With the rapid advancements in technology, augmented intelligence (AI) has the potential to improve healthcare systems and patient care. Implementing AI requires a well-thought-out readiness plan, including assessing data access, infrastructure, and training. By embracing a plan, healthcare systems can position themselves at the forefront of technological patient care delivery, improved operations, and greater fiscal responsibility.
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At a time when technology is advancing at a break-neck speed, the healthcare industry is not exempt from its transformative effects. As we stand on the brink of a new era, augmented intelligence tools are emerging as powerful allies in revolutionizing patient care and executive decision-making. But before leaping into this brave new world, healthcare organizations must ask themselves a crucial question: Are we ready to embrace augmented intelligence (AI)?
A Five-Step Readiness Plan to Harness Augmented Intelligence in Healthcare
Healthcare experts estimate that AI in healthcare will increase by 40 percent by 2028. The decision to adopt this tool goes beyond mere interest; it requires careful consideration and planning. Developing a comprehensive readiness plan is crucial to ensure successful integration and adoption. This guide provides insights for creating such a plan, highlighting key considerations and best practices for healthcare executives looking to leverage AI effectively in their organizations.
Assessing infrastructure and data accessibility is essential in evaluating a health system’s readiness to implement augmented intelligence. The health system’s infrastructure should support the deployment of AI technologies. Analysts must be able to easily integrate AI solutions into existing workflows and business intelligence (BI) tools.
Effective AI integration also requires data science expertise. Unfortunately, many health systems lack in-house data science talent, making it difficult to scale this expertise to the broader data analytics team. Therefore, this first step may further reveal the need for alternative data analytics solutions, or self-service needs, to achieve more refined and efficient data warehousing and management practices.
Data accessibility is another critical aspect to evaluate. Health systems must assess whether the organization can access relevant data in various domains and ensure it is standardized and properly structured. Standardizing data formats and structures facilitates interoperability between different systems and improves the quality and reliability of stored information. Adhering to internationally-recognized health data standards, such as HL7 or FHIR, offer a unified framework for exchanging electronic health records (EHRs) and other medical information across various platforms. Structured data also paves the way for advanced healthcare analytics that leverage machine learning and AI algorithms.
Apart from considering infrastructure and data accessibility, the effective integration of augmented intelligence necessitates two crucial factors: 1) Defining the goal that AI can help the health system achieve and 2) building trust in AI. Without a clear objective, haphazard attempts to integrate a new technology may leave organizational members disheartened and skeptical, breeding resistance and potential failure. Moreover, the pace and ease of implementing AI technology in healthcare directly determines its adoption and usage.
If the execution of AI tools is slow or cumbersome, teams may hesitate to adopt AI, potentially resulting in the complete avoidance of newer technologies. Having a distinctive objective, however, ensures that efforts to leverage AI remain on track and helps team members gauge their success and the effectiveness of the AI tool in their tasks or roles.
One way to build trust in AI is by ensuring that transparent and well-defined protocols back the implementation. Another way is seamlessly incorporating AI solutions and supports without disrupting current processes rather than utilizing a standalone tool. When end users view AI as an integral part of existing tools and processes, it becomes less intimidating or disruptive, fostering acceptance and trust.
When pursuing AI in healthcare, organizations must identify end users and their objectives in using AI solutions. End users may include engineers, clinicians, data analysts, and leadership executives. Understanding each end user’s role in data analytics and how AI might facilitate better decisions is a crucial next step in the AI adoption journey. For instance:
Indeed, AI applications can handle a wide range of tasks and challenges. Analysts and decision-makers, including board members, clinicians, hospital administrators, and executives, can benefit from AI technology without needing expertise in data science to calculate value improvements. To be sure, built-in statistical models analyze data, identify patterns, and make predictions, leading to better decision-making for all. While AI doesn’t simplify a problem, it can simplify how leaders assess and analyze big data in healthcare to gain meaningful insights across the organization.
Another vital consideration to effectively utilize AI is proper training and education for end users. Medical professionals do not need to develop a deep understanding of AI algorithms or data analysis techniques but must know how to apply an AI tool in clinical practice. The same is true for non-technical healthcare leaders; they must grasp how augmented intelligence fits into their role.
Therefore, training programs must provide comprehensive education on the fundamentals of AI and its role in healthcare systems. This includes explaining how augmented intelligence works, its limitations, and ethical considerations. Ongoing learning opportunities, workshops, and educational resources can help bridge knowledge gaps and empower healthcare providers, executives, and administrators with the necessary skills to make informed decisions based on AI-driven insights. When possible, offer customized training sessions to different groups of end users and departments based on their roles and responsibilities within the organization
Endeavor to maintain open communication channels where end users feel comfortable asking questions and providing feedback. Involving stakeholders and end users in the development and deployment process is key. For instance, seeking their input during the design and implementation phase can ensure the technology deployment and usage reflects their needs and preferences. Indeed, establishing a sense of partnership while adopting AI solutions can foster collaboration and transparency.
When incorporating a new augmented intelligence tool, healthcare systems may want to begin with smaller-scale AI integration, such as a pilot program to test the tool and address a single problem. The planning stage may include identifying a specific area of healthcare delivery that can benefit from more efficiency or remote monitoring, such as diagnostic accuracy, patient engagement, or treatment planning.
An AI pilot program can help leaders ascertain a tool’s potential impact. To do so, organizers must define key performance indicators (KPIs) beforehand to assess metrics like accuracy rates, time savings, cost reduction, or improvements in patient satisfaction. Stakeholders can therefore, collect relevant data throughout the duration of the pilot and compare it to pre-implementation benchmarks. A reputable technology partner can facilitate the pilot, guiding stakeholders through implementation without tapping unnecessary resources or placing additional burdens on teams.
AI in healthcare is still in its infancy, so garnering executive buy-in for implementing augmented intelligence solutions in your organization may take time and effort. Throughout the exploration and adoption phases, it is crucial to highlight how this technology will improve efficiency, productivity, and outcomes in revenue, operations, and patient care.
Demonstrating concrete examples of successful implementation in other industries and settings can provide tangible evidence of its effectiveness while dispelling concerns surrounding job displacement or decreased human involvement in clinical care decisions, according to Phil Rowell, Chief Analytics Officer at Health Catalyst and former Vice President of Clinical and Business Intelligence at Carle Health.
When exploring AI tool integration, therefore, present a well-crafted strategic plan that includes the following elements:
Emphasizing cost savings through reduced medical errors and optimized resource allocation will also pique executive interest. By illustrating how augmented intelligence streamlines administrative tasks like documentation, scheduling, or inventory management, stakeholders can see firsthand how this technology enhances workflow efficiency while allowing clinicians more time for patient interactions.
Understanding and effectively articulating AI’s numerous advantages will significantly contribute to gaining executive support. Furthermore, emphasizing the importance of staying ahead of technological advancements and remaining competitive should resonate with leaders. By adopting AI solutions, organizations can lead innovation efforts while enhancing patient care and organizational outcomes.
“Healthcare executives want to be assured that the technology they have selected for adoption will lead to continuous improvement and enable them to effectively translate data insights into actionable steps. AI is a tool that can help them make that next mission-critical business decision.”
- Phil Rowell, Chief Analytics Officer, Health Catalyst
An AI solution can derive better insights and be scalable in the future only when data is of high quality. Assessing an organization’s data integrity, completeness, and interoperability benefits every department. Inconsistencies in data collection or categorization across systems are red flags that an organization’s data quality needs attention. For instance, gaps in data from connected sources like electronic data warehouses and electronic medical records (EMRs) could indicate problems within data capture processes.
After adhering to data quality standards, leaders must focus on advanced privacy and security measures. Often built into an AI application, capabilities such as data encryption or role-based access controls can help secure sensitive data. Given the unprecedented rise in cybercrime, healthcare systems must employ extra security measures with any technological solution to safeguard against potential threats.
Experts contend that the global cost of cybercrime will surge in the next five years. Meanwhile, the average cost of a healthcare data breach peaked at $10.1 million in 2022, according to IBM’s “Cost of a Data Breach Report 2022,” which identified costs that can include ransom payments, computer network repairs, and government-imposed penalties for violating patient privacy laws.
Fortunately, AI is a versatile tool, and in the realm of data privacy and security, it combines human critical thinking with analytics automation to reinforce security parameters. Implementing AI privacy analytics benefits patients and providers in multiple ways, as its regular security audits and risk assessments protect sensitive data against cyber-attacks.
For instance, research shows that augmented intelligence enhances security and privacy by possessing algorithms that continuously monitor and detect anomalies and potential security breaches, alerting users when suspicious activities arise, which ensures early intervention. AI solutions can also regularly scan user access and employ authentication measures to ensure that only authorized personnel and patients can access personal data.
Johns Hopkins implemented an AI application to produce a highly effective privacy analytics model that reviewed every access point to patient data and detected when the EHR encountered a potential privacy violation, attack, or breach. Specific techniques, including supervised and unsupervised machine learning and transparent AI methods, advanced Johns Hopkins toward its predictive, analytics-based, collaborative privacy analytics infrastructure.
Johns Hopkins observed the following results after privacy and security enhancements —
Johns Hopkins demonstrates how healthcare systems can proactively prevent security breaches and their far-reaching effects with AI-enabled solutions.
Finally, healthcare organizations should consider several factors when selecting an augmented intelligence solution and vendor partner. An experienced solutions provider will understand the unique obstacles healthcare providers face and possess a deep understanding of regulatory compliance issues. It is also essential to assess the scalability of the technology offered, which must adapt to the health institutions’ changing needs and accommodate growth over time.
When selecting a partner for AI adoption, consider the user-friendliness of the solution or service, its ability to fulfill all your requirements, or whether you would need an additional layer of support or functionality down the road. Also, evaluate a vendor’s reputation in the industry regarding service quality and response times.
Furthermore, prioritize organizational needs and end-user goals when selecting AI tools and a vendor. Ideally, self-service AI products and expert services must address the following tenets:
In summary, partners must be transparent, forward-thinking, and possess real-world experience. They should also endorse augmented intelligence tools that seamlessly integrate into current workflows. Bringing this combination of expertise, sound judgment, and modern technologies to your organization will equip all stakeholders—executives, data analysts, and clinicians—to generate meaningful data-driven insights swiftly, realize the value of AI investments from inception, and advocate for scaling trusted solutions to tackle broader organization-wide challenges.
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