16
 min read

Data as a Strategic Asset in Healthcare

Delivering the next generation of healthcare services, and powering business resiliency with Big Data and AI/ML driven insights

Data as a Strategic Asset in Healthcare

Improving patient care outcomes and optimizing health care delivery are the primary goals of healthcare organizations. These two objectives also drive the greater goal of reducing return hospitalizations, and reducing costs in the current environment of extreme resource shortages and increasing regulatory oversight. The healthcare industry, unlike other industries, is beholden to two parties in the transaction – the individual patient, and the payer (usually the US government or a private insurance company). Both individuals and payer are now becoming more demanding about outcome focused care delivery.

Healthcare systems have to retool their IT and data systems extensively in order to adapt to the new demands on the business. Illustrative data infrastructure needs include systems that support a deep understanding of the patient, new communications models for tracking post- visit patient health, personalized medicine, and telehealth and home care models.

While data technology is expensive to create and maintain, modern AI/ML and Big Data technologies are making it easier to organize data, discover patterns, and derive intelligence that can revolutionize all aspects of a hospital’s business. However, these technologies are rapidly evolving and skills shortage can hamstring your organization’s ability to develop and maintain analytical and operational systems.

Enter Datamorph - Kwartile’s self-serve data transformation platform. Datamorph is a suite of tools that optimizes development of modern data architectures. It shields developers from the complexities of the implementation, reduces development time, and allows engineers to focus on uncovering intelligence within the data. The platform abstracts out the complexities of distributed computing and deployment environments – the two critical facets that represent the most complex and time-consuming tasks of systems development.

Datamorph makes it vastly more convenient to create and maintain powerful data and analytical applications such as Electronic Health Records, advanced Patient ProfileManagement systems, Patient Behavior Predictors and Outcome Analytics, and Home HealthDelivery Analytics. Additionally, the platform is customizable and extensible, allowing engineers to implement any kind of complex business logic required for building custom business applications.

With Datamorph, healthcare businesses can create streamlined applications to obtain deeper insights into patient health, create advanced operational systems, optimize billing management, and achieve high-ROI on all investments – all with significantly reduced spending on IT skills and infrastructure.

Introduction

For much of the last decade the US and Global Healthcare Industries are facing a multitude of rapid and unpredictable changes ranging from tectonic regulatory shifts to technology disruption and crippling shortages in clinical staff. And then came the Covid-19 pandemic,stretching global health systems to the breaking point and rationing healthcare. Hospitals are essential infrastructure and have been propped up by governments and so the true economic fallout remains to be ascertained. Consider the following statistics:

  • The healthcare industry is the largest employer in the US employing more than 16 Million workers. 5 of the 10 jobs seeing the fastest growth in the next decade are in health care. But even then, the next few years will see a large shortfall in Doctors,Nurses, and Lab Technicians.
  • Despite all this additional labor, the most meaningful difference in quality over the past 10 years is the recent reduction in 30-day hospital readmissions from an average of 19 percent to 17.8 percent.
  • Global health care spending is projected to increase at an annual rate of 5.4% in 2018–2022 (much higher than in 2012-2017). The emergence of personalized medicine and new technologies are further increasing cost of healthcare.
  • The revenue make-up is changing quickly with out-patient visits now significantly exceeding in-patient visits. This trend will increase as technology innovation accelerates.
  • Payers are now demanding results - public and private funding models are changing with outcome-based care delivery model becoming a major requirement. Under the Accountable Care Act (PPACA), hospitals have agreed to roughly $155 billion in payment cuts from Medicare and Medicaid over 10 years. Similarly, Medicaid’s Value- Based Purchase program ties hospital payments to performance on several quality and patient experience indicators
  • Increasingly, Hospitals are competing with disruptive market entrants. As just one example, Pharmacies with retail health outlets are growing quickly.
  • Rapid advances in technology in all areas of care (diagnostic, biological and pharma, drug delivery, surgical, manufacturing), are also leading to cost increases as well as skilled labor shortages.

In response to the disruptions, the U.S. Healthcare industry is changing on multiple fronts:

  • Due to cost pressures, vertical integration is happening globally at a rapid pace. Mergers are taking place between Hospitals, Insurance companies, Pharmacy chains, Pharmacy Benefits Managers (PBMs), and even Ride-Hailing apps.
  • Healthcare systems are implementing rigorous financial management practices to control costs, and implementing technologies to improve operational efficiencies.
  • Hospitals are being forced to increase the type of care delivery sites as they look for new revenue models. These Value-Based Care (VBC) models require the development of a robust data infrastructure.
  • Greater burden of costs as well as care responsibilities are being transferred to patients with increasing premiums and at-home care delivery models.

Predictably, leaders are pulling away from the pack with increasing revenues while laggards are going out of business. Further strengthening this trend is the rise of digital adoption. Since the Pandemic, digital sales have skyrocketed and retailers that doubled down on digital have done much better than their peers.

Capability Requirements

Successful healthcare organizations will have to make significant investments to improve care outcomes, embrace technological advances, and expand delivery models – all while controlling costs. None of this is going to be possible without a capable data infrastructure. Indeed, the role of data will only increase rapidly in healthcare. Consider the following:

  • Consumers are increasingly using digital health, telehealth, wearable monitoring and fitness devices. Healthcare companies that provide online resources and embrace social media, and other technologies will likely be well-positioned to develop patient engagement strategies that help individuals make informed health care decisions. All of these technologies generate massive.
  • The healthcare industry is the most regulated industry. Major regulatory issues include Data privacy, Cyber Security, Drug cost tracking, European Data Protection rules(GDPR) for global companies. Compliance requires storing and tracking vast amounts of data.
  • AI powered nurses and Intelligent Virtual Assistants will help improve care delivery while reducing costs. AI requires huge datasets for training.
  • And finally, every single financial and operational objective of a modern healthcare system will require analysis of rapidly increasing amounts of data.

The healthcare industry must catch up to its technology industry peers in understanding how to deal with massive amounts of data. The industry must embrace mature data integration and management practices supported by the latest data processing software and infrastructure. These include:

  • Cloud Datawarehouses
  • Next-Gen Data Lakes/Delta Lakes
  • ELT Systems
  • Dataflow Automation
  • Self-Serve Analytical Systems
  • AI/ML Frameworks

More specifically, healthcare needs to embrace the paradigm shifts happening in data processing technologies, as illustrated below in Figure 1:

System Complexity

The above defined technology stack has great data manipulation and processing capabilities, but requires a highly skilled data team to organize the data, create processing workflows, and build operational data systems.

Add to this, advances in AI/ML systems which require complex training systems and a multitude of data stores for scenarios, and IT development operations can become unwieldy.

It is well known that about 85% of a data scientist’s time is spent on just identifying data sources and pre-processing steps. Similarly, application developers spend much of their time in dealing with the complexities of distributed computing environments (i.e., the multi-cloud),and the complexities of deployment environments.

The Solution

Kwartile’s DataMorph platform is a Low/No-Code Data transformation platform that reduces the hurdles to developing and maintaining modern data applications. The platform offers a powerful set of tools to create and maintain a modern data processing framework (see Figure 2):

Challenges of Modern Data Architecture

While the Modern Data Architecture solves many problems of data organization, data processing, data accessibility, and data governance, it creates a new set of challenges for developing data applications, including the following:

  • The complexities of
    - managing cloud-based data warehouses
    - maintaining deployment environments
    - implementing an optimized DevOps framework
  • Additional complexities of ingesting and transforming both real-time and batch data simultaneously
  • Inflexibility of Visual Tool Environments
  • Non-standard coding methodologies (SQL mixed with polyglot coding)
  • Non-standard logging environments

Due to all these challenges, the data applications programmer is faced with a steep learning curve. The underlying technologies are also advancing rapidly, making it even more challenging for developers to keep up.

What is needed is a way of reducing the quantity of coding required for developing and maintaining data applications, and managing DevOps, cloud deployments, and security frameworks.

The Datamorph Platform

Datamorph is a low/no-code platform that is optimized for developing data applications. By using the platform, developers can improve their productivity by 50-90% while reducing operational costs between 20-60%. In the long term, Datamorph provides many other benefits for software maintenance including automated version control, dependency management, and application security management. This leads to high code maintainability and improves the long-term economics of application development even further for companies.

At a high-level, the specific features and benefits of the Datamorph platform include the following:

  • Abstracts out the complexities of distributed computing
  • Abstracts out the complexities of deployment environments
  • Provides Visual tools for no code development but also supports custom code development
  • Fully extensible and flexible – Developers can add their own custom templates and code libraries
  • Includes deployment components for all leading cloud environments as well as on-prem environments
  • Sophisticated management of Type 2 Slowly Changing Dimensions into the warehouse
  • Data Skew analysis, which often becomes the nemesis of distributed systems
  • Built-in support for regulatory data compliance requirements including GDPR and CCPA deletes

Also, the platform provides many productivity enhancing tools including the following:

  • Design templates and architectural components for common use cases
  • Fast deduplication of data in streaming and batch mode
  • Built-in monitoring tools with unified interfaces
  • Rich data-quality detection library based on configurations
  • Provides pre-defined components and the ability to create customized component libraries

Case Studies

While the Datamorph platform is appropriate for almost any possible data application development scenario, some more representative healthcare use cases include the following:

  • Benefits Management and Claims Validation
  • Revenue and Cost Analytics
  • Patient Profile Management
  • Disease Management including Patient Behavior Analytics and Predictive Analytics
  • Real World Evidence, Personalized Medicine, and more broadly, Personalized Healthcare Delivery
  • Supply-Chain Analytics
  • Guidelines Compliance

Below are three representative case studies that the Datamorph platform has been implemented at healthcare customer sites.

  • Disease Management System
    A Disease Management system predicts hospitalization risk for individual patients, prompts timely intervention by doctors and nurses, and results in reduced spending on patients. Such a system requires historical data from the specific patient as well as large data sets of general patient data across wide geographic areas.
    There are significant challenges in unifying the various sources of data and create reference data sets that can be used consistently across multiple time periods. IT spends an enormous amount of time and effort in data wrangling and stitching together multiple internal and external systems in an effort to create the reference data. Modern data architectures now give the ability to create and manage such data libraries on the fly, without having to build traditional and difficult-to-maintain data models. Kwartile’s Datamorph platform makes it easier to create meta-models that can be applied in near real-time to create data sets usable for the purpose at hand.
  • Claims Verifications
    Claims Validation applications use large volumes of patient data to find unexpected patterns in diagnoses and treatments, detects incorrectly coded claims, and results in increased share of successfully rejected claims. Modern AI/ML techniques can automatically discover known and unknown patterns in claims data and uncover discrepancies, errors, intentional fraud and other forms of revenue leakage.
    The Datamorph platform provides powerful AI/ML models to implement pattern recognition systems. The vast amount of historical data that healthcare systems possess is ideal for AI model training. Once the models are trained, future analytical operations become easier to maintain.
  • Remote Patient Monitoring
    Many hospitalization and intervention programs (e.g., addiction recovery) require monitoring of data from devices and sensors attached to patients. In the past, this required significant hospitalization as the patient is hooked up to devices that had no remote connectivity. Now, with the advent of modern devices that are smaller and connected to the internet, patients can be sent home with the devices. IoT and streaming data technologies are used for monitoring, analysis, and even real-time intervention.
    The Datamorph platform enables integration of multiple data sources including IoT devices, with near real-time data processing, alerting capabilities, and development of predictive analytics and recommendation engines. All this in a fraction of the time required with conventional software development methodologies. Built-in templates and algorithm libraries enable significantly faster implementations.

Benefits Achieved

The rollout of the projects provided multiple benefits and capabilities to the care team, finance operations, and operational organizations including IT and Customer Service.

  • One of our healthcare customers realized over $28 M in NPV through revenues and cost savings over three years.
  • Another customer experienced a 417% ROI with a less than 6 month payback period.
  • On the Data Science front, the Claims Business units now has democratized access to operational data, powerful visualization capabilities, and pre-built dashboards providing actionable information and alerts. With the implementation of the Datamorph platform, the Data Team’s productivity increased by 25% over the last two years.

IT is now off-loaded and has successfully shifted focus to providing better data science capabilities and improving the systems needed for enabling a data driven culture.

In conclusion, data is increasingly proving to be key to a healthcare organization’s competitiveness. The next generation healthcare provider must make significant investments in modernizing their data infrastructure or risk survival. Kwartile’s Datamorph platform provides critical data application development capabilities and improves IT Team productivity.

Support and Services

Kwartile offers a robust set of solution and support services for the Datamorph platform. Our Product Support services offer multiple plans for Enterprises, Software Development professionals, and Partners. The support services include technical help during the warranty period, bug fixes, and regular version upgrades.

In addition, our professional services team offers data strategy, design, and application development services. Kwartile Consultants and Data Engineers possess a combination of deep and practical technology expertise, industry expertise, and strategic product management experience.