Author: altysys

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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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.

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.

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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.

The healthcare industry saw significant opportunities through the application of technologies like cloud computing and automation in the last decade. Big data applications were developed over cloud-based technology infrastructure mostly over the last five years and brought key advancements in drug discovery, clinical trials, early diagnoses, and customer service.

Today, the healthcare sector is waiting to exploit the opportunities presented by another key technology – i.e., generative AI (GenAI). In fact, the three levers presented by the application of GenAI can help the healthcare sector achieve unprecedented efficiency in care delivery, and mitigate staff and clinician burnout in care provider organizations. Read on to understand these three levers in detail.

Rising administrative burden in care delivery systems

The healthcare machinery operates on a massive scale and serves millions of people through varying frameworks of care delivery. As a result, administrative functions are inevitable in such a system. However, the fraction of time and money spent on administrative functions in healthcare organizations is on the rise and is impacting the quality of care received by the patients as well as the costs incurred by payers.

Key Challenges

In the US alone, administrative spend on healthcare accounts for nearly 25% of the $4tn of total annual spend. This means that 1 out of every 4 dollars spent on healthcare goes into supporting the administrative functions, thus, increasing the cost of care to the patient or the payers.

Moreover, the rising administrative burden is being borne by clinicians and the administrative staff. Clinicians are now spending 13.5 hours/week on documentation on average, and this number has gone up by 25% compared to 7 years ago. Clinician burnout is, therefore, now a crucial challenge that is being faced by most healthcare organizations. In addition, the administrative staff must effectively communicate across multiple channels with different stakeholders like patients, insurers, clinicians, and payers to facilitate care delivery.

All these factors collectively lower the quality-of-care delivery outcomes or raise the costs of processes for each patient. 

Maximizing care delivery efficiency with GenAI

Most of the aforementioned challenges can now be mitigated with GenAI, a technology that leverages large language models (LLMs) to perform text-processing tasks. See its top applications below.

#1. Improving patient engagement and enhancing care delivery outcomes

Call centers, hospital administrative staff, and clinicians play a key role in keeping the patients engaged throughout the span of care delivery. While self-service apps are mitigating some of this burden, follow-up, patient education, and emotional support still take up considerable time for clinicians, nurses, and hospital staff, especially in chronic disease management.

The convergence of conversational and GenAI will now free up a considerable chunk of this effort. Patients are already comfortable with scheduling appointments and selecting care providers via chatbots. The adoption of chatbots can be extended to improve patient awareness of chronic conditions, improve adherence to prescribed care plans, and deliver emotional support through GenAI. GenAI algorithms can be trained to deliver personalized and engaging content based on patient behavior and create adaptive learning experiences for them.

All of these levers can ultimately improve the outcome of care delivery for providers and patients, resulting in a holistic uplift in the quality of healthcare interventions.

#2. Speeding EHR creation and updating procedures to minimize clinician burden

In the modern healthcare organization, electronic health records (EHRs) are crucial in simplifying the clinician and patient experience. However, many workflows involving the creation and updating of EHRs remain manual. Clinicians usually update EHRs through manual entry, which consumes time that could otherwise be directed to the patient and introduces room for human error in EHRs. Moreover, EHRs may have missing data, which impacts the quality of care that the clinicians can deliver.

In this context, GenAI can prove highly useful. It can automatically spot missing data in a patient’s EHR, and fill it in automatically, suggest diagnoses based on natural language queries, and even create summaries of patient-doctor conversations. LLMs are capable of spotting relevant text from such conversations, picking prescription names and dosages, and identifying medical terms to generate contexts of these conversations.

These capabilities are ripe to accelerate clinician productivity and improve the outcomes of care delivery for patients and clinicians.

#3. Enabling faster documentation to mitigate clinician and administrative staff burnout

In the healthcare ecosystem, documentation and correspondence between stakeholders comprise a significant fraction of administrative overheads. Over the years, some healthcare facilities have already moved from paper-based documentation to more advanced digital frameworks. For instance, a UK-based healthcare organization now makes use of speech-to-text services to help doctors, nurses, and healthcare professionals generate detailed notes with their voices.

GenAI is enriching these processes even further. It can synthesize data and information from multiple sources to generate contextual drafts for correspondence (for instance, with patients for disease queries, or with payers and insurers), thereby speeding collaboration between parties. Now, GenAI can also help physicians draft clinical notes, create discharge summaries, and highlight key information using years’ worth of existing clinical notes of patients. This can free up a significant amount of time for clinicians and administrators, reduce their overtime hours, and help direct their expertise to more valuable activities.

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Next Steps:

GenAI undoubtedly possesses the potential to transform core healthcare processes and elevate the quality of care outcomes while reducing administrative overheads on clinicians and support staff. However, the application of this technology calls for thorough testing before it is activated by care providers.

In partnership with technology experts like Altysys, care providers can rapidly prototype and implement GenAI use cases to mitigate rising clinician burnout. Get in touch with us today and start reaping the benefits of GenAI in your care delivery operating model.