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Imagine stepping into a future where every aspect of your healthcare—from initial consultation to payment—is seamless, secure, and personalized.
While that level of service may feel far off, advances in data analytics and automation are making it a near reality. And this new era of patient care is the ultimate win-win-win:
Even before the pandemic, much of the patient lifecycle went virtual. Our charts got digitized. We began chatting with doctors and therapists over the phone. Specialists received pathology and radiology reports via email.
These digital enhancements reduced the need for in-person visits or discussions, saving time and resources while expanding access to care for individuals in remote or underserved areas. But they only scratched the surface of the care and efficiency that data analytics, automation, and machine learning can generate.
At major healthcare companies, configuring benefits plans and processing millions of claims per year takes significant manual work. AI can automate the process end-to-end. At the same time, it can assess the likelihood of claims acceptance and uncover fraud, waste, and abuse, speeding up claim approvals, enhancing the accuracy of reserves for forth coming claims, and mitigating losses.
Of course, that level of automation requires a solid data foundation—something Launch has helped several enterprise healthcare organizations achieve. One Fortune 50 healthcare company used Launch’s digital transformation services to streamline their claims data, automatically surface low-confidence claims records, and leverage AI to process and maintain claim record quality over time.
With this transformation, the company could reallocate the thousands of employees they used to handle claims manually to more strategic roles, boosting productivity and lowering internal operational costs while processing claims faster for their members.
Because our healthcare records are digital, there’s no reason providers can’t consumerize the patient experience. And it’s something patients want and expect—just think about how personalized your experience is at Starbucks, Delta, and Amazon.
With the right technology stack and architecture, providers can build centralized portals that function similarly to a retail app:
As Neil Crist, Managing Director at Launch Healthcare, says,
“AI can transform the patient journey by providing personalized care plans, predictive insights, and real-time notifications to ensure that each patient receives tailored and effective treatment.”
VSP is a fantastic example of patient consumerization in action. With Launch’s help, they committed to a data-first, cloud-first approach to customer experiences, owning all pieces of the customer journey and connecting them in an intuitive and engaging way—as a top retailer would. A personalized journey like VSP’s engages patients with their health and improves treatment outcomes.
In areas like radiology, pathology, and ophthalmology, AI can be particularly transformative. It can assist clinicians in detecting conditions such as cancers, retinal diseases, and other disorders. Getting a patient’s diagnosis right the first time—and potentially earlier—can improve their prognosis and decrease the burden on healthcare systems.
Philips Lumify, the first app-based portable ultrasound system, is a groundbreaking example of AI-powered diagnostic imaging. This system, designed in collaboration with Launch, connects toAndroid devices, enabling high-quality, mobile ultrasound imaging accessible to a wide range of users—without compromising security or speed.
Because of its cloud-based image storage structure, providers can offer real-time diagnostics from anywhere, setting anew standard in patient care and medical imaging.
Telemedicine has already enabled patients to receive timely consultations from specialists regardless of geographical barriers. AI-driven chatbots take this care to the next level with 24/7 health advice and built-in triaging workflows, directing patients to the appropriate level of care without the need for in-person visits.
This is a huge advantage in regions with a shortage of medical professionals, enabling providers to offer consistent and scalable access to healthcare advice and support. Youper, an“empathetic, safe, and clinically validated” chatbot, for example, supports mental healthcare for over two million individuals and counting.
Patients use Youper to find customized meditations, have illuminating conversations, track their moods, and regulate their emotional health. Because Youper is available in the App Store and GooglePlay, it’s accessible to anyone with a smartphone, reaching those needing psychological support without skyrocketing costs.
Encryption methods powered by AI—and potentially the blockchain—protect patient data from unauthorized access, even when patient data is exchanged from a health professional to a hospital to a pharmacy.
Machine learning models also improve health data security by continuously monitoring for and responding to new cyberthreats and tagging any HIPAA or other compliance violations immediately. Designed correctly, ML models can prevent data breaches and help providers keep up with changing regulations with quick iteration and deployment.
Protenus, for instance, uses AI to monitor and protect patient data, detecting anomalies that could indicate data breaches or misuse. Widely available tools like Protenus (or other homegrown security solutions) build patient safety and trust and safeguard compliance.
Predictive analytics can help clinics and hospitals reduce bottlenecks and optimize care in critical areas such as the emergency room and intensive care unit by:
But AI is useful in non-emergency settings, too. It can take care of routine tasks like appointment bookings, data entry, billing, and compliance checks, freeing healthcare professionals to focus more on their patients.
“Streamlining clinical workflows with integrated digital tools not only reduces administrative burdens but also enhances efficiency and accuracy, allowing healthcare providers to dedicate more time and attention to direct patient care rather than cumbersome paperwork,” says Neil. “The use of automation and AI technology ensures consistency and reliability in healthcare processes, significantly reducing the likelihood of errors and improving overall quality and outcomes of patient care."
LeanTaaS is a good example of how AI relieves clinicians' mental workloads at Penn Medicine, Stanford Health, and New York Presbyterian. Machine learning models at these hospitals automatically surface ways to streamline operations, optimize surgery schedules, and manage inpatient bed capacity, ultimately offering patients better care with fewer staff in less time.
AI-driven pharma can lead to outsized impacts on billions of people’s health and longevity.
For one, models can analyze scientific literature, genetic data, and biological databases to distinguish and validate new drug targets—genes, proteins, or other molecules—for potential candidates for therapeutic intervention. Once targets are validated, AI can screen thousands of potential drug compounds to narrow the list for further testing.
Atomwise, for instance, uses AI to predict how molecules will behave and interact with each other, reducing reliance on costly and time-consuming physical assays and accelerating the drug discovery process.
AI can also identify individuals likely to respond well to a treatment based on their genetic makeup and medical history. It can also monitor trial data in real-time to pinpoint potential safety issues or adverse effects. Plus, feeding AI models real-world data can help researchers understand how drugs perform in diverse populations outside the controlled conditions of clinical trials—a crucial step forward for personalized medicine.
Healthcare providers can deliver more efficient and effective care by embracing digital tools. And early adopters are already seeing the benefits—decreased costs, happier staff, and, most importantly, superior patient health outcomes.
That’s why staying at the forefront of digital change is so imperative. But going all-in on AI and other emerging technologies doesn’t happen overnight. It requires enterprise-wide digital transformation and a proper data foundation.
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Imagine stepping into a future where every aspect of your healthcare—from initial consultation to payment—is seamless, secure, and personalized.
While that level of service may feel far off, advances in data analytics and automation are making it a near reality. And this new era of patient care is the ultimate win-win-win:
Even before the pandemic, much of the patient lifecycle went virtual. Our charts got digitized. We began chatting with doctors and therapists over the phone. Specialists received pathology and radiology reports via email.
These digital enhancements reduced the need for in-person visits or discussions, saving time and resources while expanding access to care for individuals in remote or underserved areas. But they only scratched the surface of the care and efficiency that data analytics, automation, and machine learning can generate.
At major healthcare companies, configuring benefits plans and processing millions of claims per year takes significant manual work. AI can automate the process end-to-end. At the same time, it can assess the likelihood of claims acceptance and uncover fraud, waste, and abuse, speeding up claim approvals, enhancing the accuracy of reserves for forth coming claims, and mitigating losses.
Of course, that level of automation requires a solid data foundation—something Launch has helped several enterprise healthcare organizations achieve. One Fortune 50 healthcare company used Launch’s digital transformation services to streamline their claims data, automatically surface low-confidence claims records, and leverage AI to process and maintain claim record quality over time.
With this transformation, the company could reallocate the thousands of employees they used to handle claims manually to more strategic roles, boosting productivity and lowering internal operational costs while processing claims faster for their members.
Because our healthcare records are digital, there’s no reason providers can’t consumerize the patient experience. And it’s something patients want and expect—just think about how personalized your experience is at Starbucks, Delta, and Amazon.
With the right technology stack and architecture, providers can build centralized portals that function similarly to a retail app:
As Neil Crist, Managing Director at Launch Healthcare, says,
“AI can transform the patient journey by providing personalized care plans, predictive insights, and real-time notifications to ensure that each patient receives tailored and effective treatment.”
VSP is a fantastic example of patient consumerization in action. With Launch’s help, they committed to a data-first, cloud-first approach to customer experiences, owning all pieces of the customer journey and connecting them in an intuitive and engaging way—as a top retailer would. A personalized journey like VSP’s engages patients with their health and improves treatment outcomes.
In areas like radiology, pathology, and ophthalmology, AI can be particularly transformative. It can assist clinicians in detecting conditions such as cancers, retinal diseases, and other disorders. Getting a patient’s diagnosis right the first time—and potentially earlier—can improve their prognosis and decrease the burden on healthcare systems.
Philips Lumify, the first app-based portable ultrasound system, is a groundbreaking example of AI-powered diagnostic imaging. This system, designed in collaboration with Launch, connects toAndroid devices, enabling high-quality, mobile ultrasound imaging accessible to a wide range of users—without compromising security or speed.
Because of its cloud-based image storage structure, providers can offer real-time diagnostics from anywhere, setting anew standard in patient care and medical imaging.
Telemedicine has already enabled patients to receive timely consultations from specialists regardless of geographical barriers. AI-driven chatbots take this care to the next level with 24/7 health advice and built-in triaging workflows, directing patients to the appropriate level of care without the need for in-person visits.
This is a huge advantage in regions with a shortage of medical professionals, enabling providers to offer consistent and scalable access to healthcare advice and support. Youper, an“empathetic, safe, and clinically validated” chatbot, for example, supports mental healthcare for over two million individuals and counting.
Patients use Youper to find customized meditations, have illuminating conversations, track their moods, and regulate their emotional health. Because Youper is available in the App Store and GooglePlay, it’s accessible to anyone with a smartphone, reaching those needing psychological support without skyrocketing costs.
Encryption methods powered by AI—and potentially the blockchain—protect patient data from unauthorized access, even when patient data is exchanged from a health professional to a hospital to a pharmacy.
Machine learning models also improve health data security by continuously monitoring for and responding to new cyberthreats and tagging any HIPAA or other compliance violations immediately. Designed correctly, ML models can prevent data breaches and help providers keep up with changing regulations with quick iteration and deployment.
Protenus, for instance, uses AI to monitor and protect patient data, detecting anomalies that could indicate data breaches or misuse. Widely available tools like Protenus (or other homegrown security solutions) build patient safety and trust and safeguard compliance.
Predictive analytics can help clinics and hospitals reduce bottlenecks and optimize care in critical areas such as the emergency room and intensive care unit by:
But AI is useful in non-emergency settings, too. It can take care of routine tasks like appointment bookings, data entry, billing, and compliance checks, freeing healthcare professionals to focus more on their patients.
“Streamlining clinical workflows with integrated digital tools not only reduces administrative burdens but also enhances efficiency and accuracy, allowing healthcare providers to dedicate more time and attention to direct patient care rather than cumbersome paperwork,” says Neil. “The use of automation and AI technology ensures consistency and reliability in healthcare processes, significantly reducing the likelihood of errors and improving overall quality and outcomes of patient care."
LeanTaaS is a good example of how AI relieves clinicians' mental workloads at Penn Medicine, Stanford Health, and New York Presbyterian. Machine learning models at these hospitals automatically surface ways to streamline operations, optimize surgery schedules, and manage inpatient bed capacity, ultimately offering patients better care with fewer staff in less time.
AI-driven pharma can lead to outsized impacts on billions of people’s health and longevity.
For one, models can analyze scientific literature, genetic data, and biological databases to distinguish and validate new drug targets—genes, proteins, or other molecules—for potential candidates for therapeutic intervention. Once targets are validated, AI can screen thousands of potential drug compounds to narrow the list for further testing.
Atomwise, for instance, uses AI to predict how molecules will behave and interact with each other, reducing reliance on costly and time-consuming physical assays and accelerating the drug discovery process.
AI can also identify individuals likely to respond well to a treatment based on their genetic makeup and medical history. It can also monitor trial data in real-time to pinpoint potential safety issues or adverse effects. Plus, feeding AI models real-world data can help researchers understand how drugs perform in diverse populations outside the controlled conditions of clinical trials—a crucial step forward for personalized medicine.
Healthcare providers can deliver more efficient and effective care by embracing digital tools. And early adopters are already seeing the benefits—decreased costs, happier staff, and, most importantly, superior patient health outcomes.
That’s why staying at the forefront of digital change is so imperative. But going all-in on AI and other emerging technologies doesn’t happen overnight. It requires enterprise-wide digital transformation and a proper data foundation.
