IT data lakes are dependable repositories created to store large volumes of IT data, both organised and unstructured, at scale. These data lakes serve as central hubs for the storage of a wide range of data on IT operations, infrastructure, and performance in the healthcare industry. IT data lakes excel at managing massive amounts of data, unlike traditional databases, which makes them the perfect fit for the data-intensive environment of healthcare.

In the dynamically evolving realm of healthcare, two pivotal innovations are reshaping the industry: Medical Interoperability and machine learning (ML). Medical Interoperability revolves around the seamless sharing of healthcare information across diverse systems and platforms. To harness the immense potential of these advancements, Neurealm unveiled one of the industry’s Best AIOps tools based on big data for healthcare services, known as Zero Incident Framework (ZIF^TM). This ground-breaking service empowers healthcare providers, payers, and life sciences companies to securely store, transact, analyse, and exchange health data at an unprecedented scale.

At the heart of ZIF^TM Data Lake’s capabilities lies its robust support for Fast Healthcare Interoperability Resources (FHIR). FHIR, a universally standardized format for healthcare data exchange, has gained widespread acceptance within the industry. It streamlines the exchange of structured medical data, making it readily accessible to clinical researchers, informaticians, and machine learning tools. FHIR introduces a designated resource for capturing documents, such as physician’s notes or summaries of lab reports. However, to fully unleash the potential of this data, it must undergo extraction and transformation into a more user-friendly format.

As FHIR-formatted medical data finds its way into ZIF^TM Data Lake, a transformational process ensues. ZIF^TM, recognized as one of the industry’s Best AIOps tools employs advanced natural language processing (NLP) techniques, meticulously trained to comprehend medical terminology. These techniques enrich unstructured data with standardized labels, a crucial step that involves identifying medications, conditions, diagnoses, procedures, and more. Through standardization and tagging, ZIF^TM  ensures that all data becomes normalized and effortlessly searchable.

Medical Imaging Advancements: With the analysis of large patient datasets, Generative AI enhances its potential to improve medical diagnosis by discovering patterns linked with certain diseases. This ground-breaking medical technology has the potential to help doctors, nurses, and other healthcare personnel detect illnesses more accurately and effectively. The relationship between Google Cloud and healthcare institutions exemplifies how AI technologies are being leveraged to tackle administrative and operational issues. These technologies, which are meant to make tasks like information retrieval and documentation easier to accomplish, will increase the amount of time available to researchers and clinicians (Gupta & Corrado, 2023).

Drug Discovery: The promising route that Generative AI offers allows for the identification of novel pharmaceutical compounds with the potential to treat a wide range of illnesses. According to McKinsey & Company (2023), one of the strengths of Generative AI is the analysis of various and unstructured data sources typical in the healthcare business. This innovative technology has the potential to transform these data sources into meaningful resources, allowing Generative AI to be more creative and effective in its quest for novel medications.

Patient Data: Generative AI is a potent tool for producing new patient data sets, which is critical for developing treatment regimens and improving patient well-being. According to BCG (Huddle et al., 2023), Generative AI systems have the ability to methodically analyze vast libraries of medical data and develop entirely novel stuff. The level of therapy may be enhanced, accessibility and cost may be improved, imbalances in research and healthcare delivery may be reduced, and companies may be able to generate hitherto untapped value as a result of this breakthrough technology.

Transformative Force: Healthcare may experience a fundamental transformation as a result of the promise of Generative AI, which transcends fads and trends to construct a continually evolving toolset. According to predictions, the global market for Generative AI would be valued $118.06 billion by 2032, highlighting the technology’s enormous potential to revolutionize a wide range of operational elements and healthcare operations (Precedence Research, 2023).

The Ethical Use: However, in order to guarantee that this technology is utilized ethically and responsibly, it is critical that it be used with prudence. The ability of Generative AI systems to distinguish tiny alterations in longitudinal medical images, such as X-rays, CT scans, and MRIs, is proven. In the long term, examining these minute differences can aid in the development of improved diagnoses and treatment regimens.

Technology has emerged as a crucial ally in the fight to save lives and enhance patient care in the field of healthcare. We have entered an era where Health Information Technology (HIT), Electronic Health Records (EHR), and data observability are not only key advancements but also basic requirements thanks to the combination of current technology and persistent medical research. However, increasing dependence on technology comes with a built-in problem: assuring the steadfast dependability of critical healthcare systems.

At its core, HA is a straightforward concept that underpins Service Reliability. It involves two or more similar computers or servers, with one serving as the primary production processor while the others act as backups, consistently updated in near real-time. System software such as ZIF^TM manages the replication and monitoring process on both servers. When the primary server experiences a malfunction or requires maintenance, users seamlessly transition to the backup server, all orchestrated by ZIF’s intelligent automation. Any connections with other networked systems are swiftly re-established on the backup server, thanks to ZIF’s real-time monitoring capabilities. Once the primary server is back in action, users and networked systems are smoothly redirected through a process known as “failback” all with the support of ZIF^TM. This architectural resilience, enhanced by ZIF’s proactive measures, grants healthcare practitioners the confidence to rely on automated systems without hesitation.

In terms of criticality, not all healthcare systems are created equal. Usually, only those that have a direct bearing on patient care come under this category; a classic example is the hospital’s code paging system. However, the following healthcare systems should be on the “cannot go down” list:

Patient record systems are the backbone of modern healthcare, and any downtime can have severe consequences for patient care. ZIF^TM ensures the resilience of these systems through its proactive approach. By continuously monitoring system performance, ZIF^TM identifies potential issues long before they can disrupt operations. It employs predictive analytics models to foresee bottlenecks and address them before they escalate into problems. This ensures that patient records are always accessible, enabling healthcare professionals to provide timely and accurate care.

With nursing staff stretched thin, the efficient use of clinical automation tools is critical. ZIF^TM plays a vital role in this scenario by maintaining the uninterrupted operation of these tools. It monitors the performance of devices used by nurses, such as PDAs and clinical workstations, and ensures they are always available. This minimizes disruptions, allowing nurses to focus on patient care without the worry of technology failures.

Automated care management protocols are essential for delivering evidence-based treatment. ZIF^TM understands the importance of these protocols and ensures their continuous operation. By monitoring the systems that support these protocols in real-time, ZIF^TM identifies and resolves issues before they impact patient care. This guarantees that healthcare professionals can rely on these protocols to provide the best possible treatment.

In the high-stakes environments of Intensive Care Units (ICUs) and anaesthesia administration, where every second counts, ZIF^TM serves as a vigilant guardian for essential IT systems. These systems include patient monitoring equipment, data recording, and communication tools in ICUs, while anaesthesia relies on various IT components for drug delivery and patient safety. ZIF^TM proactively monitors these IT systems, swiftly addressing potential issues to ensure uninterrupted operation and service reliability. This critical support enhances patient safety and bolsters the efficiency of healthcare delivery in these life-sensitive settings.

Point-of-care devices connected to EHR systems are vulnerable to interruptions in system operations. ZIF^TM acknowledges this vulnerability and takes proactive measures to prevent disruptions. It monitors both the EHR systems and the connected devices, ensuring they operate seamlessly. In case of any issues, ZIF^TM initiates swift resolutions, minimizing the risk of errors caused by manual interventions.

Healthcare organisations are often concerned about medication errors. ZIF^TM makes sure that systems that employ barcoding for medicine administration run without interruption, which promotes patient safety. It keeps an eye on these systems and responds quickly if they go down, lightening the load on nursing personnel and removing the chance for mistakes.

Automated case documentation improves efficiency and reduces errors in healthcare. ZIF^TM recognizes the critical role of these systems and guarantees their continuous availability. By closely monitoring these systems, ZIF^TM prevents disruptions and ensures that clinicians can document events and issues without delays or errors.

Physicians rely heavily on automated order entry systems for efficiency and safety. ZIF^TM  understands the importance of these systems and ensures their uninterrupted operation. By employing predictive analytics and real-time monitoring, ZIF^TM  identifies potential issues that could affect order entry and take proactive steps to prevent them. This ensures that physicians can continue to use these systems with confidence, reducing the risk of errors.

Every pulse is a priceless moment in the world of healthcare, and ZIF^TM emerges as the steadfast defender of uninterrupted operations. By assuring the continuous operation of crucial IT systems, this revolutionary solution goes beyond simple optimisation and acts as a beacon of confidence, guaranteeing the safety and wellbeing of patients.

Every Second Counts Here

In the realm of critical healthcare IT services, time is a relentless adversary. Here, ZIF^TM, armed with predictive analytics models and real-time monitoring, ushers in a new era of incident resolution. It brings the power of proactivity to the forefront, where potential issues are identified and addressed before they can morph into full-blown disruptions. ZIF’s presence ensures the unwavering resilience of healthcare systems, allowing IT professionals to streamline incident resolution, minimize downtime, and, most importantly, elevate patient care.

Protecting the Foundation of Healthcare

Electronic Medical Records (EMRs) are the lifeblood of modern healthcare, and their security is sacrosanct. Here, ZIF^TM steps in as the vigilant sentinel. It bestows healthcare organizations with a panoramic view of their IT infrastructure, guaranteeing the security and seamless operation of pivotal systems like EMRs. With ZIF^TM, healthcare professionals gain the ability to swiftly identify and rectify service outages, root out lurking security threats, and zealously safeguard patient data. It’s not merely EMR management; it’s the fortification of EMR security, an impervious bastion upheld by ZIF^TM.

Budgetary Tide-Sharing with Acuity

In an era of tightening budgets and fiscal prudence, healthcare organizations relentlessly seek innovative avenues to curtail costs. In this fiscal landscape, ZIF^TM  extends its capabilities to the realm of software asset management. Here, it serves as the vanguard, enabling healthcare IT teams to consolidate and streamline their software assets. This endeavour doesn’t merely ensure compliance and risk mitigation; it unveils substantial cost savings hidden within the labyrinth of IT expenditure. With ZIF^TM, healthcare organizations embark on a journey of financial optimization, where every resource is meticulously allocated, all without compromising the paramount objective of patient care. It’s more than mere cost reduction; it’s a symphony of cost optimization, harmonized by the orchestration of ZIF^TM.

The ongoing functioning of important IT systems is not a luxury, but rather a must in the life-or-death field of healthcare. ZIF^TM assumes the role of the unshakable watchdog, assuring the continuous operation of vital healthcare systems. ZIF^TM  is the foundation of healthcare resilience with its predictive analytics, proactive issue resolution, real-time monitoring, intelligent automation, and adaptive learning. Additionally, to operational optimisation, it guarantees uninterrupted and secure access to every patient information, every medicine, and every pulse. ZIF^TM AIOps, with its observability capabilities, is the ultimate assurance of patient well-being in the realm of healthcare.

Effective provider communication is crucial in the ever-changing healthcare environment for providing greater patient care and increased service reliability. One cannot overstate the importance of seamless communication, and our flagship solution, ZIF^TM, uses Natural Language Processing (NLP) to take healthcare communication to new heights. In this article, we’ll look at how ZIF’s NLP capabilities were created with the goal of fostering information exchange and collaboration among healthcare practitioners.

In order to give patients with the finest treatment possible, healthcare providers must work together smoothly. The healthcare industry is also rife with communication issues that may prevent this collaboration. These challenges consist of:

• Information Amplification: Healthcare providers are inundated with vast amounts of patient data, research articles, and clinical guidelines. Keeping up with this information can be overwhelming and time-consuming.
• Data Silos: Electronic Health Records (EHRs) are often stored in disparate systems that do not easily communicate with one another. This fragmentation can lead to critical information being missed or delayed in reaching the relevant healthcare provider.
• Cognitive Strain: Healthcare providers must process complex medical information quickly and accurately. Cognitive overload from sifting through data can lead to fatigue and errors in decision-making.
• Standardization: Maintaining standardized terminologies and documentation practices across different healthcare institutions is a challenge, making data exchange and analysis difficult.

The cornerstone of providing high-quality healthcare is effective communication. It is essential for assuring patient safety, precise diagnoses, and effective treatment results. The importance of effective communication among healthcare practitioners cannot be stressed in the dynamic and complicated world of healthcare, where information is plentiful and always changing. The improvement of interprofessional communication lies at the core of ZIF’s NLP-driven capabilities. The natural language interface of our AIOps platform allows for smooth communication between IT teams and clinical professionals. Here are some ways that ZIF^TM is transforming healthcare communication:

• Efficient and systematic Incident Reporting: One of the key features of ZIF’s NLP-powered communication is its ability to streamline incident reporting. Healthcare providers can effortlessly report incidents, whether they involve system outages, performance glitches, or any other IT-related issue. With ZIF^TM, reporting becomes as simple as expressing the problem in natural language. This streamlines the incident resolution process, reducing response times and minimizing disruptions to patient care. This ease of reporting ensures that incidents are promptly communicated and acted upon, minimizing disruptions to patient care.
• Collaborative Problem-Solving: ZIF’s NLP capabilities foster collaborative problem-solving between IT teams and clinical staff. In the complex healthcare environment, issues can have far-reaching consequences. By providing a common platform where both technical and non-technical personnel can communicate and work together, ZIF^TM  bridges the gap and ensures that incidents are resolved efficiently. This collaborative approach creates a culture of teamwork and ensures that the right expertise is engaged when needed. This fosters a culture of teamwork and efficiency.
• Useful and Actionable Insights: ZIF’s NLP-driven insights are presented in a user-friendly manner, making it easy for healthcare providers to understand complex IT issues. This ensures that decisions are based on data-driven insights, leading to better outcomes for patients.
  Complex IT jargon can be a barrier to effective communication, especially in healthcare where time is of the essence. ZIF^TM  simplifies the presentation of insights derived from its NLP-driven analysis. Healthcare providers receive actionable information in advance, and they can take action before facing issues and complexities. The decisions are based on clear, data-driven insights and follow AIOps best practices to deliver great data outcome, thus contributing to better patient outcomes.
• Alerts and Notifications in Real-time: Healthcare providers can receive real-time alerts and notifications, allowing them to take immediate action when necessary. This ensures that critical incidents are addressed promptly. In healthcare, immediate action can be a matter of life and death. This feature is vital in healthcare settings where every second counts. NLP-powered decision support systems can provide healthcare providers with evidence-based recommendations and alerts, reducing cognitive load and enhancing decision-making accuracy.
• Chatbots and Virtual Assistants: NLP-driven chatbots and virtual assistants can answer common queries, solve issues, and provide resolution in real-time, freeing up healthcare IT team for more complex tasks. ZIF^TM has 250+ bots which can resolve incidents by triggering the bots. Also, there is a self-service portal where end-users can fix the technical issues by themselves.

I was born in Bangladesh and grew up with a sense of ambition, hunger for knowledge, along with a deep appreciation for my cultural heritage and a strong sense of community. However, I recognized that to fulfill my aspirations and make a difference in the world, I needed to venture beyond the borders of my homeland.

At the age of 16, I relocated to the US, becoming the first member of my family to do so. However, this venture was not undertaken just to fulfil my dreams of education and success but was also fueled by a burning desire to contribute to society.

After arriving in the United States, I had to face some inevitable challenges that come with immigration – adjusting to a new culture, language, and way of life. Language was a significant obstacle. I had to master English, which was essential for communication and building a successful life here. I tackled this challenge head-on, enrolling in English language classes and practicing tirelessly to improve my proficiency. Financial constraints were yet another hurdle. I took on various jobs, often working long hours to support myself and pursue my educational goals simultaneously.

My experiences as an immigrant instilled in me a deep appreciation for the opportunities the United States offered and a strong desire to give back to the community that had welcomed me. It taught me the value of adaptability, the importance of community support, and the significance of embracing diversity.

Success to me is making a difference in the lives of people, whether is providing easy access to education or healthcare and close the equity gap. After I came to the US, I became involved as an activist addressing the following issues: affordable health care, hate crimes, immigrant rights, domestic violence, environmental issues, improving elderly care, improving the public educational system, child day care and after school programs, affordable housing and transportation efficiency. These are a few causes that are near to my heart, and I continue to participate in several collaborative organizations to give back to the community.

My approach has been to lead the team from the front and to believe in them. Leading from the front means being visible and accessible to your team and being willing to roll up your sleeves and work alongside them. Also, believing in them gives them the autonomy and resources they need to succeed, and being supportive when they make mistakes. When you believe in your team, they are more likely to believe in themselves and in their ability to achieve their goals.

Staying focused on the mission is a critical way to maintain engagement and morale during challenging times. When people know what they are working towards and why it is important, they are more likely to stay motivated and engaged, even when things are tough.

Do not look for the perfection, rather focus on the continuous improvements.

There are 3 main stakeholders to the RCM process – Patient, Healthcare Provider and Healthcare Payer.

• Patients register themselves with the hospital, get treated according to the insurance plan they are enrolled in and get reimbursed for their medical cost.
• Providers provide medical services to the patient, submit medical bills to the insurers and get reimbursed for their services.
• Payers are the people who pay for the healthcare services. They take in the medical claims, evaluate it, and reimburse the hospitals depending on the policy rules.

Payers are of 2 types – private and public or the government-run.

In the US, there is a huge government-run centre called the CMS, The Centres for Medicare and Medicaid Services which provides health coverage to more than 100 million people through various schemes such as Medicare, Medicaid, Children’s Health Insurance Program, etc. which caters to the elderly, low-income groups, children, retired military people, and so on. Key private insurance companies of US would be Cigna, Humana, Blue Cross Blue Shield, among others.

Similarly, in India, we have government-run healthcare schemes such as Ayushman Bharat Yojana, Pradhan Mantri Suraksha Bima Yojana, Aam Aadmi Bima Yojana (AABY) and so on which cater to diverse group of people. Some key players in private health insurance sector would be Star Health and Allied Insurance, Bajaj Allianz General Insurance, ICICI Lombard Health Insurance, among others.

Revenue Cycle Management Market in terms of revenue was estimated to be worth $49.6 billion in 2023 and is poised to reach $84.1 billion by 2028, growing at a CAGR of 11.1% from 2023 to 2028 according to a new report. In 2022, North America held the largest market share for revenue cycle management.

Insurance, ICICI Lombard Health Insurance, among others.

The RCM process comprises of 3 major categories of activities – Front office, Claims office, and Back office.

The front office plays a role when the patient calls the hospital for an appointment or pre-registration. The front office performs activities such as taking in the details of patient, details of ailment, medical history, checking their insurance details and their eligibility of medical coverage.

The front office also analyzes the various components and benefits covered under an insurance plan such as pre-ailment coverage, room rent entitlements, ambulance cost coverage, co-pay, deductibles, co-insurance, etc.

Then the patient meets with the doctor, disease is diagnosed, and treatment plan is decided. Before starting the treatment, the hospital must do a pre-authorization check. Pre-authorization is a key step in RCM where the hospital checks with the insurance company if the proposed treatment is covered under the medical plan the patient is enrolled in.

After getting the necessary approvals from the insurance company, the patient’s admission, treatment, medical intervention, procedure happens one after the other and the patient is discharged.

In modern day healthcare where patients require holistic care and attention beyond their specific medical conditions, Value Based Care (VBC) shifts the emphasis from the traditional fee-for-service model to rewarding healthcare providers for achieving positive patient outcomes. Data management is vital for Value Based Care, enabling healthcare organizations to collect, analyze, and leverage data to improve patient care and reduce costs. Neurealm conducted a webinar that explores the challenges, provides insights and establishes a roadmap to achieving excellence in care delivery. This blog captures vital discussion points and takeaways from the webinar Value Based Care and Data Management.

The webinar panelists were healthcare industry leaders Mr. Suman Mishra – Healthcare CTO at Neurealm and Dr. Nick Patel – Founder and CEO of Stealth Consulting.

VBC is a healthcare delivery model focusing on improving patient outcomes while controlling costs and holding providers accountable. Some key enablers are strong government involvement in change, focus on information technology improvement, instituting a VBC culture among providers, and adopting a time- driven activity-based costing model. VBC is necessary as it helps improve patient outcomes, contains costs, promotes population health, aligns incentives, enables continuous quality improvement, and drives payment reform.

Data is a key component of VBC. An organization needs enough patient data to offer personalized care. The term “data journey” refers to the various stages through which data moves from collection to usage by business. It starts with identifying siloed and separate data sources. Once that is done, the next step is to consider a centralized data lakehouse. Now that all the data is in a single place, the third step is to share it with internal and external sources. Finally, it is time for data governance, where the lineage and quality of data, security, and privacy are evaluated before data is made available for predictive or prescriptive analytical (AI/ML) services.

Here are the main data points that need to be collected for VBC:

Understanding and leveraging Hierarchical Condition Category (HCC) and Risk Adjustment Factors (RAF) scores are vital for reimbursement and load balancing schedules and optimizing patient care. Healthcare providers can allocate appropriate time and resources by categorizing patients based on risk. For instance, two patients with diabetes may have different risk profiles based on complications and comorbidities. Proper coding affects the RAF score, which, in turn, influences reimbursement.

Healthcare Effectiveness Data and Information Set (HEDIS) measures are also crucial components of value-based care, with approximately 90 measures across six domains: effectiveness of care, access and availability, care experience, utilization, relative resource use, health plan components, and reporting. Comprehending these measures and their subgroups helps healthcare organizations monitor and improve care quality.

Social Determinants of Health (SDOH) are significant for patient care and outcomes. Transportation access, broadband availability, and socioeconomic challenges impact patient engagement and health outcomes. Understanding the patient’s social determinants enables healthcare providers to tailor care plans, support unique needs, and enhance overall well-being.

When building a data management framework, it is critical to integrate and centralize various data sources, such as claims, Electronic Health Records (EHRs), and IoT (Internet of Things) devices. Automation and AI tools can leverage this consolidated data to drive personalized care and engage patients effectively. A centralized system, often called a command center, is a hub for monitoring and coordinating patient movement across different care settings (for example, hospital, post-acute, ambulatory).