Author: altysys

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.

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

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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.

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^[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

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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.

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^[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

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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.

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^[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/

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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

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