Digital Healthcare
Article | November 29, 2023
It’s no secret now that healthcare is an in-demand field. Today, business leaders need modern and intelligent decision-making solutions for their customers and clients. They must also focus on the right investment areas and learn the tricks for investing, spending, and setting goals for revenue generation to accelerate business.
With continuous developments in the healthcare sector, integrating AI into processes can help increase ROI. Therefore, if you, like any other business leader, are looking for solutions to empower your services and products in the healthcare domain, this article will help you through AI’s ultimate use cases and churn out a higher ROI.
What’s with AI in Healthcare at Present?
AI’s role in healthcare is evolving and enhancing traditional business operations, particularly marketing. According to a study by IBM, 71% of customers expect real-time communication. Thus, global demand is fueling the rising adoption of AI marketing solutions.
The effects of AI in healthcare are evident. Gartner reports increased marketing efficiency and effectiveness (86%), improved decision-making (71%), better data analysis and new insights (79%). Global AI spending will rise from $450 million in 2019 to over $28 billion by 2024 is not surprising.
Similar and further studies are ongoing on various use cases of AI in healthcare at scale. What are the efficient use cases of AI that will help healthcare businesses boost their ROI? Let’s find out.
How is AI Applied in Healthcare?
The promising applications of AI in healthcare to improve outcomes are very intriguing. While there is still much to achieve in the AI-dependent healthcare business, there is sufficient potential that tech companies are willing to invest in AI-powered tools and solutions.
Let’s examine the potential examples of AI in healthcare to prepare and support business strategies accordingly and foster higher ROI generation.
Predictive Analytics
AI-based predictive analytics impacts a business by automating administrative tasks, predicting sales outcomes for a year, customers’ behavior and making strategies accordingly. According to a Forbes study, AI-based predictive analytics can save businesses $18 billion in tasks, expenses, and pricing.
To understand this, one example of using AI to automate admin tasks is a collaboration between the Cleveland Clinic and IBM. Cleveland Clinic uses IBM’s Watson to mine big data and provides personalized services for customers and clients on marketing deeds.
Some of the practical applications of AI and predictive analytics in healthcare are:
Monitoring market trends to maximize marketing efforts
Organizing datasets
Creating marketing campaigns tailored to each demographic-based client
Mining collective data for future decision-making
Fraud Prevention
AmerisourceBergen Corp detects fraud and misleading business operations through AI. A sales account team conducts audits with AI to detect usual lea and queries to prevent hefty expenses for businesses.
The example explains that implementing AI in your process will help detect any significant fraud attempts inside your business operation. This will help your business save huge expenditures.
Boost Sales
By putting down false leads, AI helps in maximizing sales numbers, resulting in significant ROI generation. For example, AI transforms data into personalized data, which reduces the cost of operations.
Chatbots
Most healthcare businesses leverage chatbots on their websites to engage more and more customers and boost engagement. In this way, businesses tend to gain multiple leads and convert them into clients by providing the best marketing solutions.
Chatbots are fruitful for AI start-ups in healthcare—small businesses can deploy AI to their websites. By doing so, they can save millions in administrative costs and attract numerous leads.
The most prominent examples of AI in healthcare hail from giant tech titans such as IBM, Amazon, and Microsoft. They are assisting healthcare providers with AI to create and deploy digital-human employees.
Segmentation of Marketing Targets
Is your target audience not responding to your marketing campaigns (for example, by not clicking a link, subscribing or unsubscribing to a newsletter, or not registering for a medical event)? If that's the case, how should you go ahead?
Using AI-based tools allows your marketing to easily identify target behaviors and reactions based on the type of marketing actions to be carried out. Analyzing these actions can help segment targets based on your company's marketing objectives.
The most significant development took place in April 2022, when Amazon Alexa became fully HIPAA compliant. It works with health developers and service providers that manage protected information for customers.
AI Leads to Data Modernization
It’s all about the data—not any data!
There’s a precise association between AI and data management, resulting in data modernization. According to a Cognizant research study, healthcare leaders have made significant progress in modernizing their data. In contrast, most upcoming businesses are expecting to do so by 2024.
The maximum acceleration of AI in modernizing data will be seen in the manufacturing and marketing of healthcare products and services, respectively. It is because AI helps to churn data easily. The accessibility of data, in particular, becomes simpler with automation than doing it manually, which generates a massive amount of data. Such effects of AI in healthcare can be one of the prime reasons for the higher ROI of your business in the future.
“There has never been a greater need for skilled analytic talent in health care. Because AI is becoming more strategic, organizations must ensure access to this skill set, either by growing their analytic teams or seeking out experienced partners."
Steve Griffiths, CEO of Optum Enterprise Analytics
AI Expenditure is on the Rise
McKinsey says that by 2025, the use of AI in healthcare will be widespread, resulting in significant expenditure by global healthcare leaders.
AI is a significant concern for healthcare decision-makers, investors, and innovators as customers extensively engage and react to AI-powered services and solutions. AI is constantly bringing improvements to almost all processes, including cost savings, management of services and products, and monitoring of multiple operations. Even small businesses in the healthcare industry are proactively investing in AI applications to match steps with the current wave of innovation in healthcare services.
Accelerate ROI Using AI
AI in healthcare is becoming one of the prime responsible technologies for accelerating ROI. Technology can eradicate multiple business growth challenges. Let’s find out how.
Enhanced Performance
As previously stated, use cases of AI in healthcare can relieve stress on employees. This would allow them to devote their time to more value-added marketing activities to churn more ROI.
Emphasize Cost-Effectiveness
Most of the businesses associated with healthcare are concerned about the costs involved. With AI, they now develop policies to spend less on non-essential activities and necessitate profit-oriented actions.
"We believe in the potential of AI to deliver insights and operational efficiencies that unlock better health-care performance."
Robert Musslewhite, CEO at OptumInsight
Frequently Asked Questions
How is AI used in healthcare?
AI in healthcare automates and predicts processes by analyzing data throughout. It is used to predict potential customers, improve business management workflows, and manufacture medical products.
How does AI drive growth in the healthcare industry?
AI drives business growth by improving the ability to understand better day-to-day customer patterns and needs based on services and products.
How is AI changing the Healthcare industry?
AI applications in healthcare have demonstrated their potential to improve analytics and data management and assist service providers in making timely medical decisions.
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Health Technology, Digital Healthcare
Article | August 16, 2023
As medical science has improved rapidly, life expectancy around the world also has risen. Still, as longevity increases, healthcare systems are facing a growing demand for advanced services, increased costs, and a struggling workforce to meet various requirements of patients. Demand is driven by many unstoppable forces: a shift in lifestyle choices, shifting patient expectations, population aging, and the never-ending cycle of innovation are a few among others.
Challenges Faced by the Healthcare Industry
According to Mckinsey, one in four people in North America and Europe will be over the age of 65 by 2050. This shows that, soon, the healthcare industry will have to deal with a larger number of patients with more complex requirements. Catering to these patients is expensive and requires health systems for providing long-term focused and proactive care. To remain sustainable, healthcare systems need major transformational and structural changes.
The industry also needs a larger workforce because according to the World Health Organization (WHO), globally there is a shortfall of approximately 9.9 million nurses, physicians, and midwives. Apart from attracting, training, and retaining these healthcare professionals, you also have to ensure that their time and effort add value to patient care. Utilizing the solutions powered by modern technologies, such as Artificial Intelligence (AI) in the healthcare industry, will add perfection and more value to human efforts.
AI in the healthcare industry has the potential required to transform and revolutionize healthcare by addressing the challenges in the industry mentioned earlier. AI can better the outcomes, improve efficiency, and augment productivity in healthcare delivery. This article takes an in-depth look at the impact of AI in healthcare.
Impact of AI in the Healthcare Industry
In the coming years, AI in the healthcare industry will improve the day-to-day life of healthcare practitioners, augment the patient experience, improve care delivery, and can even facilitate life-saving treatments and revolutionize the industry. Additionally, AI will improve population-health management, operations, and strengthen innovations.
According to Statista, the global AI healthcare market will increase to more than US$28 billion by 2025. Here is a detailed look into the areas where and how AI in the healthcare industry will be impactful.
Chronic Care Management
Chronic diseases, such as cancer, diabetes, kidney diseases, are the leading cause of disability and death in the US and the main drivers of the country’s annual health cost. Effectively managing various chronic diseases is an overarching and long-term process. But with the help of the right tool, healthcare providers can meet the needs of these patients without delay.
Artificial intelligence tools in the healthcare industry can help healthcare providers overcome the complexities of chronic disease management and make it more effective and provide quality treatment. AI in the healthcare industry is increasingly being leveraged by organizations to improve chronic disease management, enhance patient health, and drive down costs, which will also eventually result in data-driven and personalized care. AI in the healthcare industry is expected to move the industry toward proactive care delivery from a reactive one and lead the industry to provide more individualized treatments. This is just one of the ways AI in the medical industry is going to revolutionize chronic care management in hospitals.
Care Delivery
Artificial intelligence in the healthcare industry is changing the way care is delivered; it is expected to make healthcare more efficient, accurate, and accessible. Reducing costs and improving health outcomes are the values health systems and hospitals are trying to deliver to patients every day. Hospitals are increasingly incorporating technologies, which are powered by the use of AI in healthcare to meet the challenge.
According to the American Hospital Association (AHA), AI in the healthcare industry has unlimited potential to solve most of the vexing challenges in the industry. They identify AI use cases in the healthcare industry in four broad areas, which are administrative, operational, financial, and clinical areas.
Administrative Use Cases for AI in the Healthcare Industry
• Admission procedures
• Appointment scheduling
• Customer service responses
• Discharge instructions
• Hiring and orientation protocols
• Licensure verification
• Patient check-in procedure
• Prior authorizations
• Quality measure reporting
Operational Use Cases of AI in the Healthcare Industry
• Inventory management
• Materials management
• Supply chain management
• Facilities management
Financial Use Cases for AI in the Healthcare Industry
• Billing and collections
• Claims management
• Insurance eligibility verification
• Revenue cycle management
Clinical Use Cases of AI in the Healthcare Industry
• Predictive technologies
• Interventional technologies
By incorporating and utilizing these scopes with AI in the healthcare industry, the industry can be transformed into a next-gen level in no time. It also allows healthcare practitioners to focus more on patients, which would eventually help in raising staff morale and improving retention.
Clinical Decision Support
Recent advancements in AI in the health industry are capable of enhancing the currently used clinical decision support (CDS) tools to have value-based imaging and to improve patient safety. According to the National Institute of Health (NIH), the synergy between CDS systems and AI in the healthcare industry will be able to:
• Reduce friction in radiology workflows
• Identify relevant imaging features easily
• Generate structured data to develop machine learning algorithms
• Enable an evolution toward decision support for a holistic patient perspective
• Suggest imaging examinations in complex clinical scenarios
• Assist in identifying appropriate imaging opportunities
• Suggest appropriate individualized screening
• Aid health practitioners to ensure continuity of care
AI in the healthcare industry is competent in making CDS a next-gen one, enhancing the experiences of radiologists and providers, and improving patient care.
Diagnostics
Slowly but surely, AI is improving almost every aspect of human life with innovations and advancements. The latest is that AI in the healthcare industry is impending a revolution in medical diagnostics by providing accurate risk assessments, accelerating disease detection, and boosting hospital productivity. By automatically prioritizing urgent cases and accelerating reading time, image recognition AI enhances the workflow of radiologists. It even helps in the prevention of diseases by the early detection of diseases.
In medical images such as x-rays, MRIs, and CT scans, AI-driven software can efficiently be used to accurately spot signs of many diseases, especially in detecting many chronic diseases such as cancer. According to the NIH, AI will be widely applied in the healthcare industry especially for various tasks such as patient engagement and adherence, diagnosis, and treatment recommendations. So, there is no doubt that AI in healthcare will revolutionize the diagnostic process in the approaching years by detecting diseases, classifying diseases, and improving the decision-making process. The application of AI in the healthcare industry will make people live longer.
Triage and Diagnosis
AI can be effectively used to automatically triage cases. AI algorithms will analyze the cases and forward cases to pathologists after determining the priority based on the probability of cases according to the criteria set by labs. This makes the workflow of pathologists easier and efficient. Through the process the algorithm will be able to:
• Verify the digital images attached to the case belong to that case
• Validate the tests ordered and match the specimen type
• Identify cases marked as stat
• Determine the cases, which can be positive or are most likely to be negative
Moreover, AI technologies in the healthcare industry also can be effectively used to provide more accurate and faster diagnoses. This speeds up the entire process of triage and diagnosis and is expected to revolutionize the healthcare industry soon.
The Future Outlook for AI in the Healthcare Industry
Over the next few years, AI in the healthcare industry has the best opportunities in hybrid models to support clinicians in diagnosis, identifying risk factors, and in treatment planning. This scope will result in faster adoption of AI technology in healthcare, which will show measurable improvements in operational efficiency and patient outcomes.
With a plethora of issues to overcome, which are driven by documented factors such as growing rates of chronic diseases and the aging population, it is obvious that the healthcare industry needs new innovative solutions. AI-powered solutions in the healthcare industry will achieve a clear impact on the global healthcare industry in a short time.
Frequently Asked Questions
Which is the best application of AI in the healthcare sector?
Cognitive surgical robotics is the best application of AI in the healthcare sector as it helps practitioners collect data from real surgical processes, which would help in improving existing surgical approaches.
Why is artificial intelligence important in healthcare?
Artificial intelligence in healthcare is vital as it can help make decisions, analyze and manage data, and have conversations. So, AI will drastically change the everyday practices and roles of clinicians.
When was AI-first used in healthcare?
The term, Artificial intelligence (AI) was first described in 1950, but the limitations of the term prevented its acceptance. In the 2000s, these limitations were overcome and people started to accept the term.
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Health Technology, AI
Article | July 18, 2023
Embark on a journey into the frontier of healthcare innovation in this article. Discover how EHR telemedicine and remote patient monitoring serve as catalysts, driving forward a new era in healthcare.
Contents
1. Integration of EHRs in Telemedicine and Remote Patient Monitoring
2. Technical Challenges and Solutions in EHR Integration
3. Financial Analysis: Cost-Benefit Assessment of Integration
4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems
5. Forging Stronger Patient-Clinician Relationships
1. Integration of EHRs in Telemedicine and Remote Patient Monitoring
EHR telemedicine and remote patient monitoring have reshaped healthcare delivery by seamlessly integrating electronic health records, allowing healthcare providers and patients to exchange information effortlessly, regardless of geographical barriers. This synergy empowers healthcare professionals to access patients' comprehensive medical histories in real time, facilitating more informed decision-making during virtual consultations.
During the spring of 2020, when pandemic restrictions kept most people in the US at home, the use of telehealth rose to about 51%.
[Source: Elation Health]
Moreover, it enhances the accuracy of remote patient monitoring by providing up-to-date data, enabling timely interventions and improving overall healthcare outcomes. Integrating EHR telemedicine systems enhances efficiency and ensures that patient care remains at the forefront of modern healthcare, transcending traditional physical boundaries.
2. Technical Challenges and Solutions in EHR Integration
Navigating telehealth EHR integration and remote patient monitoring solutions uncovers a range of technical challenges, each with its own set of potential remedies. These include interoperability issues, which can be mitigated by adopting standardized data formats like HL7 FHIR. EHR interoperability solutions may involve using data exchange protocols such as HL7's Consolidated Clinical Document Architecture (C-CDA) or developing custom APIs to facilitate seamless data exchange between EHRs and telemedicine platforms. Additionally, the imperative need for data security and privacy is achieved through robust encryption and adherence to regulations like HIPAA or GDPR. Data integration challenges arising from varying EHR data storage methods can be resolved using middleware or integration platforms. Investing in telecom infrastructure and developing offline-capable telemedicine apps can address limited connectivity in remote areas. Ensuring real-time data access involves optimizing EHR databases and creating low-latency systems. Other challenges encompass integrating data from medical devices, ensuring data accuracy, scalability, user-friendly interfaces, regulatory compliance, and cost management strategies.
3. Financial Analysis: Cost-Benefit Assessment of Integration
When contemplating the integration of EHR telemedicine and remote patient monitoring systems, conducting a comprehensive cost-benefit analysis is crucial. This assessment covers financial aspects, including initial implementation costs (software development, hardware upgrades, training, and data migration), ongoing operational expenses (maintenance and data storage), and potential efficiency gains (streamlined workflows and improved data accessibility). It also evaluates the impact on patient outcomes, satisfaction, and financial benefits of enhanced healthcare quality, reduced readmissions, and increased patient engagement. Healthcare organizations can estimate cost savings in remote patient monitoring and explore expanding telemedicine services to underserved populations to make informed financial decisions.
Additionally, this analysis considers long-term financial viability and alignment with organizational goals, including regulatory compliance costs, risk assessment, scalability considerations, and the competitive advantages of integrated telemedicine services. By calculating ROI and assessing potential risks, healthcare entities can develop risk mitigation strategies, ensuring that EHR integration in telemedicine and remote patient monitoring enhances healthcare delivery and aligns with the organization's financial sustainability and long-term success.
4. Data Privacy and Consent in Integrated EHR-Telemedicine Systems
Data privacy and obtaining informed consent are paramount in integrated EHR and telemedicine systems. Patients should provide explicit consent, understanding the data collected and its intended use, with strict encryption protocols safeguarding data during transmission. Access controls and data minimization practices restrict unauthorized access, while patient portals enable individuals to manage their data-sharing preferences and revoke consent if needed. Compliance with regulations such as HIPAA or GDPR is crucial, as is maintaining comprehensive audit trails to track data access. Training, awareness, and robust incident response plans fortify data privacy efforts, fostering trust and transparency in these integrated systems where healthcare organizations and patients share responsibility for secure data handling.
5. Forging Stronger Patient-Clinician Relationships
Integrating EHR telemedicine and remote monitoring systems goes beyond mere efficiency and accessibility objectives. It serves as a catalyst for nurturing more substantial and meaningful patient-clinician relationships. This fusion of technology and healthcare has the capacity to bridge physical distances, allowing clinicians to truly understand and engage with their patients on a deeper level. Patients, armed with increased access to their health data, become more active participants in their healthcare, while clinicians, with their comprehensive information, can offer more personalized and informed guidance. The potential of EHR telemedicine reaches far beyond the digital screen; it empowers both patients and clinicians to collaborate in pursuit of improved health outcomes, ushering in a new era of patient-centric care grounded in trust, communication, and shared knowledge.
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Health Technology
Article | July 13, 2022
Long-term care comprises all the health services that help patients with chronic illnesses or disabilities meet their medical and non-medical needs. It caters to those who cannot care for themselves for extended durations. For care providers, it becomes critical to meet the needs of patients on time while delivering top-notch quality, especially at a time when virtual care is more important than ever.
To remedy this, many of the tasks and processes within long-term care are supported by digital solutions. These long-term care software applications enable care providers to automate aspects of patient scheduling, inventory control, regulation and compliance, data management, care delivery management, and much more. Some of the end users of long-term care software include home healthcare agencies, nursing homes, and residential hospice care facilities.
What is Driving the Growth of Long-Term Care Solutions?
Digitalization has swept the healthcare industry, and medical technology now occupies a significant area of medical care delivery. With the demand for a robust healthcare infrastructure aggravated by a shortage of medical professionals, the need for automation is driving the growth of medtech across all areas of healthcare. In addition, fewer medical specialists and medical cost reduction initiatives combined are powering the long-term care software market’s growth.
Challenges for the Long-Term Care Software Market
Despite the rapid growth in the use of digital solutions to manage administrative and compliance tasks, technological transformations are expensive. The high maintenance costs incurred by care providers are a major hindrance towards a full-fledged adoption. Many care providers are also unwilling to adopt new applications due to the implementation and staff training costs involved in doing so.
What the Future Holds?
With an increase in remote care and the use of technologies like the Internet of Medical Things to deliver diagnostic services and preventive care, medtech is witnessing a revolution. Long-term care is bound to follow suit thanks to areas like remote patient monitoring and wearable technology. While the long-term care market is slated to grow by leaps and bounds, solution makers must find a way to help care providers warm up to the use of technology and de
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