Tag: CitiusTech

Overcoming Challenges For Effective Contract Management In VBC

By Shyam Manoj Karunakaran, executive vice president of health plans, CitiusTech.

While VBC (Value based care) is the current focus point for the healthcare industry, it is important for organizations in this sector to ensure successful and efficient management in VBC. In this article, I would like to draw your attention toward the challenges in effective contract management and how best to overcome these.

Path to value for Value-based Care

The growing markets in healthcare are now centered around government-sponsored programs like Medicare Advantage (MA), the Affordable Care Act (ACA) marketplaces, and Medicaid. This trend is steering healthcare organizations towards more direct patient engagement and the management of high-risk, high-acuity patients. As healthcare organizations increasingly focus on Medicare Advantage, ACA, and Medicaid, they encounter a unique set of challenges, encompassing system integration, data interoperability, and effective data handling, among other critical aspects.

Addressing the SaaS Sprawl

Over the years, healthcare organizations have made significant investments in a variety of SaaS solutions to facilitate their day-to-day operations. These solutions, each housing data in different data centers or cloud environments, have become integral to their business processes. However, as the focus intensifies on managing high-risk and high-acuity patients, along with an increased emphasis on direct consumer engagement, there arises a critical need to integrate data and processes across this sprawling landscape of disparate SaaS systems. This integration is essential for a holistic view of patient care and efficient service delivery.

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Is Your Telehealth Strategy Aligned To The “New Normal”?

By Dhaval Shah, senior vice president of medical technology, and Neha Vora, healthcare consultant of medical technology, CitiusTech

In the current COVID-19 disrupted world, telehealth has seen unprecedented growth in adoption, as it minimizes the risk of exposure and aligns with the concept of social distancing. This has made healthcare systems accelerate the adoption of these services and also rapidly scale their processes to address the growing need of virtual care, as opposed to in-person visits and services.

And the acceleration is anticipated to continue for the foreseeable future. According to a report by Global Market Insights, the telemedicine market is set to be valued at $175.5 billion by 2026.[1] Today, more than 50% of U.S. hospitals provide telehealth services in some form or other,[2] and to meet the anticipated market growth, many more hospitals will adopt telehealth in the coming years.

Increased demand for remote/virtual care combined with federal and state derestriction has provided the much-needed stimulus for health systems to fast track their digital transformation journey in this space. Studies predict that 30% of all care will be delivered virtually post-pandemic as people start to see telehealth as their first point of contact for urgent care needs.

This brings us to the real question that each healthcare system needs to ask: “Is the current telehealth strategy aligned for the post-COVID world – the new normal?”

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Quality Is Critical In Today’s Data Deluge: Put Processes and Tools In Place For Robust Data Quality

By Rahul Mehta, senior vice president and head of data management proficiency, CitiusTech.

Rahul Mehta
Rahul Mehta

The sheer volume and variety of data, such as claims, EMRs, lab systems, and IoT now available to healthcare organizations is mind-boggling. The potential to pull data from these myriad sources to work for real-time care intervention, clinical quality improvement, and value-based payment models is unfolding fast.

Yet, as organizations seek to aggregate, normalize and draw insights from large and diverse data sets, the importance of data quality becomes apparent. Consider an activity as fundamental as identifying the correct patient. According to Black Book Research, roughly 33 percent of denied claims can be attributed to inaccurate patient identification, costing the average hospital $1.5 million in 2017.

For example, the average cost of repeated medical care due to inaccurate patient identification with a duplicate record is roughly $1,950 per inpatient stay and more than $800 per emergency department visit.

As data quality become more important, healthcare organizations need to understand the key characteristics that affect quality: accuracy, completeness, consistency, uniqueness and timeliness. However, data reliability and integrity also depend on other key factors, including data governance, de-duplication, metadata management, auditability and data quality rules.

With a strategic approach, healthcare organizations can employ a unified data strategy with strong governance for data quality across all data types, sources and use cases, giving them the ability to scale and extend to new platforms, systems and healthcare standards. The result is an approach that uses a combination of industry best-practices and technology tools to overcome common challenges and assure data quality for the long term.

Understanding Data Quality Challenges

Historically, providers and payers alike treated data quality as a peripheral issue, but that is no longer viable in today’s complex data ecosystems. First, there are a diversity and multiplicity of data sources and formats: EHRs, clinical systems, claims, consumer applications, and medical devices. Add to that, challenges associated with legacy applications, automation needs, interoperability, data standards and scalability.

Lastly, there are increasing numbers of use cases for clinical quality, utilization, risk management, regulatory submission, population health, and claims management that need to be supported.

Considering the current data environment, the downstream effects of data quality issues can be significant and costly. For example, in the case of patient matching as referenced above, something as common as two hospitals merging into the same health system, but following different data-entry protocols, can lead to duplicate and mis-matched patient records. It can also lead to critical patient data elements, such as date of birth, being documented differently by different facilities and then made available across multiple systems, in varying formats.

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AI Is Not The Future, It’s Now

By Jeff Springer, senior vice president of healthcare solutions, CitiusTech; and Fernando Schwartz, vice president of data science and consulting, CitiusTech.

As consumers, we experience artificial intelligence (AI) every day. In fact, we’ve come to expect the consumer experience it enables, such as Amazon’s highly personalized suggestions for additional items we might like. Now is the time to gain AI’s rewards in healthcare as well. Dr. Eric Topol, founder and director of Scripps Research Translational Institute, put it this way: “If properly and humanely deployed, AI has the potential to restore efficiency to a wide array of burdensome healthcare processes, freeing up physicians to treat their patients in the way they deserve. The path won’t be easy, and the end is a long way off. But with the right guard rails, medicine can get there.” [1]

While there are significant challenges with interoperability, data sharing, and data access, organizations are taking several approaches to overcome them. With an incremental approach to data management and analytics, healthcare organizations can reap the benefits of AI – specifically machine learning (ML) and natural language processing (NLP) faster, enabling them to overcome challenges and achieve success in value-based care.

AI and Its Subsets

First, it’s important to have a shared understanding of AI concepts. AI is a term used to describe the ability of machine intelligence to imitate human intelligence through cognitive functions and behavior. Both ML and NLP are subsets of that broader concept. ML applies algorithms and statistical models that effectively perform a specific task or make predictions using patterns and inference. NLP enables computers to understand, interpret and manipulate human language using computational linguistics. At the highest level, these tools can be applied in healthcare to help find the answers to questions and identify root causes—leading to workflow improvements at a massive scale.

Technology Advances Speed AI Adoption

Advances in data and analytics technology are making it possible to significantly reduce implementation time for AI projects. From a data management perspective, five years ago, it would have taken two years to implement a strategy and infrastructure for data management needed for AI. Hence, many organizations applied a project-by-project approach and didn’t take advantage of the opportunity to reuse data for multiple projects. The results were data silos and missed opportunities to leverage data across the enterprise.

Today, new technology and new data approaches expedite projects from the data, analytics and learning perspectives. Specifically, today’s late-binding architecture enables organizations to take only the data needed for a question, metric or pattern, and then curate additional data as needed. This opens the door for going beyond the standard healthcare sources, such as claims, HL7, and CCDA, to also include historically cost-prohibitive data, such as social media, benefit information and unstructured data. These data sets can be leveraged to determine the most effective engagement models, risk patterns and communications. Today, if organizations start by asking the right questions before implementing a new data set, they could have answers in as little as three months, rather than years. By leveraging existing infrastructure and data in conjunction with new technology, organizations can begin to see success more quickly, while building out an incremental, long-term strategy.

Putting AI to Work Answering Healthcare Questions

When organizations take advantage of the new technologies that enable sophisticated data analytics, they can more easily apply ML and NLP to specific data sets. Start with a question and determine what data can best provide answers. The examples to follow illustrate questions organizations might ask to improve clinical outcomes, revenue assurance, or operational efficiency.

Clinical Best Practices: Using ML, healthcare organizations can more effectively analyze treatment patterns by asking questions, such as: What interventions during a clinical encounter help avoid sepsis? Or how can an ED visit be prevented? Once there is an understanding of what contributed to positive outcomes, organizations can embed care protocol improvements within clinical workflows.

Population Health: Again, using ML, organizations can thoroughly analyze populations, treatment patterns and results by asking questions, such as: Which segments are having trouble and how can they be addressed? Are issues arising that are related to geography, benefit structure, or disease pattern? ML can be applied to sift through many dimensions and identify root causes. For example, a member of a diabetes population segment lives within a certain zip code, sees a certain provider, and is more likely to be readmitted. By asking the right question, variations in care can be identified, and protocols and workflows can be adjusted to improve outcomes.

Utilization Management: Healthcare organizations, especially ACOs, strive to improve quality while tightly controlling costs. Using ML, organizations can look for variations in practice patterns across providers, patients and conditions. From there, organizations can implement protocols that operationalize utilization management. For example, duplicate exams, such as expensive MRIs, can be avoided by making recent results available to providers at the point of care. And providers can be guided to the most cost-effective location for a given procedure or exam.

Data Aggregation: Even with standard EMR implementations, there can be tremendous variation in how certain types of data are captured. For example, data points needed for population health management may be captured in data fields and as unstructured text. Using NLP, healthcare organizations can now parse many different types of data from many sources. This enables access to data for risk-based contracts, such as social determinants of health like transportation, weight loss, food insecurity, and electricity.

Apply AI to Gain Quick Wins in Quality Improvement

With today’s technology, healthcare organizations can move more quickly to take advantage of AI, especially ML and NLP— seeing results in months rather than years. With modern data and analytics approaches, organizations can proceed incrementally to identify questions, metrics or patterns that deliver quick wins in clinical, financial and administrative outcomes. At the same time, these achievements contribute to a successful long-term strategy to continuously improve quality and outcomes while assuring appropriate payment in today’s value-based care environment.

[1] Miliard, Mike, “Eric Topol: EHRs have ‘taken us astray,’ but AI could fix healthcare in a ‘meaningful and positive way.” Healthcare IT News, March 12, 2019.