5 Key Non-Traditional Areas of Cost Optimization in Healthcare

A recent analysis from the Office of the Actuary at CMS, reports that the national healthcare spending reached a total of USD 3.8 trillion before the pandemic hit the world. Conversely, there has been a steady decline of around 20% and 34% in inpatient and outpatient volumes, respectively. As a result, the American Hospital Association (AHA) predicts that the higher-than-usual supply expenses and other pandemic-related costs can lead to USD 323 billion loss for hospitals and health systems across the country. To curb the mounting expenditures in the healthcare industry, healthcare CXOs are shifting focus to cost optimization strategies. However, before focusing on cost optimization, one must consider:

  • Evaluating the role of IT in improving patient care
  • Prioritizing technology initiatives to address medical costs
  • Being prepared for internal backlash on account of budget cuts

Here are five non-traditional areas that can be optimized to bring down healthcare costs.

Medical Waste Management and IoMT

Medical waste in hospitals could have a severe impact. The economic costs associated with medical waste range from USD 760 billion to USD 935 billion, as improper management invites increased premium and out-of-pocket medical expenses, thus increasing healthcare costs. Investing in care coordinators, community health workers, and social workers can help to improve quality and cut costs. Waste management monitoring through sensors and IoMT can help hospitals become more efficient and remain compliant with various regulatory bodies such as OSHA.

Care Management and Process Optimization

In a dynamic environment, a lack of process standardization can increase the chances of variations in patient outcomes, resulting in longer LOS and increased costs to the healthcare system. Leveraging the power of data and data-driven processes, healthcare providers can standardize processes and identify areas that require improvement in terms of care delivery. Over a period of time, the data can help implement evidence-based workflows that improve consistency and coordination across processes.

Diagnostic Testing and Predictive Analytics

A report states that the costs of diagnostic testing account for more than 10% of all healthcare costs. However, there is also a rise in death or disability, with more than a million a year harmed by diagnostic errors, in the US. The repercussions of wrongful diagnosis can insure both direct and indirect costs that include medicolegal costs, increase in medical liability premiums, among others. While there is still an ongoing debate to determine whether diagnostic tests are being overused or underused, the cost factor of unnecessary testing must be taken into consideration. With the growing use of technology in healthcare, hospitals and other medical facilities must leverage electronic health records (EHR) for information transfer across care teams. The data acquired from EHR can be used in combination with new-age technologies such as predictive analytics and machine learning to minimize human errors and costly adverse events.

Data-driven Decision Making

The World Bank reported that the US leads the world in healthcare spending, with almost 18% of its GDP contributing to healthcare costs. One of the focus areas of cost optimization is the quality of consolidated patient information available to physicians, as it is critical to improving safety processes and quality. Lack of insight into patient information and process management leads to an increase in cost due to length of stay or complication rates. To manage frontline costs, it is imperative to establish a robust data-driven system. Using systems such as EDW will enable physicians to get real-time answers to clinical quality improvement queries, thus giving them the opportunity to analyze LOS and make necessary changes. Simply put, a hospital can improve its productivity by up to 26% by creating an environment for better decisions, thus creating more opportunities to optimize cost.

Supply Chain and Standardization

While focusing on increasing the volume, revenue, and growth of hospitals, one of the areas that is affected due to lack of attention is the supply chain. Gartner reports that the total healthcare supply chain cost averages to 37.3% of the total cost of patient care. The supply chain in a hospital can be affected by inefficiencies, service duplication, and poor labor management. To optimize costs in this segment, hospitals must focus on standardizing processes, manage physician schedules, and leverage health systems to handle patient access and flow. To build resilience within the healthcare supply chain, the top management must implement preventive measures such as improvements to data analytics and supplier visibility, and external intelligence focused on the healthcare supply chain.

Having discussed the different focus areas for cost optimization, it is important to implement these strategies wisely. While implementing a cost optimization strategy, one must follow these four rules:

  • Have a clearly defined area of focus
  • Build a functioning operating model
  • Learn and implement the right lessons
  • Demonstrate sustainable value

According to Gartner experts, avoiding reactionary cost-cutting in favor of purposeful prioritization requires a partnership between CIOs, CFOs, and CEOs. To that end, Gartner recommends the following:

  • Adopt scenario planning strategy to identify processes and technology impacts using various parameters such as TCO, ROI, and value creation
  • Expand IT portfolio in clinical processes using RPA, ML, AI, and NLP capabilities
  • Evaluate, review, and protect funding of transformative technology projects in different areas such as administrative modernization, patient engagement, etc.

Healthcare Transformation Using Cognitive Services

Srinivasan Sundararajan

Democratization of AI in Healthcare

Organizations are becoming increasingly digital and Artificial Intelligence is being deployed in many of them. Often small tech-savvy start-ups and large firms with huge funds, like those in technology and finance businesses, are deploying sophisticated forms of AI.

But several other companies are being left behind. They may not know how or where to deploy AI, or they may not have the resources to create their own AI. Cloud technologies are filling this gap. With options from Google, AWS, Microsoft, and plenty of other vendors, companies can begin exploring how AI can help them. The more that AI becomes accessible, the more companies – and users – can leverage it for their benefit. 

Healthcare is often cited as an area that AI can help immensely. The democratization of AI in healthcare, which is being driven by cloud technologies, is leading to greater access and more predictive work in patient monitoring and smarter reactive responses to health issues. 

ML and AI have traditionally been perceived as the domain of experts and specialists with PhDs. While democratization of AI is viewed differently by different organizations, a common theme has been to make AI adoption simpler.

The following are a few democratized AI services available as part of cloud providers (most of the examples are from Microsoft Eco System as a reference, however other providers also have similar services).

Handwriting Recognition

ai-led operations consulting firm in healthcare

With Windows Ink, you can provide your doctors with the digital equivalent of almost any pen-and-paper experience imaginable, from quick, handwritten notes and annotations to whiteboard demos.

The Windows Ink platform, together with a pen device, helps create digital handwritten notes, drawings, and annotations. The platform supports capturing digitizer input as ink data, generating ink data, managing ink data, rendering ink data as ink strokes on the output device, and converting ink to text through handwriting recognition.

There are equivalent options in other platforms like iOS and Android which can be used for making similar applications for doctors.

Optical Character Recognition

ai-led operations management services in healthcare

Azure’s latest OCR technology Computer Vision Read API extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents. It can extract text from text-heavy images and multi-page PDF documents with mixed languages and detect both printed and handwritten text in the same image or document.

Most hospitals have to deal with lot of documents, especially when it involves external parties like insurance companies. Healthcare organizations can increase productivity and cut down on costs by investing in OCR for managing medical documents.

Emotion APIs

The Azure Face service provides AI algorithms that can detect, recognize, and analyze human faces in images. Facial recognition software has varied applications like in security, natural user interface, image content analysis and management, mobile apps, and others.

Using this API, we can detect perceived facial expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and more. It is important to note that facial expressions alone do not represent the internal states of people.

Speech Translation

ai-led product engineering services in healthcare

The number of people in the U.S. who speak a language other than English is large and growing. Language barriers have been found to impede access to care, compromise quality, and increase the risk of adverse outcomes. When friends and family interpret, they are prone to omit, add, and substitute information.

The Azure Speech Translation API can translate incoming speech into more than 60 languages. This API enables real-time, multi-language speech-to-speech and speech-to-text translation of audio streams. With the Speech SDK, your applications, tools, and devices have access to source transcriptions and translation outputs for provided audio. Interim transcription and translation results are returned as speech is detected, and results can be converted into synthesized speech.

Health BOTs

AI for Health Cloud Enablement Services

  • Providers have built health bot instances that triage patient issues with a symptom checker, help patients find appropriate care, and look up nearby doctors.
  • Insurers have built health bot instances that give their customers an easy way to look up the status of a claim and ask questions about benefits and services.

Azure Health Bot empowers developers in healthcare organizations to build and deploy AI-powered, compliant, conversational healthcare experiences at scale. Combining a built-in medical database with natural language capabilities to understand clinical terminology, it can be easily customized for various clinical use cases. The service ensures alignment with industry compliance requirements and is privacy protected to HIPAA standards.

Conversational intelligence also adapts dynamically as the health bot instance learns from previous interactions.

Text Analytics for Health

The healthcare industry is overwhelmed with data. They face an incredible challenge in trying to identify and draw insights from all that information. Unlocking insights from this data has massive potential for improving healthcare services and patient outcomes.

The Key Phrase Extraction API evaluates unstructured text, and for each JSON document, returns a list of key phrases.

AI/ML Led Solutions for Life Sciences

The Text Analytics API lets you take unstructured text and returns a list of disambiguated entities, with links to more information on the web. The API supports both Named Entity Recognition (NER) for several entity categories and entity linking.

Text Analytics for health performs NER, relation extraction, entity negation, and entity linking on English-language text to uncover insights in unstructured clinical and biomedical text.

Reinforcement Learning

AI Tools for Digital Transformation in Healthcare Industry

Medical diagnoses essentially involve mapping patients’ medical history, current symptoms, and other information to the correct disease profile. It can be an incredibly complex task representing an enormous burden (in both time and cognitive energy required) for busy clinicians.

Personalizer API uses reinforcement learning to select the single best action, known as reward action ID. Azure Personalizer is a cloud-based service that helps your applications choose the best content item to show your users.

Personalizer currently uses Vowpal Wabbit as the foundation for the machine learning. This framework allows for maximum throughput and lowest latency when making personalization ranks and training the model with all events.

Anomaly Detection

digital transformation in healthcare it consulting

Anomaly detection in medical treatment can be used to discover deviations from regular patterns and determine whether the patient management is unusual. Detecting an anomaly from medical images including mammograms, CT, or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis.

The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. Using your time-series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send and determines if the target point is an anomaly.

Conclusion

Healthcare transformation requires a great amount of AI integration and implementation.

However, most healthcare organizations don’t have enough resources and bandwidth to work on AI development and deployment. Also, AI Involvement by its very nature is iterative and more concentration is required on involving the stakeholders and arriving at a consensus. Remember, the success of AI depends on the richness of data which is the main responsibility of healthcare organizations, while implementation of AI can be taken care of by these cognitive services.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi-Modal databases, Blockchain, and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero-knowledge proofs.

Healthcare at GAVS

Kushboo Goel

GAVS is not new to Healthcare services, and yet a Healthcare vertical is new at GAVS. GAVS acquired its first Healthcare client BronxCare Health System over 10 years ago in 2010. For 10 years, GAVS has been the go-to technology partner at BronxCare. From managed infrastructure services, data migration, application support, security and storage, and most recently virtual desktop solution during the COVID-19 pandemic, GAVS has not only earned deals at BronxCare as a solution partner but earned a relationship that led to several other healthcare clients such as the Jewish Board Behaviour Health centre. In a span of 10 years, the number of healthcare clients at GAVS has grown significantly to today contributing over 55% to our overall revenue.

ai-led operations consulting firm in healthcare

The unintended focus on healthcare was further strengthened when we launched Long80, a joint venture with Premier Inc. that has a network of 4000+ hospitals in the US. While BronxCare lead us to the backwaters of the healthcare industry, Premier has led us to the ocean. At GAVS we are determined to maximize this opportunity, but that means we must trade our boat for a ship!

This process has been ongoing at GAVS for the last 4-6 months. These efforts have included bringing on healthcare domain experts, having multiple conversations with our existing healthcare clients to understand the key pain points they face, gaining an industry perspective through conversations with multiple healthcare analysts to name a few. Perhaps our commitment to building a healthcare vertical is most evident through the setting up of the GAVS Healthcare Technology Institute, with the prestigious IIT Madras as our teaching partner. Through the institute, GAVS will empower its workforce with deep knowledge in the Healthcare domain in areas like Population Health Management, Revenue Cycle Management, Health informatics to name a few. In addition, courses will cover AI/ML methodologies with a focus on application to healthcare use cases.

To start this journey, we began by refreshing our existing healthcare offerings. Previously we offered mostly horizontal solutions to our healthcare customers. These included infrastructure services, app development and management, cloud migration, offshore product development and most recently cybersecurity. While integral to the provider and payer operations, most of these services were not consumed by front-line workers and did not directly influence delivery of care. Now, with a revamp of our healthcare services, GAVS offers three news offerings directly influencing quality of care delivery and clinical outcomes.

The first is Business Process Automation. Healthcare, a highly regulated industry is plagued with multiple administrative processes. These processes are low-hanging fruits for automation and cost reduction. By partnering with automation partners GAVS has successfully automated processes for existing clients and is in the process of identifying additional use cases for process automation with our existing clients.

The second is Data Modernization. Any given healthcare provider has between 20-50 applications running in their hospital. This includes Electronic Health Records, Radiology Imaging Systems, Billing Systems, Payroll, Scheduling, etc. In addition to the data residing on these systems, patients now generate healthcare data on their mobile devices through fitness apps or wearable devices. This growth in IoT has led to data in healthcare doubling every 24 months. This presents an unprecedented opportunity for GAVS to help healthcare providers and payers to integrate and manage data effectively to create a single source to truth, and allow for interoperability, giving patients and providers a holistic view of their care. 

The third, is Advanced Analytics. The increased access to healthcare data combined with rising cost pressures has been the driver for healthcare organizations to focus on ‘wellness care’ instead of ‘sick care’. Through predictive analytics, AI/ML-driven solutions, providers and payers use historic data to predict future outcomes such as the risk of certain diseases.  GAVS currently leverages its data scientists and developers to offer these services to a handful of clients. The immediate focus moving forward is to find additional use cases in both the operational and the clinical space to further expand this capability.

Now, to deliver the new services at GAVS, we must build a pipeline of skilled technologists with knowledge of the healthcare domain. Enter GAVS Healthcare Technology Institute. The Technology Institute is designed to offer three levels healthcare courses introducing participants to the healthcare industry, AI/ML methodology, and application of these methodologies to their projects. The goal is for every employee at GAVS to have an introductory knowledge of healthcare and AI/ML concepts to align with GAVS’s healthcare focus. Our partnership with IIT Madras bring to the institute the best in class faculty and curriculum. The first level of certifications went live on March 22. We aim to create a proficient, domain-ready workforce and hope they enjoy the healthcare journey!

2020 was undoubtedly a challenging year for the healthcare industry, and we proudly supported our clients through these challenges. As we look forward, our goal is to further enable healthcare organizations in managing their strategic priorities and save lives!

About the Author –

Kushboo brings with her 9+ years of experience in Management Consulting and IT Consulting in Healthcare and Financial Services. Within healthcare, she has worked at University of Chicago Hospitals, Johns Hopkins, Advisory Board Company and Apollo Hospitals. She is especially experienced in managing and supporting large transformation programs. She has worked on several process optimization and cost optimization projects contributing to FTE and dollar savings for her clients.

Introducing Jane Aboyoun, CIO, SCO Family of Services

Jane Aboyoun

1. Tell us something about your childhood. What values had been instilled in you that helped you excel later in your life?

My parents are first generation Americans – both their parents were born elsewhere and emigrated to the US.  My mother is in her 80s now and she was amongst the first of her generation to go to college and work full time while raising a family. My mom loved her job as an elementary school teacher, and she taught me that helping others is the key to finding joy in a career.  My father grew up poor and in a foster home, but he managed to earn a full academic scholarship to college.  He went on to have a successful career in sales management.  In my family, education was highly prized and through my parents’ example, I learned that life-long learning and hard work was a way to advance oneself.  I was raised to be independent, to rely on myself, to value being able to support myself, to work hard, to focus, to be proactive and to always say “yes, we can”.  

2. When did you discover your passion for technology?

I found my passion for technology in my first job as a production planner at Nestle Foods.  Production planning is all about efficiency and process.  Computers are essential in delivering that and it was impossible to do my job without them. While studying engineering in college, I had to take a programming class.  Of all the courses that I took, that one was the most challenging for me and I barely passed the course. I vowed to never go near a computer and that stood until I was faced with tracking millions of dollars in WIP (work-in-process) inventory in one of Nestle’s production facilities. To do that, I developed a barcode data collection system to track inventory as it moved around the production floor.  The system was so successful that Corporate noticed and asked me to join their Information Systems team.  I started writing code for an MRP system and all at once, with practical application in real life, it suddenly made sense. My career in IT was launched.

3. What have been some of the biggest challenges in your life and how that has shaped you?

I always wanted to have both a career and have a family, and that is a tough balancing act.  Early in my career, I found working in a global C-suite job and raising young children at the same time to be extremely challenging. I’ve had to create boundaries in order to give 100% to each.  It is much easier now that my children are older, and as a result of my experience, I have huge respect for working parents who are trying to make it all happen. It’s not easy!

4. Tell us something about the social causes that you support.

I am passionate about fighting for equality and equity, protecting the environment, and animal welfare. We have so many challenges at this time in human history and how we navigate the next ten years together will be critical in so many ways.  

5. How would you define success?

What I have learned through my life experience is that success is a journey, rather than an end game, as the world is of course a dynamic place and the bar is always being raised.  Good ideas can come from anywhere and it’s critical to stay flexible and open-minded.  As a leader success is measured by the impact one has on others, and it’s often found in tiny actions that multiply across a team to impact an organization – the butterfly effect at work.  

6. How would you describe your leadership style?

I would liken it to a conductor of an orchestra…having a vision for what could be, selecting the players, empowering the musicians, directing the various piece parts so that together, we achieve an incredible outcome.  The fun part though, is that we are playing jazz music rather than the classics and sometimes the players just need to jam, and trust that we’ve got this, as there isn’t always a playbook for what’s next.

7. Looking back on your journey and knowing what you know now, what is one piece of advice you would have given yourself along the way?

To not lose sight of the big picture. In the rough and tumble of the day-to-day work, it is easy to get distracted and buried in the details.  It takes discipline to stay focused on the end game and keep your eyes above the tree-line.  This is where I feel I provide most value to the team, as in my role, I have the opportunity to see many pieces of the puzzle and how they fit together. I can help bring that insight back to the team, so that we can recalibrate if necessary and link those day-to-day tasks to achieving business objectives.

8. What advice would you give those who want to pursue a career in STEM?

There has never been a better time to pursue a career in STEM.  As I mentioned, the next few decades are critical to the future of the planet.  There are numerous exciting new fields emerging that can help address the issues in the world today.  These solutions are no doubt rooted in science, technology, engineering, and math.  I would encourage those interested to move forward earnestly. I believe the reward would be a satisfying and worthy career that might just change the world!

About Jane Aboyoun –

Jane Aboyoun is the Chief Information Officer at SCO Family of Services, a non-profit agency that helps New Yorkers build a strong foundation for the future. In this role, Jane is responsible for leading SCO’s technology strategy, and managing the agency’s technology services to support business applications, architecture, data, engineering, and computing infrastructure.

As an accomplished CIO / CTO, Jane has spent 20 years in the C-suite in a variety of senior technology leadership roles for global, world-class brands such as Nestlé Foods, KPMG, Estēe Lauder Companies, Walt Disney Company, and the New York Public Library.

Health Information Exchanges in Post-Pandemic Healthcare

Srinivasan Sundararajan

Electronic Health Information Exchange (HIE) allows doctors, nurses, pharmacists, other health care providers and patients to appropriately access and securely share a patient’s vital medical information electronically – improving the speed, quality, safety, and cost of patient care.

HIE enables electronical movement of clinical information among different healthcare information systems. The goal is to facilitate access to and retrieval of clinical data to provide safer and more timely, efficient, effective, and equitable patient-centered care.

While the importance of HIE is clearly visible, now the important question is how hospitals can collaborate to form an HIE and how the HIE will consolidate data from disparate patient information sources. This brings us to the important discussion of HIE data models.

HIE Data Models 

There are multiple ways in which an HIE can get its data, each influencing the way in which the interoperability goals are achieved, how easily an HIE platform is built and how to sustain in the long run especially if the number of hospitals in the ecosystem increases. The two models are

  • Centralized
  • De-centralized

Centralized HIE Data Model

ehr modernization services with healthcare data

This is a pictorial representation of centralized HIE data model.

As evident, in the centralized model, all the stakeholders send their data to a centralized location and typically a ETL (Extraction, Transformation, and Loading) process ensures that all the data is synced with the centralized server.

Advantages

  • From the query performance perspective, this model is one of the most efficient, because the DBAs have complete control of the data and with the techniques like partitioning, indexing they could ensure that the query can be done in the best possible manner. Since the hardware is fully owned by a single organization (which is the HIE itself), this can be scaled out or scaled up to meet the demands of the business.
  • This model is fairly self-sufficient once the mechanism for the data transfers are established, as the need to connect to individual hospitals are no longer there.
  • Smaller hospitals in the ecosystem need not take the burden of maintaining their data and interoperability needs and can just send their data to the centralized repository.
  • Better scope for performing population predictive and prescriptive analytics as the data resides in one place and easier to create models based on the historical data.

Limitations

  • This model needs to have highest level of security built in, because any breach in the system will compromise the data of entire ecosystem. Also considering that individual hospitals send their data to this model, all the responsibility lies with a single agency (HIE) which is highly prone to lawsuits related to data privacy and confidentiality.
  • There is no control for patients in managing their own records and right to provide consent for data access, even though this information can be collected there is no easy way to implement them.
  • The system is prone to a single point of failure and hence require efforts for high availability of the platform.
  • This model will face scalability challenges as the network grows beyond a point, unless the platform is modernized with latest big data databases, the system will have scalability issues.
  • Lot of coordination required to monitor the individual ETL jobs for their success, failure, and record synchronization details, so this model will have a huge allocation of IT resources and will increase the total cost of ownership.
  • The model of expense sharing between the HIE, data producers, data consumers will be difficult and needs to have a strong governance model.
  • Difficult to match the patient information across hospitals, unless the both the hospitals use deterministic matching attributes like SSN, otherwise it would be difficult to match between patients who have misspelt names, different addresses, etc.
  • This model may suffer data integrity issues when the participant hospitals merge with each other, such that the IT systems of the two hospitals need to take care the internal details of the ETL jobs.

De-centralized HIE Data Model

ehr modernization with health data management platforms

The above is the pictorial representation of decentralized HIE data model.

As evident, in this model individual hospitals, continue to own all their data, however, the centralized database keeps a pointer – MPI (Master Patient Index), which serves as a unifying factor for consolidating data for that patient. While some books also suggest a variant called Hybrid model which combines centralized and decentralized data models, we believe that pure play decentralized model itself is a hybrid (i.e. centralized + decentralized) because there needs to be a centralized repository to keep the master patient index along with all the access rights and related information.

Advantages

  • It is much easier to implement as no huge investment is required from a centralized provider perspective. The HIEs in this model can start low and grow on demand basis
  • Less expensive as no single organization owns all the data, but only a pointer to the data, and the respective hospitals continue to own the data.
  • Much easier to provide patients the control of their own data and patient’s consent can be a key in accessing information from the respective hospitals.
  • No need to worry about broken ETL jobs and the latency between source and destination. All the data is always current.
  • No need to worry about single point of failure, as the individual sub systems will continue to exist even if one link to a particular hospital is broken. Maintaining the high availability of this light-weight platform is much easier than a monolithic large database as part of a centralized data model.
  • A data breach into the centralized repository still will not compromise all the data, as the individual hospitals are likely to have some more additional controls which prevent a free flow for hackers. This also prevents one organization from facing all the legal issues resulting from patient data breach.

Limitations

  • This model will have a query performance problem when it comes to aggregating a patient information across multiple hospitals because each has to be obtained with a separate API call and a facade has to group multiple datasets.
  • Difficult to establish common standards in terms of data formats and APIs across multiple hospitals, this may result in each hospital having their own methods.
  • Bringing all the stakeholders including the patients to agree on a MPI (Master Patient Index) will have governance challenges and needs to be implemented carefully.
  • Providing analytics for a large set of population will have challenges due to the difficulties in consolidating the data.

GAVS Point of View & Role of Blockchain

While no model can be 100% perfect for building an HIE, GAVS’ analysis point to that fact decentralized model of building and operating HIE is better than centralized model.  The COVID pandemic has changed the world and the boundaries of healthcare no longer exist within a smaller geography or neighbourhood as it used to. More the participants and bigger the network size, the better it is for population health improvement initiatives. Also, in high population countries where there are initiatives like national healthcare for all, these larger initiatives cannot be done using a pure play centralized model.

From an implementation perspective, the Healthcare IT world has been curiously watching the role of Blockchain in data interoperability and in the implementation of decentralized HIE. Blockchain which is a distributed database has decentralization built-in as part of its core architecture. It would be easier to implement decentralized HIE using blockchain.

GAVS Reference Implementation Rhodium to cater to Healthcare Data Management and Interoperability has positioned Blockchain as a core mechanism for patient data sharing, we will share more of our thoughts and details of reference implementation in the coming articles in this series.

About the Author –

Srini is the Technology Advisor for GAVS. He is currently focused on Healthcare Data Management Solutions for the post-pandemic Healthcare era, using the combination of Multi Modal databases, Blockchain and Data Mining. The solutions aim at Patient data sharing within Hospitals as well as across Hospitals (Healthcare Interoprability), while bringing more trust and transparency into the healthcare process using patient consent management, credentialing and zero knowledge proofs.