stakeholders in the ecosystem. From improvising clinical decision making to enhancing medical devices, AI can be leveraged across the board with an institution’s data intelligence framework. It’s essential to have an eye out for data intelligence tools and technologies so that a thorough ecosystem can be developed over time.

2019 is the year for developing more coherent database systems and a more complete data architecture. There are greater returns in developing a more comprehensive approach when it comes to the data infrastructure. From managing EHR to analyzing complex reports, the primary utility of the data intelligence model has evolved into something more significant.

Companies are even designing smart apps that combine data intelligence to create better patient lifestyle choices. Apple is partnering with Aetna to create a native application that works with data from a holistic perspective. It is adding new meaning to the data intelligence space by putting the power back into the hands of the patient.

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When it comes to healthcare analytics, it’s about empowering all the stakeholders in the ecosystem. That’s the best way to ensure better outcomes for insurers, healthcare institutions, and patients that are taking better care of their health.

That’s why companies should review their existing healthcare infrastructure to uncover more profound insights and create a more robust architecture.

Companies should target all areas of the healthcare ecosystem to create a more robust data infrastructure. They can then focus on predictive analytics and forecasting trends that may emerge organically. This can help in allocating resources better and creating higher value for the stakeholders involved.

The three types of Healthcare Analytics

  • Descriptive

This is the initial format of analytics which takes insights from prior data. It’s one of the more preliminary forms of analytics as it involves receiving data from records and creating new insights that are based on them. Using data aggregation and data mining, the technique allows us to answer the question of “What happened here?”

It’s also a great tool if we want to understand what happened in the past format of treatments, patient care, and healthcare results. It’s useful to create greater awareness about the problem from a technical perspective. From a healthcare analytics point of view, it’s one of the more effective methods of conducting research allowing us greater insight into the past.

From the past, we can create more in-depth predictions for the future. The core technology required for us to move from data to insights is present in descriptive analytics. It’s also our first step into really uncovering the value that big data and data intelligence can bring to us.

Using sophisticated statistical models and regression testing, we can create insights using descriptive analytics. We can drill down to the minutest of details to understand how a particular treatment went or how patient data evolved. It can also help us predict according to records.


“Healthcare firms are well represented this year – a sharp uptick from previous years. These are businesses that are undergoing a rapid transformation—data rich, but often less data-mature. This surge in participation highlights the rising priority of big data and AI within healthcare and the increasing data maturity of healthcare firms." – Lead Research New Vantage Partners.


While data moves from one aspect to another, there is a more significant impact that healthcare professionals can deliver by using descriptive analytics. Disruption emerges in the healthcare system by understanding how certain moving parts interacted with one another in the ecosystem. Whether there are inefficiencies in the treatment facility or a core gap in understanding, better disruptive analytics can help uncover the core reason behind why.

From here, the data insights achieved can help us uncover a solution from this point. This is when we start entering the stages of predictive and prescriptive analytics. In the healthcare space, this is when companies start moving from insights to intelligence, as they can take actionable steps. Analysts have predicted a massive growth in these stages, as more healthcare firms want to achieve competitive advantages.

Analytics Value Escalator

  • Predictive Analytics

In the predictive analytics space, companies are creating actionable insights that allow us to manage the future better. Whether that be AI-enabled pace-makers or better bariatric surgery equipment, forecasting is always a great way to ensure success for the long-term. It also allows us to focus on creating a more enriched environment with data analytics.

That’s represented in some of the work at Sensyne Health and the University of Oxford. They’re leveraging Big Data to make predictions on chronic illnesses around the world. They’re then able to manage these diseases better and create a more robust ecosystem for development of solutions.

With the aid of predictive analytics, healthcare firms can find greater efficacy in treatments. They're also able to predict demand from a more holistic perspective. Additionally, healthcare analytics can also help us bring a more data-based approach to a better predictive strategy. When predictive analytics can be introduced to the core strategy within the healthcare firm, it makes for a more robust ecosystem.

The Healthcare business and financial analytics market is set to reach $50B by 2024 and is backed by some of the brightest minds in the industry. The healthcare industry runs on predictive analytics to create better outcomes for patients and healthcare institutions. From medication adherence to new cancer treatment, predictive analytics can save lives and improve the condition of millions around the world.

That’s mostly the result of the ongoing efforts in the predictive analytics AI enabled stage. AI is leveraging big data to create better predictive models. It’s also allowing the companies to develop more stable models that are increasingly relying on the more significant impact being formulated. AI is also taking center-stage, especially when it comes to wearables, predictive health, and chronic illnesses. This is where predictive analytics can shed real light into the problem and create models that can allow for greater efficiencies.

The healthcare system, as a whole, becomes more efficient when predictive analytics are involved in the picture. It creates a more robust environment that can flourish by using the latest technologies and techniques in prediction and development. It’s also enabling less technically savvy individuals greater access to valuable insights. There is a more coherent ecosystem being developed with the help of automation and technology with many healthcare institutions leveraging them effectively.


"Like every healthcare IT department that I know of, we have historically faced significant delays in responding to requests for data analysis, simply because the process has always required a data analyst. Rapid Response Analytics is quickly making us—and our clinical partners—significantly more productive by enabling us to get data slicing and access out of the hands of the IT department and into those of less technical people who can now use applications to answer their questions themselves, without our help.”- John Sanders, Chief Information Officer of Health Share of Oregon (most substantial coordinated effort)


Predictive analytics helps make it easier for non-technical members of the healthcare space to become more technically adept. It creates a more straightforward dashboard that gets to the root of the problem at hand. Through predictive analytics, there are more significant cost savings and efficiencies across the board.

  • Prescriptive Analytics

Prescriptive analytics comes into the picture when there are genuine insights to uncover. This is where companies go from an idea into intelligence. When there is good talent added onboard, this brings the discussion towards true-intelligence. That's the ultimate stage, where healthcare analytics is put to the useful utility. It's also where intelligence becomes a value add to the healthcare community at large.

Prescriptive analytics also enables companies to become better at finding the gaps. This makes it clearer to excel in certain areas within the healthcare space. Whether that’s Machine Learning for better analysis or Blockchain for data retrieval, prescriptive analytics leverages the power of both to uncover insights. The healthcare space is driving innovation in the tech space by demanding more tools to perform better prescriptive analytics. It's shaping the discussion we're having around analytics affecting healthcare outcomes. Through prescriptive analytics, healthcare professionals can scale solutions effectively.

As greater unstructured data is being presented to the organization, companies might mistake prescriptive analytics for predictive analytics. They may form results over predictions of the future. They may also formulate strategies based on predictive data, while not truly understanding the pathways towards greater integration.

Since running trials, uncovering new solutions and creating efficiencies comes at a cost, healthcare analysts must understand the importance of prescriptive analytics. As companies scale up over time, prescriptive analytics helps uncover the right pathway towards success. It also enables companies to create value that revolves around the right strategies.

The new e-Health infrastructure depends on prescriptive analytics to dig deeper into data to create value. This is also true in the case of patent trials and conditions that must be satisfied for stage II and III of testing.

This is also applicable to testing mechanisms that rely on unstructured and multi-state data. At a strategic level, companies must make the right call using prescriptive analytics and predictive analytics. The combination of the two brings real value to the ecosystem while creating a more profound impact in the strategic sphere.

Empowering healthcare analytics talent

Accurate intelligence is achieved when there is a dedicated team developed to uncover insights. It’s not essential to have the right tools or requirement present to create ideas. It’s equally important to have the right team current to enable the company to uncover some of the steps and action plans to execute.

Empowering healthcare analysts is one of the more critical components of developing true Data Intelligence. Without that, we’re just in the data insights part of the journey.


"This issue and the low percentage of companies that have achieved data-driven organizations and cultures suggests the need for a new focus. Respondents clearly say that technology isn't the problem—people and (to a lesser extent) processes are. We would guess with high confidence that the great majority of spending on big data and AI goes for technology and its development. We hear little about initiatives devoted to changing human attitudes and behaviors around data. Unless the focus shifts to these types of activities, we are likely to see the same problem areas in the future that we've observed year after year in this survey." - Randy Bean NewVantage Partners CEO


Companies are making the right investments as we move forward to 2019. We see more significant results in the drugs delivery, insight platform-technology, and chronic illness tackling from research houses. All of this has been because of highly trained doctors and healthcare professionals that can leverage their core Bio-Tech knowledge to discover new pathways. While the technologies become sharper with time, the talent as evolved as well. This has led to a greater focus on healthcare analytics as an area of excellence within all healthcare domains.

Having the right team sets healthcare IT Solutions providers apart from each other from a technical perspective.

Pitfalls of mistaken data intelligence

Companies might sometimes slip data insights for data intelligence and make critical errors in the healthcare domain. They can also cause mistakes in the space of consulting when they have the data present. Here are some of the pitfalls of mistaken data intelligence.

  • Confusing more data with more insight

This is a common misconception that many healthcare analytics firms make, and many teams do as well. They create more data and mistake that for more excellent ideas being developed. They also focus on uncovering insights in a too broad approach.

This dilutes the research, making it useful to only a handful of people. For research to indeed be relevant, it must be specific enough to appeal to a unique audience set. That’s when the most significant impact can be made with data insights. Healthcare analytics needs to dig deeper into the data to uncover greater insights.

  • Insight doesn’t equal value

Value is arrived through intelligence and revealing insights through big data. That’s when healthcare analytics indeed shows its worth. From a data operations stand-point, it makes sense to create value in specific areas. E.g., if healthcare analytics uncovers better ways of enhancing medication adherence, then that’s a good value-add there.

If it merely states the problem in another format, then that’s an insight that doesn’t produce value. In the end, it’s all about how much value your ideas have delivered.

  • Incorrect implementation of data

This is another area of non-value where healthcare analytics suggest specific routes that aren’t necessary to follow. They create inaccurate assessments that companies start to develop, but the results aren't there.

That’s when data has been incorrectly implemented. There can be more significant results with data intelligence if there is a path that is formulated through data insights. Only insights alone can’t create value as discussed above. There needs to be a more thorough approach to implementing data in the right way.

  • Creating insights from bad data

It’s essential to maintain data integrity when it comes to uncovering insights. Healthcare analysts may feel tempted to generate ideas from bad data or may not know the actual impact of bad data. That’s when they need to step back and understand the data value that they’re bringing to the solution.

They need to rework back on that data to produce greater insights from better quality data. Whether data capture was the issue or data interpretation, it’s essential to have a more transparent approach when uncovering insights from data.

There are many other pitfalls when it comes to mistake data intelligence incorrectly. However, it's important to remember that for effective implementation and scale, information needs to be crafted on the grounds of data. Without data, taking the right steps will not be justified in any situation.

In the case of hospital operations, it can create additional loopholes. In the case of medical delivery, it may delay shipments. Data intelligence is problem-based and is focus on solving real-world problems within the healthcare space. The real impact of healthcare analytics is uncovered using that approach.

Additionally, healthcare analytics should be developed on as a core asset within the healthcare institution. The framework includes all three types of data analytics (descriptive, predictive, & prescriptive). Along with that, when companies hire the best in talent and execution, they can create a more robust approach to data intelligence. That’s when companies can go from data insight into information. Adding perspective is more important than predicting the next trend within the healthcare space. It's critical to evolve and create a value-centric approach when it comes to healthcare analytics.

Conclusion

Healthcare analytics must be seen as a unique value-driver within the organization. It has added in various fields, including patient care and better treatment outcomes. Healthcare data should not be confused with analytics, as healthcare analytics is a unique part of the overall intelligence ecosystem. The journey from raw data to insight to intelligence is a complex one, but it has multi-fold returns from a global perspective. It’s not just essential to have a data-based mindset. Companies need to enforce real value-based intelligence that creates an impact from a holistic view.