Is Hadoop Holding Your Business Back? Have you tried Databricks?

Hadoop, initially developed to address the challenges of big data processing, has become a cornerstone technology for many organizations. However, as businesses strive for agility and real-time insights, Hadoop’s limitations have increasingly hindered progress. Here’s how Hadoop prevents businesses from moving forward.

Challenges with Hadoop

Complexities in Management 

One of the most significant barriers is the complexity involved in managing a Hadoop cluster. Setting up and maintaining Hadoop requires specialized skills and knowledge of its various components, such as HDFS, MapReduce, and YARN. This complexity leads to resource inefficiencies and increased operational costs, making it difficult for organizations to adapt quickly to changing data needs.

Performance Limitations 

Hadoop is primarily designed for batch processing, which means it processes large volumes of data but does so with high latency. This delay is detrimental to businesses that require real-time analytics and insights to remain competitive. The MapReduce framework, while powerful for certain tasks, is not optimized for speed, leading to slower processing times compared to modern alternatives.

Scalability Issues 

Although Hadoop is designed to scale horizontally by adding more nodes, this does not always translate into linear performance improvements. The management overhead and potential network congestion diminish the expected benefits of scaling, creating bottlenecks that stifle growth.

Security Concerns 

Hadoop’s default security features are often inadequate for protecting sensitive data. The lack of built-in encryption at both storage and network levels poses significant risks, especially in industries where data privacy is paramount. Implementing robust security measures typically requires additional tools and expertise, complicating the overall architecture.

Not a Comprehensive Solution 

Perhaps most critically, Hadoop is not a comprehensive solution for modern data needs. Organizations often find themselves seeking ways to integrate multiple tools and frameworks to build an end-to-end data solution. This piecemeal approach leads to inefficiencies and increased costs as teams struggle to stitch together disparate systems.

What next?

It’s time for organizations to move away from Hadoop and adopt a more comprehensive solution. Databricks has emerged as an answer to the complexities, offering a complete data platform that addresses Hadoop’s challenges head-on. It provides a unified environment for data engineering, collaborative analytics, and machine learning—all in one place. Databricks supports real-time processing capabilities that allow businesses to gain immediate insights from their data streams, significantly reducing latency issues associated with Hadoop.

Moreover, Databricks simplifies management with its user-friendly interface and robust security features built-in from the start. Organizations can leverage Databricks’ capabilities to build AI and machine learning solutions seamlessly, appealing particularly to companies looking to adopt advanced analytics without the complexities associated with Hadoop.

In summary, while Hadoop laid the groundwork for big data processing, its limitations increasingly prevent businesses from advancing. Databricks emerges as a superior alternative by providing a comprehensive platform that simplifies workflows and enhances capabilities in real-time analytics and AI/ML applications.

Here’s a table demonstrating how Databricks overcomes Hadoop’s limitations:

Altysys leverages Databricks to empower businesses with data and intelligence, enhancing operational efficiency across diverse workloads. Contact us to modernize your big data processing systems with improved real-time analytics using Databricks.


Sunil Singh
Author:
Sunil Singh
Sr. Solution Architect

Enhancing clinical decision-making quality with Generative AI

With the ubiquity of patient data and clinical knowledge, GenAI can play a significant role in enhancing the quality of decision-making at hospitals.

Clinical decision-making is a complex process where efficacy is determined, not only by biomedical knowledge, but also by experience, patient-centric processes, and completeness of data. It is an iterative process where early interventions can make the difference between life and death for patients.

The good news is that diagnostic data and patient history are now ubiquitous and available to doctors. Where digitization is already underway, this data may already be available through Electronic Medical Records (EMRs). By leveraging Generative AI (GenAI), doctors can now exploit this data to improve the accuracy and timeliness of clinical decisions, thus equalizing the quality of care across the healthcare ecosystem.

Clinical decision making: key challenges

Some of the most pressing issues in clinical decision-making include diagnostic errors or delays in identifying fast-progressing conditions. These issues become more challenging to address when doctors treat patients with multiple ailments, where it becomes difficult to attribute symptoms to a particular condition.

Moreover, doctors may find it difficult to correctly diagnose patients with a long and complex medical history. In such scenarios, overlooking important information may result in suboptimal decisions or erroneous prescriptions – which lead to poor patient outcomes.

How GenAI can improve clinical decision outcomes

In the past, AI has already been applied in Clinical Decision Support (CDS). However, the applicability of such systems is limited, and their black-box output emerges as an impediment in the decision-making process.

More recently, GenAI has shown significant promise in CDS. It can help clinicians analyze structured and unstructured data, summarize information, and identify similar cases to facilitate early interventions. Two-thirds of clinicians already see the technology as beneficial, and 40% are ready to use it this year.[1] Consider the following ways in which GenAI can enhance the quality of clinical decisions at scale.

Devising optimal treatment plans

By training Generative AI on large datasets comprising medical history, genetics, and lifestyle, clinicians can exploit the technology to infer how a patient will respond to the prescribed treatment. By accounting for minute details in troves of data, GenAI can spot correlations that evade doctors’ judgment.

This can help clinicians create personalized treatment plans that work the best for a particular patient. For instance, GenAI systems could be applied to anticipate the side effects of a blood pressure medication or various classes of statins on people based on their lifestyle and genetics.

Enhancing the scope and precision of CDS systems

GenAI can augment the accuracy of medical diagnoses in two ways. First, it can sharpen the accuracy of existing tools like lesion segmentation or diagnosis networks by enriching their training data. For this, GenAI can be applied to generate synthetic medical image data, which can be used to offset issues like overfitting or bias in existing CDS systems.

Flow-based models show promise in generating high-quality images, making them apt for completing deficient samples. This can help expand the applicability of CDS systems beyond a particular race or demographic – a shortcoming that has been noted in AI-based CDS solutions.

Optimizing care journeys for patients

Secondly, GenAI can infer patterns from complex medical histories of patients with multiple conditions. By making sense of seemingly unrelated details, such systems can provide clinicians with the complete picture of a patient’s health, enabling doctors to make more accurate calls when devising their care journey.

Making CDS systems more explainable and transparent

Hospitals are already leveraging AI-based CDS systems to help doctors arrive at more optimal diagnosis and treatment decisions. While these have been improving over time, GenAI can make them more effective and transparent. For instance, ML-based CDS models can be integrated with GenAI to make their outputs explainable to doctors. This can be achieved with LLMs that are trained in medical research, which can then contextualize CDS outputs for clinicians.

Final words

As hospitals pursue digital transformation, it is important to focus on the quality of clinical decisioning, as it significantly impacts patient outcomes. GenAI, especially multimodal models will be of special importance in this regard, as vast volumes of patient data become available to doctors.

However, it will be crucial to build well-tested systems as erroneous outputs could do irreversible damage to patients. That’s why, when implementing GenAI for CDS, it is important to address issues like response variability, which is characteristic of GenAI models. With a trusted technology leader, healthcare providers will be better equipped to effectively preempt such issues, and enhance clinical decision quality at scale with GenAI.


[1] https://www.wolterskluwer.com/en/news/gen-ai-clincian-survey-press-release


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Mitigating clinician burnout in healthcare with GenAI

Amidst the rising shortage of healthcare workers, GenAI shows significant promise in reducing the burden of care delivery on burnt-out clinicians.

Clinician burnout is a rampant issue in the most sophisticated healthcare systems today. Surveys indicate that there is a shortage of 7m healthcare workers globally, which will rise to 13m by 2035.[1] This, in addition to the rising disease burden, has turned physician and clinician burnout into a pressing issue in care delivery. What’s more, burnout rates are above 50% in critical specialties like emergency medicine, obstetrician gynecology, oncology, and radiology.[2]

Why clinician burnout needs an urgent solution?

Clinician burnout causes depression among front-line care delivery workers. But more importantly, it also has a negative impact on patient outcomes. Nearly 40% doctors get easily exasperated with patients, and over 25% are less careful with patient notes when experiencing burnout. Both of these factors erode the efficacy of care delivery and lead to a lack of empathy in doctor-patient interactions.

A closer look at the causes of clinician burnout

Interestingly, a plethora of bureaucratic tasks like charting and paperwork is the top cause of burnout according to 62% physicians.[3] This is not surprising, as care delivery involves a great deal of paperwork, note-taking, and charting – most of which is carried out by doctors as they attend to patients. This leads to too many hours at work – something that is felt by over 40% doctors today.[4]

But what is surprising, is that the top cause of clinician burnout can be readily addressed by a key piece of modern technology – and that technology is generative AI (GenAI).

How to mitigate clinician burnout with GenAI

Generative AI finds numerous important applications that can accelerate clinicians’ workflows. Amongst these, here are the top use cases that can help mitigate clinician burnout.

#1. Applying Generative AI for documentation and summarization

GenAI can significantly reduce the time clinicians spend on documentation, summarizing, and reporting tasks by automating processes across various stages of patient care.

One key application is querying patient history for essential details like allergies or genetic predispositions. Instead of sifting through years of medical records, clinicians can rely on GenAI to quickly surface relevant information, thus saving time and improving decision-making.

Similarly, summarizing clinical notes using GenAI helps clinicians focus on the most critical information in patient records. By condensing subjective symptoms, objective assessments, and treatment plans, GenAI accelerates care delivery and ensures that no vital details are overlooked.

GenAI also assists in summarizing radiology reports and bloodwork, helping clinicians interpret diagnostic data faster and more accurately. It highlights key trends and offers insights into potential diagnoses, even suggesting treatment plans by referencing other patient cases.

Furthermore, automating discharge summaries and patient handover documentation allows doctors to generate standardized reports using data from EHRs and lab results. These summaries maintain consistent terminology and structure, which clinicians can quickly review and sign off, saving considerable time while maintaining accuracy.

Together, these applications streamline workflow and enhance both efficiency and accuracy in medical settings.

#2. Streamlining patient interactions and ongoing care with GenAI

Doctors spend a major fraction of their time looking at patient monitoring systems, interacting with patients, and revising their medication based on symptoms and new research.

In each of these areas, GenAI can help clinicians save a significant amount of time. GenAI integrations can flag important findings from patient monitoring and alerting systems to ensure that doctors spend time only on the most relevant details.

Similarly, GenAI can draft responses to patient queries in post-care scenarios. Clinicians can then simply verify and edit these responses to deliver a more empathetic experience to their patients without spending too much time. Likewise, patient symptoms can be analyzed by GenAI models to find suitable alternatives to drugs based on the latest research.

Next steps

GenAI is an important tool for mitigating clinician burnout. However, given the critical and sensitive nature of the above applications, it is important to build GenAI use cases in a fail-proof fashion. The most important consideration is to offload the task of prompt engineering from clinicians, by offering well-tested prompts built into the solution.

Lastly – before these applications are rolled out, it may be prudent to hold working sessions to educate doctors about the right way to leverage the technology. Working with an experienced technology partner can help hospitals effectively navigate these issues and build resilient GenAI solutions.

<Contact Us> for more details about our GenAI solutions to mitigate clinician burnout.


[1] https://www.passmed.uk/doctor-shortage/
[2] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout
[3] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout
[4] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Reducing the administrative burden of care delivery with Generative AI

With corporate functions accounting for a major chunk of the healthcare spend, hospitals must identify innovative means to mitigate wastage.

Over the last few years, healthcare costs have been on the rise in developed and developing nations alike. In India, medical inflation was recorded at 14% in 2023, and in the US, costs have been rising consistently by over 5-6% annually.[1], [2]

Rising cost of healthcare typically trickles down to the patients in the form of higher health insurance costs and increased out-of-pocket expenditure. However, not all of this increase in the cost of care arises from medical expenses.

Administrative spend accounts for nearly 25% of overall healthcare spend in the US.[3] This number increased further in 2022, when administrative spend grew by 50%. This is a key impediment in delivering value-based care to patients in the long term. With innovative technologies like GenAI, hospitals can mitigate a significant part of this spend, and lower the cost of care delivery for payers and patients – while finding enhanced profitability to support their operations.

What contributes to high administrative spend?

Looking closer at the components of administrative spend reveals that hospitals are not executing corporate functions efficiently. Of $250bn of total administrative expenses of hospitals, 46% is spent on corporate functions.[4]

These include processes like drafting RFPs for procurement, creating POs, building reports, drafting SOPs, billing, compliance, and communicating with payers and vendors. When new hires are deployed to execute these processes, they take longer due to a lack of understanding of systems and workflows.

While hospitals have adopted Hospital Management Systems (HMS) to digitize these workflows, employees continue to execute them manually. For instance, drafting RFPs and SOPs can take anywhere between a few hours to days, wasting precious time of administrative staff.

Lowering hospital administrative spend with GenAI

In the context of administrative workflows, Generative AI (GenAI) is typically exploited for the following capabilities:

  1. Summarisation
  2. Text drafting
  3. Advanced search
  4. Knowledge management
  5. Knowledge extraction

These capabilities complement business process automation (BPA) to significantly accelerate administrative workflows, thus reducing administrative expenses for hospitals.

Take a look at the ways in which the technology can be applied across key administrative functions.

#1. Claims adjudication

Claims adjudication is typically preceded by the extraction of settlement invoices from hospitals, which are structured in different formats. GenAI can extract this data from non-standard formats, and eliminate the need for manual data extraction.

In addition, it can classify claims based on coverage of the policy. This helps decision-makers ensure that the payout is in alignment with the terms of the policy.

#2. Health plan marketing and sales

By analyzing patient data and history, generative AI can create personalized marketing material to enhance the conversion rates for healthcare plans. In addition, it can streamline the purchase experience for buyers through conversational interfaces, easing enrolment, and answering customer queries about their healthcare plans.

#3. Physician administration costs

In healthcare facilities, clinical managers spend a significant amount of time routing the right cases to clinicians and enabling collaboration between doctors of different specialties in complex cases.

Generative AI can abstract away most of the manual work involved in the process, by optimizing clinician assignments and scheduling appointments across the organization. Moreover, it can ease cross-speciality collaboration by enriching the context of the case, enabling clinicians to arrive at the right decisions faster.

#4. Legal and Compliance

Today, most hospitals execute compliance processes on paper, which requires multiple human resources to comb through legal documentation and patient files. A significant part of these processes adds little to no value to core operations.

Regulatory experts will find GenAI highly useful for extracting relevant data from documents and analyzing text data by making use of advanced search capabilities. Moreover, where multimedia files like images and PDFs are involved, multimodal models can be applied to extract, identify, and summarise relevant details, making regulatory filings faster and more efficient.

Bringing it together

Generative AI adoption cannot be a siloed undertaking. Hospitals must implement the technology in tandem with other levers like HMS, robotic automation, and advanced data architectures. This is important to ensure the security of patient data and facilitate inter-department collaboration. Otherwise, handoffs between various teams can inject delays and security risks in the absence of a centralized, secure environment.

Moreover, using generic GenAI tools could prove risky, especially due to issues like response variability, and non-specialized models which may not be suited for the healthcare domain. That’s why, hospitals looking to transform their administrative function for reduced operating costs will find it crucial to employ internal teams or partners that have prior experience in hospital digital transformation.


[1] https://www.livemint.com/money/personal-finance/medical-inflation-in-india-reaches-alarming-rate-of-14-reveals-report-11700634947658.html
[2] https://www.statista.com/statistics/720767/medical-cost-trend-in-us/
[3] https://www.mckinsey.com/industries/healthcare/our-insights/administrative-simplification-how-to-save-a-quarter-trillion-dollars-in-us-healthcare
[4] https://www.mckinsey.com/industries/healthcare/our-insights/administrative-simplification-how-to-save-a-quarter-trillion-dollars-in-us-healthcare


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

How Databricks Simplifies ML Model Development

Simplifying Machine Learning: Databricks’ Scalable and Collaborative Approach

Machine learning (ML) model development is widely characterized by its complexity. Effectively developing, deploying, managing, monitoring the performance, tracking versions, and sharing models is key to avoiding confusion, particularly when thousands of them are being experimented with, tested, or put into production at the same time.

As ML operations (MLOps) continue to evolve, data professionals increasingly seek a unified, machine learning platform to test, train, deploy, and monitor the performance of the models with minimal friction. Databricks, along with tools like MLflow, simplifies the ML lifecycle, right from data preparation to deployment, ensuring a seamless yet more rigorous and reproducible process.

Here are the key ways Databricks enhances the model creation process:

Unified Data Platform

The collaborative workspace in Databricks integrates tools for data engineering, data science, and business analytics. The unified environment uses a single source of truth that enables teams to efficiently work together, reducing silos and fostering a data-driven culture. From handling raw data to creating inference tables that log each request and response for a served model, the platform consolidates all the functions, streamlining the entire ML process. All the assets—models, functions, and datasets—are governed by a central catalog. As a result, it becomes easy to identify performance issues and maintain model quality with the built-in tracking and monitoring features, ensuring the process is traceable and streamlined.    

Scalability and Infrastructure

Powered by Apache Spark, Databricks offers a flexible and scalable infrastructure for handling large datasets. Leveraging Spark’s distributed processing framework, the platform allows parallel data analysis, significantly reducing processing time. Its autoscaling feature dynamically adjusts cluster resources, enabling seamless performance even as data volume and machine learning workloads increase.

AutoML Capabilities

The Databricks platform provides support for AutoML to automatically handle tasks such as data preprocessing, model selection, hyperparameter tuning, and model evaluation. AutoML in Databricks is built on top of open-source libraries, such as MLFlow and Hyperopt to streamline the machine learning workflow. It simplifies ML model development for users of different levels of expertise by providing both a low-code user interface and a Python API. After the data professional selects a specific dataset and ML problem type, AutoML handles data cleaning, orchestrates distributed model training across open-source evaluation algorithms such as scikit-learn, LightGBM, ARIMA, XGBoost, and, Prophet, and identifies the ideal performing model.

AutoML handles the classification, regression, and forecasting tasks by generating Notebooks for each trial. This allows professionals to review, replicate, and customize the code. Data exploration notebooks and the best trial can be automatically imported into the workspace. The rest of the trial notebooks, stored in the form of MLflow artifacts, can be manually imported through the AutoML Experiment UI.

AutoML’s approach to automating ML model development processes empowers professionals to create accurate models without extensive data science knowledge. It also delivers clear results and evaluation metrics, making the development process more transparent and efficient.

Comprehensive ML Lifecycle Support

Databricks platform offers multiple features that holistically support the entire ML lifecycle. These features include:

  • Data ingestion and preparation: Databricks facilitates the capturing of raw data from various sources, allowing professionals to merge the batch and streaming data, thereby maintaining data quality through scheduled transformations and versioning.
  • Feature Engineering (Feature Extraction and Feature Selection): Databricks provides a powerful, scalable, and flexible environment for performing feature engineering with a combination of Apache Spark, PySpark, Python, and integration with other popular libraries such as pandas, scikit-learn, and MLFlow. This includes support for both feature extraction and feature selection, which are critical aspects of the machine learning workflow.
  • Model training and experiment tracking: Databricks automatically tracks experiments, code, results, and model artifacts in a central hub, making it easier to reproduce results and manage model versions.
  • Deployment and monitoring: The Databricks platform simplifies the deployment of models into production and includes built-in monitoring tools to track model performance and data quality over time. This ensures model accuracy and regulatory compliance.

Advanced Analytics and Integration

Databricks supports diverse ML and deep learning frameworks, including scikit-learn, TensorFlow, and PyTorch. This compatibility provides data professionals the flexibility to use the tools they know best in an optimized environment. Additionally, the platform features advanced analytics capabilities of exploratory data analysis and interactive visualizations, which offer deeper insights from the datasets.

In conclusion, Databricks has become a game-changer for ML model development with its feature-rich comprehensive, collaborative, and scalable platform that simplifies and streamlines every aspect of the ML lifecycle, from data preparation through deployment to monitoring.

Altysys leverages Databricks to improve their productivity in ML model development and deployment, empowering industry-wide organizations to take advantage of the benefits of machine learning and AI effectively in their operations, regardless of their technical expertise.


Sumit Verma
Author:
Sumit Verma
Solutions Architect

Enhancing patient outcomes in healthcare with modern data lakes

With the rising volume of patient data and growing AI applications, healthcare organizations need robust data foundations to activate analytics at scale.

Healthcare data is rapidly growing in variety and volume. Every year, a typical patient generates nearly 80 MB of data in the form of radiological imaging, blood work, clinical notes, and prescriptions.[1] Therefore, unlike in other industries, healthcare data comprises both structured and unstructured data of differing formats.

At the same time, data is driving some of the most advanced use cases in healthcare technology today. From clinical decisioning to connected patient experiences, data is at the heart of large-scale care delivery transformation programs.

With falling costs of computation and the development of healthcare-specific AI applications, all care providers need to activate analytics at scale. Data lake technology is the answer to this pressing need in healthcare digital transformation.

Limitations of traditional data architectures

Despite significant leaps in AI and ML, healthcare organizations were limited by the data architectures that supported analytics over the last decade. Data warehouses were at the core of most architectural patterns, whereas structured data represented only a small fraction of healthcare data.

Moreover, data warehouses proved very costly for healthcare organizations: a 1 TB warehouse supporting 100,000 queries would cost north of $450,000 annually.[2] In addition, extensibility and scalability were a major limitation in on-prem models. Support for live data streams was difficult to implement, and pre-processing steps consumed a lot of time.

While cloud lowered the infrastructure costs, security and compliance were still a key concern for care providers. With these factors, healthcare organizations were expected to function like a technology company – a move that couldn’t be justified without proving RoI to senior leaders.

Why data lakes for healthcare analytics?

The challenges posed by data warehouses are no longer a limitation in healthcare analytics, thanks to the evolution of the data lake architecture.

What is a data lake?

Data lakes enable healthcare organizations to centralize the storage of structured and unstructured data, and unify the processing layer – thus enabling teams to consume analytics-ready data at scale. Because the schema of the data is not predefined, various use cases can be implemented over the data lake – like diagnostic decision support, remote patient monitoring, and so on.

Data lakes can be implemented on compliant cloud environments, where security operations can be handled centrally with Role or Policy-based access control (RBAC/PBAC).

How data lakes enhance patient outcomes

Data lakes are typically viewed from the perspective of data production and consumption. In a typical healthcare organization, data producers include:

  • EHRs,
  • medical device-generated data,
  • admin and pharmacy data,
  • files from radiology,
  • data streams from wearables, and
  • primary care data.

This data is unified and stored in its native format, allowing consumers – i.e., various analytics use cases, to manipulate it as needed. Data lakes are typically housed in low-cost storage tiers, enabling significant cost savings compared to data warehouses.

By making this data available in a central location, data lakes power complex analytics solutions. For instance, at the patient level, they can help with disease prediction, forecasting the trajectory of chronic conditions, and devising targeted treatment programs. This is facilitated by drawing inferences from various data sources at the same time. Moreover, hospitals can offer outpatient solutions like prescription adherence and continuous monitoring to drive better patient outcomes in the long run.

At the hospital level, data lakes can facilitate enhanced collaboration between physicians, and coordination with 3rd parties like payers and insurers.

To sum it up, these applications not only enhance the quality of care but also the patient experience at each stage of their journey – from the front desk to post-discharge care.

Next steps

How to modernize the data foundation at your healthcare organization

Building a data lake should begin with a thorough assessment of the use cases that your healthcare organization plans to implement. Based on this, data engineers devise an optimal architecture along with data governance mechanisms to support those use cases.

This is followed by configuration of the cloud environment, data integration, and cataloging. At first, such an initiative may seem daunting to hospitals with limited technical talent. However, data lakes and downstream analytics solutions can be easily implemented in collaboration with a technology partner that specializes in healthcare digital transformation. With trustworthy experts, the vision of connected, AI-enabled care is now within reach for healthcare organizations.


[1] https://publichealth.tulane.edu/blog/data-driven-decision-making/
[2]https://www.striim.com/blog/data-warehouse-vs-data-lake-vs-data-lakehouse-an-overview/


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Creating Value with Data Analytics in Health Informatics

In the post-pandemic era, most healthcare organizations renewed focus on being data-driven. The healthcare sector generates a lot of data in the form of electronic medical records, medical images, clinical data, and wearables; and it is growing exponentially. The digitization of healthcare records over the last few years, coupled with the proliferation of tech-driven medical devices as well as structured and unstructured patient-centric data, has led data volumes to skyrocket.

Unlocking the potential of this data paves a new path for improved healthcare outcomes, reduced healthcare costs and better community health management as the healthcare industry is undergoing a rapid transformation to be patient-centric. But how do we derive these needed insights from this data?

This is where data analytics comes to the fore.

Data analytics can help uncover patterns with different techniques to fuel the decision-making for healthcare organizations. Health informatics, the confluence of data, IT and business insights to manage patient care and healthcare operations have been witnessing great value from data analytics. But there is a catch.

Healthcare data is multi-faceted, a lot of it is unstructured, which often limits the extent to which this data can be analyzed. As per research from HIMSS and Arcadia, less than 60% of data in healthcare organizations is leveraged for intelligent insights. However, most healthcare leaders agree that quality data and insights from across healthcare systems are critical for organizations’ performance.

Harnessing the power of Data Analytics in Health Informatics

Well-informed decisions powered by data analytics can drive patient outcomes and empower change in new-age healthcare services. There is now more data than ever from electronic health records, medical images, clinical results, and patient logs to analyze and fuel decision-making. Here are a few data analytics use cases in healthcare:

  1. Forecasting operations – To improve the scheduling of hospital equipment and clinical procedures, providers need to understand patterns from their past data. This can help them to plan their resources more effectively. Distinctive operation capabilities can be a true differentiator in the new care model.
  2. Readmission risk – Readmissions increase costs significantly to healthcare providers. Hence, analyzing the data for a readmission score can help in enhancing post-operative care and communication with the patients.
  3. Managing medical errors – Errors in diagnosis, surgeries, and medical imaging affect around 400,000 patients annually. Data analytics can help identify these outliers to some extent and reduce the costs as well as improve the quality of care.
  4. Population health management – Understanding the healthcare needs within a community can help mitigate severe outbreaks and plan for the resources accordingly.

These are just a few to showcase the value of data analytics. A lot more can be achieved; depending on the payer and provider needs to reduce costs and optimize operations.

Benefits

Focusing on a data-led patient-centric care approach can help resolve a lot of existing challenges in healthcare. Here are a few benefits of leveraging data analytics in health informatics.

  • Preventative care and adherence to treatment plans
  • Personalizing treatment plans and care options
  • Lower costs and better operational efficiencies
  • Healthier population and communities

Conclusion

Patient-centric care is a transformative approach to new-age healthcare needs. Predicting health risks, and improving operational performance of healthcare facilities while understanding the patient behavior plays a vital role in patient-centric care. However, challenges with the security and privacy of patient data need to be mitigated while leveraging data analytics in health informatics. The path to value creation is bound to adopting data analytics, enhancing operational workflows and standardized practices. Want to kickstart your healthcare data analytics journey? Reach out to us at reach@altysys.com


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

The Promise of Large Language Models (LLMs) in Clinical Documentation

Clinical documentation plays a multi-faceted role in the world of value-based healthcare, which by default means it’s a mission-critical aspect for providers. For instance, accurate documentation is critically vital to support full and fair reimbursement. For patients, on the other hand, documentation is key to recording their medical history, current diagnosis, and ongoing treatment plan. And like any mission-critical system, it can create a heavy burden for clinicians and medical staff. According to a report by JAMA Internal Medicine, physicians spend nearly two hours each day after office hours to complete clinical documentation.

The issue of burnout has been a major talking point in the last decade when it comes to clinical documentation. While there have been several technological advances, especially in medical transcription, that have helped alleviate some of that burden, none have held the promise that large language models (LLMs) bring to the table.

The Prospect of LLMs in Clinical Documentation

LLMs were launched into an exponential trajectory of popularity with the launch of OpenAI’s ChatGPT, which brought the foundational models to the forefront of commercial application. These models excel at processing and manipulating natural language, which makes them great at generating, summarizing, classifying and extracting content in text form. By virtue of these capabilities, LLMs have emerged as the single most transformative technology in the field of clinical documentation.

Content synthesis and summarization

LLMs are capable of synthesizing data from numerous sources to generate contextual drafts for speedier collaboration between parties (for instance, between payers and hospitals). For instance, LLMs can streamline the creation of clinical documents by distilling ambient transcripts into a clearly distinguished Subjective Objective Assessment Plan (SOAP) format, converting large volumes of unstructured data into a universal, structured, systematic format.

On the other hand, using LLMs to optimize medical dialogue summarization can provide both physicians and patients with concise points that are easy to understand and retain. Similarly, LLMs can be employed to respond efficiently to patient queries, offering quick and accurate answers to questions regarding diagnoses, treatments, or medications, thereby saving time for healthcare providers while keeping patients well-informed.

Predictive health analytics

When exploring the possibility of LLMs in clinical documentation, predictive analysis of patient data emerges as a high-impact area. By effectively analyzing patient data, LLMs can highlight patients who stand a higher risk of certain conditions, allowing providers to plan more efficient treatment plans.

Improved value-based outcome

Their ability to extract and process unstructured data allows LLMs to provide unprecedented support to clinical decision-making tools and predictive models, based on nothing but natural language inputs. This includes providing data-driven insights into the potential risks and benefits of different treatment paths and informing balanced clinical decisions.

From an administrative perspective, they can automate the coding of medical procedures and services with superhuman accuracy, identifying and correcting coding errors and facilitating improved efficiency in billing processes. This would, in turn, lead to a significant reduction in denied claims and financial penalties.

The Risks and Road Ahead

For as long as LLMs have enjoyed the limelight, there has been a growing concern in the shadow: reliability of models. LLMs are pre-trained on a vast pool of data. However, the purpose of this training data is to decode human language, not use it as a knowledge repository.

The lack of reliability of LLMs takes many forms, but the one that is talked about is hallucination. This happens when an LLM that is faced with a question it does not know the answer to start generating an “educated” guess that may include false information or made-up facts.

There are a few other reliability issues with LLMs like:

  • Sycophancy: The LLM generates responses that conform to the user’s beliefs rather than the factually correct information
  • Potentially risky behavior: The LLM tries to manipulate corresponding systems and human users through its responses (read how GPT-4 convinced a human to fill a CAPTCHA for it)
  • Overconfidence: The LLM repeatedly generates incorrect responses due to overdependence on training data.

Proper guardrails can, however, ensure the model’s accuracy, which includes undertaking traditional validation exercises, developing a process to flag high-risk errors in a clinical setting, and always keeping a “human in the loop”. Moreover, while LLMs are more or less reliable when it comes to reductive tasks like analyzing data, summarization, categorization, and extraction), the risks are much higher for generative tasks.

Despite their shortcomings, LLMs have a disruptive potential that can not only transform medical documentation but also elevate the quality-of-care outcomes with greater efficiency. However, the application of this technology calls for thorough testing before it is implemented by care providers. Altysys, a technology expert partner, helps care providers rapidly prototype and implement LLMs across healthcare use cases to mitigate rising clinician burnout and derive the greatest possible value. Get in touch with us today and start reaping the benefits of LLMs in your medical documentation workflow.


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Using AI to Combat Alert Fatigue and Improve Care Delivery Outcomes

Alert fatigue has been one of the most challenging side effects of digitization in the healthcare sector. So much so, that it has consistently been named one of the top hazards in health technology over previous years.

Alert fatigue refers to the desensitization of clinicians and nurses to audio-visual warnings issued by smart patient monitoring devices or computerized provider order entry systems (CPOEs). This is caused by the sheer number and irrelevance of alerts that are delivered to healthcare workers daily – sometimes to the tune of 60,000+ alerts per day.

The impact of alert fatigue on care delivery

Alert fatigue not only erodes the trust of healthcare workers in decision support systems, but it also compromises the quality of care delivered by providers to their patients. In fact, alert fatigue is estimated to cause 200 deaths and 500 adverse events on an annual basis.

When urgent and irrelevant notifications are delivered to clinicians in the same stream, they must actively scan those notifications for relevance. For 90% of the alarms, no action is taken by clinicians, and some even try to bypass them in the fastest way possible.

Why alert fatigue is difficult to mitigate

Clinical decision support systems (CDSS), CPOEs, and monitoring devices are deployed to improve care delivery outcomes by notifying healthcare workers of significant events, allergic responses, or to prevent prescription errors. However, when the alerts delivered by them do not conform to the ‘principles of alert appropriateness’, they generate more noise than signals.

This issue is related partly to digital system design, and partly to the difficulty of implementing them effectively. Healthcare processes involve numerous workers, like clinicians from multiple sub-specialties and support staff. Poor implementations and lack of effective controls lead to incidents where topical steroid warnings related to a patient end up being delivered to a surgeon in an operating room. That such alerts should be ignored, then becomes a part of the problem. 

Mitigating alert fatigue in healthcare with artificial intelligence

Alert fatigue is essentially caused by a classification problem. If the “critical” alerts delivered by CDS systems can be classified into those that are really critical and those that aren’t, the noise can be eliminated from the flood of notifications received by them. Moreover, those notifications that are non-critical can be grouped together, and delivered to the right clinicians to reduce the overall number of notifications received by them in a typical workday.

So far, few attempts have been made to employ these strategies through the use of AI. Previously, closed-loop systems have been implemented, where clinicians actively provide feedback on each alert to weed out irrelevant alerts from going off the next time. However, such systems merely shift the burden from one type of action to a different one.

The real breakthrough would arrive in the form of a self-learning system that can assess the criticality of an alert through various attributes associated with it, and how clinicians respond to them. For instance, low-priority alerts are typically associated with topical steroid application warnings, duplicate order notifications, and low-value contraindication alarms. Such notifications can be filtered, grouped, and then delivered to clinicians with visual markers to indicate their evaluated criticality. This entails an analysis of medication alerts that fire at a healthcare organization, and identifying the top alerts by volume. These are then contextualized by drawing patient data from Electronic Health Record (EHR) systems to understand the impact of the alert changes, and the root cause that triggered the alert. These findings can then be fed back to the alerting system to optimize alert volume, and integrated with Best Practice Advisories (BPA) to further optimize the alert frequency and volume.

In a study, artificial neural networks (ANNs) based prediction models resulted in the best performance when compared to other algorithms. The implementation sought to predict clinician response to disease medication-related CDSS notifications, where ANNs offered an accuracy of 0.885, with 87% sensitivity, and 83% specificity. While these efforts are recent, the rapid adoption of cloud and modern software architectures in healthcare points to a significant opportunity for improving care quality.

Two of the key issues that must be considered in the design of such systems are data privacy and explainability of alert suppression and classification algorithms. In the EU and the USA, current regulations already require the use of anonymization techniques and impose explainability requirements on healthcare analytics solutions. The latter can prove challenging in the case of AI techniques like neural networks, where the reasoning behind the decisions of black box models must be explained.

Closing words

Alert fatigue is not only a crucial technology hazard that impacts the quality of care, but it also wastes valuable time of clinicians while eroding their trust in digital systems. While the time is ripe to eliminate this long-persistent issue in healthcare technology with the use of AI, providers must pay close attention to issues like data privacy and explainability before implementing such systems in the real world.

Healthcare 4.0: Towards Patient-Centric Care Delivery with Cloud

The healthcare sector has operated at the forefront of technological innovations since the last century. New technologies have helped surmount some of the most challenging diseases and conditions with repeatable success.

The adoption of these new technologies has been confined to the development of new care procedures and pharmaceutical innovations. By taking a page out of this history of healthcare as a discipline, care providers can unlock new paradigms of efficiency and care delivery with the mature technologies of today. In the spectrum of such technologies, cloud is of crucial interest for the modern healthcare organizations – especially as it is enabling care providers to create patient-centric care delivery models. The growing relevance of cloud in building the modern healthcare organization is evident in the numbers – the healthcare cloud market has grown to $35bn in 2022, and cloud is expected to create $170bn in value in the healthcare industry by 2030.

See how cloud can be leveraged to rebuild the healthcare organization for patient-centric care delivery by activating high-value use cases.

A roadmap for rebuilding the healthcare organization with cloud

In the legacy healthcare organization, the IT department operated on the sidelines to support care delivery and other back-office processes. It played the role of an enabler, and providers looked at IT as a cost center that was nested within the organization.

In the modern healthcare organization, this cost center approach is not relevant anymore, and IT plays a more integral role in care delivery. Such an organization has cloud at its foundations, and is more agile, transparent, and scalable, and delivers its services in a patient-centric fashion.

However, running the healthcare organization on the cloud is not a goal, but rather a means – to activate new models of care delivery (like continuous care, mobile experiences, and smart services for clinicians), which simplify and accelerate care delivery for all stakeholders. Hence, providers must devise a value-oriented journey to the cloud. Here are the foundational steps that will be inevitable in this journey.

Step 1: Storing and managing patient data more effectively in the cloud

Today, patient data resides in siloes and is distributed across multiple media – like paper, imaging reports, and digital applications. Cloud enables the healthcare organization to eliminate these siloes, and create a single, 360-degree view of a patient by storing their data at a single location.

As a result, clinicians and patients alike, have access to all their medical history in a single place. This significantly reduces the time spent by clinicians in gathering context about the patient. Moreover, it simplifies the patient experience and eliminates the need to carry multiple documents to care providers through every visit. Finally, it also enables more significant remote care interventions in follow-up scenarios, reducing footfall in hospitals, and mitigating the need to visit hospitals in person.

By storing patient data in the cloud, care providers can build modular services atop it to activate mobile experiences for patients, and decision support for clinicians. To move the patient data to the cloud, care providers must consider its regulatory implications, and devise a strategy that enables uninterrupted care delivery through the transition period. Cloud-based patient data management is a foundational capability – therefore, care providers need to assess the use cases they intend to build atop this capability before they embark on such a transition.

Step 2: Cloud-based healthcare information systems (HIS) to build seamless patient journeys

The next foundational capability that care providers must focus on, is the implementation of a cloud-based HIS to drive back-office and front-door processes. These include appointment scheduling, billing for appointments and diagnostics services, staff management, hospital asset management, and communications.

Cloud-based HIS eliminates the friction from these processes, thereby speeding workflows, and removing friction from the patient experience. Finance teams can seamlessly find billing codes for precise procedures, clinicians can order diagnostic services for patients with web and mobile apps, and patients can book appointments and services without having to reach out to call centers.

This significantly reduces administrative costs and enables care providers to build a connected patient journey. Cloud-based HIS can also be integrated with other systems like EHRs and ERPs using APIs to activate an integrated healthcare operating model by organizations.

Step 3: Improving transparency in core care-delivery procedures with digital experiences

Finally, one of the most important outcomes that can be achieved with the cloud, is the ability to connect physical and digital worlds with IoT devices, APIs, and connected assets. In healthcare, these capabilities can be used to create more transparent, and continuous care delivery procedures.

For instance, care providers can deliver real-time updates on the status of a procedure or surgery to the patient’s family through mobile experiences. Similarly, clinicians can make meaningful interventions in continuous care by observing patient data from medtech devices. Moreover, patient behavior can be logged through mobile apps to gain deeper insights into their health – thereby enabling better outcomes for the patients.

Such capabilities can enable providers to differentiate themselves and build new revenue streams through these services.


Summing it up

Cloud is becoming an essential element of the modern healthcare organization, especially as it helps them tackle some of the most pressing challenges facing the industry today. However, healthcare providers must recognize that building a cloud-based organization should not be their end goal. Instead, cloud is a foundational capability, into which targeted investments must be made. This requires decision-makers to identify key opportunity areas and build a high-ROI cloud roadmap. This will enable rapid recovery of investments, and pave the way for further innovations with technologies like generative AI, machine learning (ML), and the Internet of Things (IoT).

As healthcare providers will typically lack the technology expertise to navigate through a cloud transition, engaging a trusted technology partner will be the best way ahead for most. It will help them achieve assured success with the cloud, and build on the learnings of cloud journeys similar to their own. With a thriving technology foundation and expert technology partners, the next frontier of care delivery is now within arm’s reach for care providers.

Knowledge Center

Blog

Is Hadoop Holding Your Business Back? Have you tried Databricks?

Hadoop, initially developed to address the challenges of big data processing, has become a cornerstone ...

Read More
Blog

Enhancing clinical decision-making quality with Generative AI

With the ubiquity of patient data and clinical knowledge, GenAI can play a significant role ...

Read More
Blog

Mitigating clinician burnout in healthcare with GenAI

Amidst the rising shortage of healthcare workers, GenAI shows significant promise in reducing the burden ...

Read More
Blog

Reducing the administrative burden of care delivery with Generative AI

With corporate functions accounting for a major chunk of the healthcare spend, hospitals must identify ...

Read More
Blog

How Databricks Simplifies ML Model Development

Simplifying Machine Learning: Databricks’ Scalable and Collaborative Approach Machine learning (ML) model development is widely ...

Read More
Blog

Enhancing patient outcomes in healthcare with modern data lakes

With the rising volume of patient data and growing AI applications, healthcare organizations need robust ...

Read More