Are AI Tools Making Us Think Too Much?
It’s a funny question to ask and seems somewhat antithetical, but are AI tools making us think too much? From the standpoint of an AI tool’s output you could argue quite the opposite. But from a UX perspective, the tools themselves may be requiring more effort to use than people are typically used to.
By Andy Rench
Don’t Make Me Think
Steve Krug’s classic book “Don’t Make Me Think” has been an invaluable resource for UX Designers for decades and still feels relevant today. The title alone is a useful mantra for any designer working towards creating usable experiences that pose as little user friction as possible. Emphasizing intuitive and effortless interfaces, Krug advocates for minimizing users’ cognitive effort. He stresses the importance of clear labeling, simple navigation, and the removal of unnecessary distractions in digital experiences.
Krug’s principles remain pertinent in today’s ever-evolving AI design landscape: clarity and clear interface design are as important as ever. AI tools are new to everyone – including those designing them. The sooner we can get to common design patterns that reduce the ambiguity of what AI tools can offer, the sooner we’ll be in a place where users won’t have to think quite as much to get the outcomes they are looking for.
Laws of UX
Many of the principles outlined in Krug’s book follow the classic laws of UX – a series of guidelines backed by psychology studies and research, which provide a framework to guide UX design and ensure that the user experience is effective and enjoyable. Of these laws there are a few in particular that seem relevant in our current evolution of the AI tool interface: Fitts’s Law, Jakob’s Law, and Hick’s Law.
The idea behind Fitt’s Law is understanding the relationship between the size and distance of a target and the time it takes to reach that target. In UX design, this suggests that larger and more easily accessible interactive elements, such as buttons or links, can enhance user efficiency and reduce errors.
The prevailing AI feature that is booming right now is the chatbot, exemplified by the pioneer and arguably most popular AI tool ChatGPT. In theory, you would expect the ChatGPT interface to crush Fitt’s Law: it’s quick and simple to get from prompt to output from a single, giant input box. But in order to gain efficiency in the task, the user is required to know exactly what they need to ask and how to ask it within a prompt. All of a sudden the “distance” to the target feels miles away.
Some things that could potentially alleviate some of this “distance” would be to incorporate UI/UX design patterns that the user is more accustomed to. Google, for instance, allows you to type in short, often cryptic words and phrases into their prompt and what you get in return is pages and pages of potential answers that you simply have to click on. This is what crushing Fitt’s Law looks like. Less typing, less thinking, and more simple clicking.
This principle suggests that users’ expectations are shaped by their past experiences with similar products. As mentioned earlier, common design patterns and conventions that make users feel comfortable and confident when interacting with a new interface should always be favored.
The problem here is that there isn’t much to pull from in past experiences with AI tools. We’re a little bit in the wild west with new ideas, conventions, and patterns emerging daily. It’s similar to the early days of the internet where there weren’t common ways to perform basic tasks like website navigation and UI/UX experimentation was rampant. The good news is that this will all eventually settle down and best practices for UX in AI will fall into place. In the meantime, all we can do is keep the best interests of our users in mind and try not to fall for incorporating trendy UI/UX tactics that may not be ready for prime time.
With Hick’s Law, the time it takes for a person to make a decision is directly proportional to the number of choices they have. In UX design, reducing cognitive load and providing clear, concise options can improve decision-making and user satisfaction.
In regards to AI tools, we run into some of the same problems we discussed in Fitt’s Law. If our interface is reliant on the quality of user prompts where the options are infinite, then we’re in trouble. Frustration will build quickly if users feel like the AI tool is not understanding them, providing answers that are off-base, too obvious, or downright false. And if the AI tool is not there to catch this frustration and offer a way back, it’s a recipe for rage-typing.
This is when it becomes really important to utilize all the tried and true UX techniques we’ve relied on for ages: journey maps, empathy maps, personas, etc. In order to craft a great experience with AI we first need to fully understand what that could look like. What are the user’s pains, gains, and jobs? What are common use cases for interacting with your tool and what do those journeys look like? What is the user feeling at each stage of the journey? And most importantly, we need to understand what good outputs look like.
Admittedly, there is too much variation in the outputs of AI to reliably craft an exact experience, but that doesn’t mean you and your team shouldn’t have a full understanding of what a good output should look like. While you can’t fully tame the AI brain, there are enough levers to guide it in the right direction.
Where do we go from here?
It’s a scary and exciting time now that AI has disrupted pretty much everything. We have so few reference points to guide us in designing new AI tools, but it’s also exciting because the possibilities are endless. The good news is that the UX practices that got us here can still help us. The laws of UX don’t only apply to UIs on screens, they are basic psychology principles that will continue to be relevant as long as there are humans interacting with technology. And if all else fails, just keep repeating Steve Krug’s timeless mantra, “Don’t make me think”.
Living in a digital age with endless information available anytime, it’s no surprise that patients increasingly rely on the internet for healthcare insights. In fact, a staggering 89 percent of patients search for their symptoms online before seeking healthcare guidance or connecting with their providers.
This significant shift in patient behavior has driven necessary innovations in healthcare delivery, with artificial intelligence-powered chatbots emerging as powerful tools to enhance patient engagement and provide timely and accurate information.
From providing quick, relevant, and legitimate responses to health questions to streamlining essential tasks, AI chatbots are revolutionizing how patients interact with healthcare providers and provide unique new opportunities for health systems to enhance operations with digital tools.
The Evolution of Healthcare Chatbots
Healthcare chatbots have come a long way from clunky early iterations. When these early generation chatbots hit the market, patients and providers often saw them as rudimentary tools with limited capabilities. While they could perform basic tasks and provide general information about healthcare facilities, technological constraints sharply limited their potential.
However, the healthcare chatbots landcape has undergone a remarkable transformation in recent years. New technological innovations have played a pivotal role in elevating these digital assistants from comparatively simple tools with limited utility to more sophisticated and valuable resources for patients to help them navigate unique and complex care journeys.
Thanks to recent innovations in natural language processing (NLP) and clinically validated large language models (LLMs), healthcare chatbot capabilities and use cases have dramatically expanded. Solutions with integrated NLP and LLM can provide general health and wellness information, deliver medical advice, and even support care team efficiency with symptom analysis and preliminary diagnoses.
NLP enables chatbots to understand and interpret human language effectively, and thanks to recent advances, allows chatbots to permit patients to engage in more intuitive and natural conversations. Instead of the rigid and scripted interactions in previous iterations, today’s chatbots can engage in dynamic and context-aware conversations as they guide users through a broad array of functions.
The incorporation of clinically validated LLMs represents a substantial leap forward for AI- powered chatbot capabilities. These large language models are trained on vast amounts of medical literature, research, and patient data. Chatbots with LLM integration can draw on this extensive and dynamic knowledge base to offer comprehensive and highly accurate responses to medical inquiries.
Conversational AI assistants still show significant promise far beyond current use cases. But in order to unlock their full potential, the healthcare industry must learn to harness LLMs safely. When used appropriately, these models enable chatbots to transcend traditional administrative tasks like scheduling, and enhance care team capabilities with personalized and medically sound responses and recommendations.
Revolutionizing Patient Engagement With Advanced AI-Driven Chatbots
In addition to recent innovations that support clinically accurate and personalized medical advice and care, these more sophisticated, AI-driven chatbots are revolutionizing patient engagement in healthcare in several ways:
1. Efficient triage and referral: Chatbots can help healthcare organizations efficiently triage patients based on the severity of their symptoms. They can identify patients who need immediate attention and those who can wait, ensuring that organizations allocate their resources effectively and patients receive timely care.
2. Streamlined appointment booking: Chatbots allow patients to schedule appointments quickly and easily with access to real-time availability information that lets patients choose suitable appointment slots while reducing scheduling conflicts and improving the overall patient experience.
3. Immediate responses to health concerns: In traditional clinical settings, patients with medical queries can usually expect to wait for answers, whether they need to schedule a visit or need advice for a minor ailment. Chatbots bridge this gap with immediate responses to patient concerns, day or night. Quick and easy access to sound information helps reduce patient anxiety and empowers them to address minor health issues promptly and prevent them from worsening.
4. Continuity of care: Healthcare chatbots can provide seamless continuity of care with automated ongoing support and follow-up. Patients with chronic conditions, for example, can benefit immensely from regular check-ins and medication reminders from intelligent chatbots. These interactions reinforce the patient’s commitment to their treatment plan and help them stay on track, and more efficient resource utilization.
5. Multilingual support: Multilingual capabilities, supported in many AI chatbots, can help bridge language barriers between patients, providers, and care partners. Patients who speak languages other than the primary language of their healthcare providers can rely on AI chatbots to effectively communicate and access the care they need.
Patient Engagement 2.0
Enhancing patient engagement through AI healthcare chatbots is not merely about technological innovations, but building a more patient-centric healthcare experience.
These chatbots offer tailored guidance, timely responses, and convenient access, all of which support a more engaged and informed patient population.
As chatbots continue to evolve, they hold the potential to transform healthcare interactions into collaborative partnerships between patients and providers—ultimately leading to better health outcomes and patient satisfaction.Raven Cobb is Vice President of Marketing & Growth at Clearstep. Clearstep’s Smart Care Routing™ AI assistants for self-triage and healthcare navigation harness rigorously validated and curated AI models that empower patients to navigate their healthcare needs securely. Clearstep’s impact extends across call centers, websites, mobile apps, patient portals, and 2-way SMS platforms. Through these mediums, it seamlessly automates symptom assessments, streamlines triage processes, addresses routine patient inquiries, facilitates remote patient monitoring, and optimizes administrative and clinical workflows.
Selecting Digital Therapeutics: A Step-by-Step Framework for Health Systems
Digital therapeutics represent an exciting opportunity for health systems to improve patient engagement, outcomes, and access to care. These software-based solutions use technology like mobile apps, sensors, and data analytics to help prevent, manage, and treat a growing range of medical conditions. However, with an influx of companies offering digital therapeutics, health systems face challenges in identifying the right solutions that are evidence-based, integrate smoothly, and align with organizational goals.
This article provides a step-by-step framework health systems can follow to evaluate and select digital therapeutics for their needs. We’ll cover:
- Assessing organizational needs and goals
- Evaluating clinical evidence and outcomes
- Examining integration requirements
- Analyzing patient experience design
- Reviewing costs and potential value
- Selecting the right vendor
Following a methodical selection process will help ensure health systems choose digital therapeutics that are impactful, scalable, and set up for long-term success across the organization.
Assessing Organizational Needs and Goals
The first step in any digital therapeutic evaluation should involve clearly defining the problems you need to solve or goals you hope to achieve. Bring together stakeholders from different areas of your organization to get a multidisciplinary perspective. Engage leaders and users from clinical departments, IT, administration, patient experience, population health, and other areas in the process early on.
Key activities in this phase include:
- Documenting challenges related to patient engagement, outcomes, costs, or staff efficiency for specific conditions. Be detailed in defining issues.
- Brainstorming opportunities where digital therapeutics could have the biggest impact based on organizational priorities and pain points.
- Prioritizing the top focus areas or use cases to evaluate potential solutions for.
- Developing clearly-defined objectives a digital therapeutic should meet like improving A1C levels by 0.5% for diabetes patients.
This stakeholder engagement and planning stage is essential to align on the biggest needs to address with digital therapeutics and set criteria to measure success against from the start.
Evaluating Clinical Evidence and Outcomes
Once target use cases are defined, the next element of evaluating digital therapeutics focuses on clinical evidence and real-world outcomes data. The level of evidence and proven results will vary widely between the multitude of solutions on the market. Thoroughly vetting this can determine whether a solution has the intended therapeutic benefits and safety.
Key questions to ask vendors and consider regarding clinical evidence include:
- Has the digital therapeutic been clinically tested in randomized controlled trials to support claims of outcomes?
- What measurable clinical impacts on specific health metrics has it achieved in trials?
- Are other health systems using this vendor?
- Are outcomes validated not just in controlled studies but also in real-world use in other healthcare settings?
The FDA has an emerging approval process for digital therapeutics as software as a device which can serve as a marker of quality. However, not all solutions will have formal FDA approval yet; therefore, published outcomes data are essential.
Examining Integration Requirements
Before selecting a digital therapeutic, it’s critical to determine how well it will integrate with your existing health IT ecosystem. Meeting technical and operational requirements ensures sustainable workflows. Key integration factors to examine include:
Technical and Operational Integration
- Can the solution integrate with your electronic health record system? Is patient data, progress, and insights captured in a dashboard accessible to clinicians in their workflows?
- How much training and support is required for clinicians, care teams, and patients to adopt the digital therapeutic?
- When customers interact with the solution, is there an escalation path for received messages?
- What are the requirements for supporting, updating, and maintaining the solution long-term?
- How much training and support is required for clinicians, care teams, and patients to adopt the digital therapeutic?
- Are additional staff like health coaches or nurse educators required to manage the solution?
- What are the requirements for supporting, updating, and maintaining the solution long-term?
- Can the solution easily scale to serve large volumes of patients across the health system now and in the future?
- Is the digital therapeutic vendor currently serving large health systems comparable to your scale?
It’s important to thoroughly test integration via demos, pilot programs to uncover any gaps before full deployment.
Evaluating Patient Experience Design
While digital therapeutics must be clinically validated, the solutions also need to deliver an engaging user experience to drive adoption and sustained utilization. Evaluate factors like:
- Personalization: Are assessments, educational content, and features tailored to individual patient needs and preferences?
- Delivery modalities: Does the solution offer a multi-modal experience via mobile, wearables, sensors, websites, virtual coaching?
- Engagement strategies: Does it use proven behavior change techniques like motivational messages, reminders, incentives or gamification?
- Accessibility: Is the program design accessible to different ages, tech-savviness, languages, and disabilities?
Testing the digital therapeutic firsthand is invaluable in evaluating the user experience quality and appeal.
Analyzing Costs and Value
Upfront software licensing costs, recurring subscriptions, device fees (if applicable), and ongoing support expenses should be considered when comparing digital therapeutic solutions. However, also analyze long-term value:
- Does the solution offer clear opportunities to improve outcomes and lower associated care costs long-term for a condition?
- Can you project cost savings from reducing hospital admissions, readmissions, ER visits for target patient groups, based on available outcomes data?
- Will the solution help meet quality incentive or shared savings goals under value-based care contracts?
- For vendors that do not have strong outcomes, explore a partnership model for product validation.
Anticipated value should be weighed against costs, keeping in mind that higher price does not always guarantee better results or value.
Selecting the Right Vendor
Developing a comprehensive vendor scoring system across the above factors will help compare solutions head-to-head and make the final selection. Key steps include:
- Start by making a list of vendors and their capabilities and integrations. You can use AVIA Marketplace’s DTx Market Map to help with that.
- Creating a scorecard incorporating all the criteria above, weighted by importance. Assign points per criterion.
- This is something AVIA develops for its members and through consulting.
- Scoring each vendor solution in your current consideration set based on how they fulfill the criteria.
- Comparing total scores to identify the technology that optimally fits your defined needs and preferences.
- Performing product demos and pilot tests with one or two top choices to confirm the right fit before fully deploying a selected digital therapeutic across the organization.
View AVIA Marketplace’s Top Digital Therapeutics Companies report for a list of the top solutions in the space.
Focusing on solutions that align with the organization’s needs will drive greater adoption and value. Part of that is appropriately vetting and scoping projects that match patient and provider goals and desires. By following a structured selection framework, health systems can evaluate options and identify the right technology that delivers demonstrable value.. While digital therapeutics require an initial investment of time and resources in the selection process, the long-term benefits of choosing the right fit makes this upfront effort well worth it.
For an in-depth discussion by AVIA digital therapeutics experts, see AVIA’s Exploring the Potential of Digital Therapeutics webinar.
Visit AVIA Marketplace ahead of your next purchasing decision for unbiased third-party information, ratings, and reviews for hundreds of the leading digital health companies and solutions.
As healthcare moves, by necessity, towards greater digitization and automation, conversational AI has emerged as the connective tissue between providers and their patients.
Increasingly, health systems are implementing automated virtual assistants to help attract patients and streamline care access and navigation. Unlike rudimentary chatbots of old, which were designed to provide only basic information without the delays associated with human engagement, today’s virtual assistants are capable of much more than programmed responses to simple questions.
Conversational AI, which leverages machine learning and natural language processing, comprehends the context and intent of human language, and parses it to deliver adaptable responses. This capability is invaluable in an industry like health care, where clinical vernacular and medical jargon differ significantly from the vocabulary that patients may use to relay information. With the ability to recall preceding statements and apply context to ongoing dialog, conversational AI can identify the needs of individual users with greater precision, delivering personalized and actionable responses. Broadly speaking, conversational AI engages patients and provides important information with efficiency and empathy.
Advances in conversational AI virtual assistants deliver tremendous value both to patients and their providers. Leading solutions can answer routine questions, support symptom triage, help patients schedule appointments, provide education, facilitate intake and procedure prep activities, and engage patients post-discharge and during follow-up, all while reducing administrative burdens for care teams and support staff.
Consider this example…
For the patient: Finding care and the right provider
Chris comes to “Memorial Health’s” website, looking for information and care guidance because he is experiencing shortness of breath. He enters this information into the digital virtual assistant’s symptom checker. The conversational AI-powered assistant requests additional details: Chris’s age, the duration and severity of his symptoms, his medical history, and so on. The solution allows him to use everyday language to describe his symptoms, so it’s unlikely that Chris will become frustrated with obscure medical terminology or receive inaccurate information.
Drawing from approved knowledge bases, the virtual assistant conducts a hyper-personalized dialog to help Chris decide whether to seek care right away or follow up with his PCP or a specialist the next day. Throughout the exchange, the virtual assistant also obtains important information like insurance coverage, whether preauthorization may be required, Chris’s location and proximity to care, his language preferences, and which gender of provider he is most comfortable seeing.
At the end of the dialog, conducted solely through the virtual assistant, Chris will have more information about his symptoms and clinically sound guidance about available providers and care settings. He can take the action that is appropriate for him and, if necessary, schedule appointments directly through the virtual assistant.
It is worth noting that, not only is a virtual conversational AI experience more efficient and effective, it is also the preferred method for today’s patient. Research published in 2022 by ModMed found that 68% of patients get frustrated when they call and have to wait to be called back. In addition 67% say they are more likely to use chat over calling to make appointments or request lab results.
For the health system: Alleviating call volume and improving the employee experience
More than ever, hospitals and health systems struggle to balance the volume of inbound queries from patients against a workforce that is overextended and burned out.
Not only does Chris’s story illustrate how conversational AI streamlined his healthcare experience, but it also underscores how a virtual assistant can improve efficiency, employee performance and job satisfaction. Consider what did not occur, from the provider’s perspective:
- Call center operations did not have to answer Chris’s inquiry about his symptoms. This reduced the daily call volume, shortened the queue for other patients and eliminated the possibility that Chris would be placed on a lengthy hold or that his call would be dropped altogether. Staff members could instead prioritize calls from patients who truly needed human assistance.
- Chris did not visit a higher-cost setting of care unnecessarily or without the essential insurance coverage. This, too, helped ease the burden on both clinical and administrative staff, who did not need to explain bottlenecks to Chris, phone insurance companies, or juggle overextended resources in a busy care setting.
- The scheduling staff did not need to engage in a frustrating and time-consuming game of telephone tag with Chris while trying to reach him during business hours (which is when Chris is also at work). Instead, he could schedule his appointment on his own time, asynchronously, and conveniently.
The principles and promise of conversational AI virtual assistants can also be applied to unique challenges for specific practices or service lines. For example, consider the potential time and effort savings an AI virtual assistant delivers in guiding patients through surgical prerequisites and the procedure fully compliant and prepared. Patients receive all the necessary instructions and reminders, which frees up staff time, reduces preventable scheduling issues, and increases volume and throughput.
Likewise, conversational AI virtual assistants can be used at the enterprise level, triggering dialogs with post-discharge patients to monitor recovery and identify potential complications. These frequent touchpoints help improve outcomes and reduce avoidable readmissions while freeing up staff to connect one-on-one with patients who require additional intervention.
There is no doubt that virtual assistants will play an increasingly important role in successful hospital operations as technology and use cases continue to evolve. Conversational AI offers serious value for both patient and the provider and delivers scalable functionality that can address specific and unique challenges across the healthcare enterprise.
Patty Riskind is CEO of Orbita. Orbita uses conversational AI, generative AI and machine learning to automate both administrative and clinical workflows to make navigating healthcare easier. Patty was previously Head of Global Healthcare for Qualtrics, an experience management technology company. Prior to Qualtrics, she was CXO at Press Ganey, and before Press Ganey, she founded the first e-survey company in healthcare, PatientImpact, that Press Ganey later acquired.
Patty has served on several venture-backed private boards as well as a public (non-physician) board member of the Accreditation Council for Graduate Medical Education (ACGME) on its compensation, audit, and finance committees. She earned her Bachelor of Arts degree with honors from Brown University and her MBA from the Kellogg School of Management at Northwestern University.
The combined impact of deep learning, large training datasets, increases in computing power, and new model architectures has led to a step change in the performance of Conversational Artificial Intelligence (AI). This technology will transform how healthcare providers interact with their patients, health systems manage their operations, and patients navigate their healthcare needs.
Impact for healthcare providers
For healthcare providers, conversational AI could have a significant impact on operational efficiency. AI has historically excelled at handling high volume routine tasks that consume significant staff time and resources: appointment scheduling, prescription refills, department and provider routing, for example. Conversational AI will not only improve the performance of handling these tasks but will support healthcare providers in addressing the long tail of use cases that are individually less frequent but in aggregate consume time. By allowing healthcare professionals to focus on more complex and urgent tasks that demand their expertise and experience, adopting conversational AI can also improve job satisfaction as healthcare professionals can devote their time to more meaningful and engaging patient interactions.
Moreover, conversational AI can enhance the creation of clinical documentation. Documentation tools can transcribe and digitize patient-provider conversations, leading to more thorough and accurate medical records while reducing provider burden. Whether it’s drafting a patient visit note, a reply in a provider’s inbox, or a handover summary to support a shift change, conversational AI can not only ensure a complete record, but allows clinicians to focus on patient care, not just documentation.
Impact for patients
For patients, conversational AI will offer unprecedented levels of personalization and accessibility. EHR-integrated AI systems can provide personalized health advice and reminders based on a patient’s medical history and current health status. This is particularly beneficial for patients managing chronic diseases, for whom continual monitoring and adherence to treatment plans are crucial for optimal health outcomes. Hallucination remains a risk for large language models (LLMs) but coupling the semantic understanding of these models with clinically validated knowledge sets would allow patients to ask and get answers to questions whenever they need – before a procedure, after discharge, or just managing their health. This will improve patient understanding of their condition and care plans while reducing the burden on clinicians.
Embracing the Future of Healthcare with Conversational AI
These improvements from conversational AI in healthcare provider efficiency, staff satisfaction, documentation, and personalized information will ultimately serve to improve patient care and patient access. These tools are available 24/7/365 and can scale to match demand in real time – whether it’s a public health emergency or just another busy Monday. It is an exciting moment in the development of AI and healthcare with incredible potential for clinicians and the patients they serve.
Carter Dunn is Chief Product Officer at Syllable. Syllable is a leading provider of healthcare contact center and medical practice automation solutions using conversational AI. Syllable’s product, the Patient Assistant, is used by both hospitals and practices to intelligently route calls more efficiently and provide for automated transactions like appointment scheduling and prescription refill on the phone.
The entry of conversational AI into healthcare applications has augmented the benefits of modern medicine while simultaneously amplifying risks. Advanced chatbots can offer immediate medical advice, help patients schedule appointments, and even assist with mental health monitoring. While these applications promise greater efficiency and a more personalized patient experience, they also introduce another layer of complexity to the already convoluted world of data security.
With conversational AI involved, legal frameworks like HIPAA and GDPR still apply, but involve added complexity and dimensions. Within legal frameworks like HIPAA and GDPR, conversational AI raises new questions about patient privacy and compliance. For instance, can a chatbot be HIPAA-compliant? How do we ensure that the data processed through conversational interfaces receive the same level of security as traditional electronic health records? Legal authorities and legislative bodies are starting to tackle these issues, but healthcare providers must stay ahead of the curve to ethically utilize these tools and ensure legal compliance.
Consent and Vulnerabilities
It’s well-established that patients must provide informed consent for medical procedures, but how does this translate into the realm of conversational AI? When a patient interacts with an AI chatbot, to what extent do they understand that their data might be stored, analyzed, or shared? Transparency isn’t just about a list of terms and conditions that most users ignore–it’s about making sure patients fully understand how their data will be used, stored, and protected.
Conversational AI interfaces are also susceptible to new forms of cyber-attacks. Language processing algorithms can be tricked, confused, or exploited in ways that traditional databases cannot. ‘Adversarial attacks’ in natural language processing are an emerging concern, during which attackers manipulate input text to deceive the AI model, with consequences that include false advice or unauthorized data access.
When conversational AI systems interface with Electronic Health Records (EHR) systems, patient management systems, or other healthcare databases, the potential points of failure multiply. Ensuring end-to-end encryption and robust access control measures becomes not just advisable, but indispensable.
The ease and accessibility of conversational AI could also create a false sense of security for patients. They may casually share sensitive information and not fully appreciate the data risks involved. Healthcare providers and AI developers need to build systems that consistently inform users about the security measures in place and the limits of those measures.
The Future is Here
In an era of unprecedented technological innovation, the healthcare industry can’t afford to be reactive when it comes to data security. As conversational AI continues to mature into an integral part of healthcare, the imperative to protect patient data grows stronger.
Conversational AI holds the potential to revolutionize patient engagement and healthcare accessibility. However, if we’re careless about data security, we risk undermining not just the technological advancements but the very foundations of trust and ethical responsibility upon which healthcare rests.
As we navigate this uncharted territory, a comprehensive, forward-looking approach to data security isn’t just advisable, it’s an ethical and legal mandate. From chatbots to predictive algorithms, as we usher in a new age of AI-driven healthcare, the commitment to patient data security must remain unwavering. After all, in healthcare, trust isn’t just earned–it’s prescribed.
Redgee Capili is VP Information Technology at Syllable. Syllable is a leading provider of healthcare contact center and medical practice automation solutions using conversational AI. Syllable’s product, the Patient Assistant, is used by both hospitals and practices to intelligently route calls more efficiently and provide for automated transactions like appointment scheduling and prescription refill on the phone.
The Fullstack Employee
In the evolving landscape of AI-driven workplaces, the “Fullstack Employee” emerges as a versatile professional, proficient across organizational roles, thanks to a continuous learning mindset and AI tools.
By Zak Randall
My colleague who leads engineering emphasizes the value of full-stack developers and their ability to navigate both front-end and back-end development, allowing our lean team to punch well above our weight class. And we have a high-performing team across the board. The attributes we value in our developers – intelligence, motivation, adaptability, and a hunger for learning – are shared by everyone, from our data scientists to our marketing experts.
Yet recently, the introduction of AI tools has amplified our team’s capabilities. Colleagues are venturing into areas previously outside their expertise. Data scientists are exploring marketing, while marketers are diving into data analysis. This evolution led to the term the “Fullstack Employee.”
The idea of a fullstack developer is now common parlance. However, what we’re witnessing is the rise of a different beast altogether, one that transcends the conventional understanding of roles and team boundaries. The Fullstack Employee isn’t just a professional skilled in a specific department, they’re proficient across the entire organizational landscape, thanks to their continuous learning mindset and the powerful toolkit AI technologies provide. This shift isn’t just reshaping our team—it’s redefining the entire work environment as we know it.
AI: The Core of the Fullstack Employee Trend
Our Chief of Technology for AVIA AI Labs, Aaron Diestelkamp, aptly observes, “AI is not just a tool, but a transformative force that’s redefining the way we work.” Amidst this tidal wave of AI-driven change, the professional sphere is undergoing a significant metamorphosis. The birth of what we dub the “Fullstack Employee” marks this evolution – professionals who, empowered by AI, can seamlessly traverse different functions and roles within an organization. Said differently, the Fullstack Employee is not an employee who does everything but one who is empowered to do anything.
As we delve deeper into the AI-driven landscape, we encounter what’s being termed the “Jagged Frontier.” This metaphorical boundary, as Ethan Mollick, professor at the Wharton School of the University of Pennsylvania describes, represents the unpredictable capabilities of AI. On one side, AI excels, while on the other, it falters. The challenge lies in discerning where this invisible boundary exists, as tasks that seem equally complex might be on opposite sides of this frontier.
To navigate this unpredictable landscape, Mollick and co-authors suggest that professionals adopt one of two approaches: becoming a Centaur or a Cyborg. Centaurs maintain a clear division between human and machine tasks, strategically delegating based on strengths. Cyborgs, on the other hand, deeply integrate their efforts with AI, moving fluidly across the jagged frontier, blending human intuition with machine intelligence.
These approaches empower the Fullstack Employee to harness AI’s strengths while mitigating its weaknesses, and which one the Fullstack Employee chooses may depend on the task. The Fullstack Employee leverages AI to navigate tasks that require the nuances of human judgment, creativity, and insight, while also executing tasks that demand deep, specific knowledge.
4 Advantages of a Fullstack Employee
When it comes to the Fullstack Employee, the advantages are manifold. Here are some key ones:
1. Greater Productivity and Efficiency
One of the most compelling arguments for embracing the Fullstack Employee model is the significant boost in productivity and efficiency it can offer. With the power of AI tools, Fullstack Employees can offload a significant number of routine tasks. This automation provides them with more bandwidth to focus on tasks that require a higher level of expertise, creativity, or strategic thought. Where we’ve applied this model within AVIA Marketplace, our team isn’t spending their time on monotonous paperwork or performing repetitive tasks. They’re on the front lines, grappling with complex problems and spearheading our drive towards innovation. They can toggle between SEO, Bayesian Statistics, User Growth, and Journey Mapping because they have tools to support them in the work. This paradigm shift creates a work environment where both the employees and the company reap benefits.
2. Flexibility and Adaptability
Fullstack Employees, by nature, are agile and adaptable. Their cross-disciplinary skills allow them to pivot to new roles and responsibilities as required, enabling organizations to be more resilient in an ever-changing business environment. This adaptability of our employees was highlighted during the industry-wide shift to remote work amidst the pandemic, rapidly adjusting to changes in workflows and swiftly adopting new communication tools. A move toward Fullstack Employees recognizes this shift – elevating our team members by acknowledging their multifaceted skills, reinforcing their value within the organization, and empowering them to drive progress. Instead of being siloed within a single function, Fullstack Employees can fluidly navigate between roles, leveraging their diverse skill set to solve problems from various angles.
3. Increased Creativity and Innovation
Exposure to different fields and processes can inspire innovative solutions and ideas. A Fullstack Employee working in marketing who understands data science, for example, might leverage that knowledge to develop a groundbreaking marketing strategy. Ultimately, this way of working fosters a culture of continuous learning and cross-pollination of ideas, stimulating innovation and growth. The move towards Fullstack Employees amplifies each person’s unique potential by equipping them with advanced AI tools, effectively enabling them to scale their capabilities and impact. Thus, a Fullstack Employee is not just a response to a shifting business environment, but a proactive strategy to stay ahead of the curve and harness the full power of our collective talent.
4. Reduced Dependency
As the saying goes, ‘don’t put all your eggs in one basket.’ This is particularly pertinent in a business setting. By fostering a team of Fullstack Employees powered by AI, businesses can break down information silos. Rather than having a few key personnel who hold a significant chunk of critical knowledge or skills, fostering Fullstack Employees helps disseminate this knowledge across the organization..This approach promotes a more robust framework for business continuity and growth, mitigating the risks associated with turnover or unavailability of key personnel. This is not about replacing specialists, but rather about creating a buffer, a safeguard against unforeseen circumstances that might otherwise put a dent in productivity.
Navigating the Challenges of the Fullstack Employee Paradigm
While every new direction entails challenges, the rise of the Fullstack Employee is not exempt. We must navigate the accompanying obstacles with a clear view of the opportunities and potential pitfalls. A paramount consideration in the march towards Fullstack Employees is avoiding the trap of excessive expectations. We must be cautious that in our quest to create versatile employees, we don’t unintentionally overwhelm them. Managing workloads, setting realistic goals, and preserving a healthy work-life balance are crucial to prevent burnout and maintain high morale and productivity.
Moreover, in championing the cause of generalists, we must not neglect the invaluable contribution of specialists. There are areas where a deep well of expertise remains indispensable. For example, while AI-powered tools can assist in diagnosing health conditions, the importance of a highly trained physician’s judgment and experience in complex medical cases cannot be understated. Similarly, in the legal sector, although AI can help sift through cases and legal documents, nuanced interpretation of law and court proceedings still necessitates seasoned legal professionals.
As we embrace AI’s transformative capabilities, we must all be wary of the seductive allure to overreach. A phenomenon Mollick and team observed, akin to “falling asleep at the wheel,” emerges when professionals become overly reliant on AI. This over-dependence can lead to a form of complacency, where individuals, trusting the AI’s output implicitly, fail to critically assess or even notice its mistakes or shortcomings. It’s a subtle trap, one where the very tool designed to enhance productivity and innovation can inadvertently stifle human intuition and judgment.
This challenge underscores the importance of the Centaur and Cyborg approaches. Centaurs, with their clear demarcation between human and machine tasks, ensure that they remain engaged and critical, leveraging AI where it’s most effective but relying on human expertise where AI might falter. Cyborgs, with their integrated approach, maintain a continuous dialogue with AI, ensuring that they’re always in tune with its outputs and can quickly identify and rectify any discrepancies.
As Fullstack Employees, it’s essential to recognize that while AI is a powerful ally, it’s not infallible. By adopting the Centaur or Cyborg mindset, we can ensure that we harness the best of both worlds, combining AI’s computational prowess with human creativity, intuition, and judgment.
A final area of caution is the risk of underestimating the transformative power of AI. Often, there’s a tendency to downplay AI’s potential impact on our work – either from disbelief or misunderstanding. While it is true that AI is not about to replace humans, the augmentation it brings to various roles can’t be ignored. Advancements in AI tools mean that generalists equipped with these technologies can produce results that more closely approximate those of specialists than one might expect. For instance, a generalist leveraging AI tools might not match a seasoned data scientist’s capabilities in developing complex predictive models, but they could quite proficiently manage a range of analytic tasks that were previously the exclusive domain of specialists.
Striking the right balance between generalists and specialists, between human judgment and AI capabilities, and between transformation and stability is pivotal. Only then can we fully unlock the potential of the Fullstack Employee model and thrive in this exciting new paradigm.
Looking Ahead: A Necessity, Not a Novelty
We’re just scratching the surface of what AI can do for us. As AI and automation technologies continue to advance, the prominence of the Fullstack Employee is set to rise. This shift represents an exciting opportunity for employees to grow their skills and versatility and for businesses to maximize their internal capabilities.
The emergence of the Fullstack Employee, driven by AI, heralds a significant shift in the workforce dynamic. As the Jagged Frontier of AI continues to advance, the Fullstack Employee’s role becomes even more pivotal. They can stay ahead, ensuring they harness AI’s potential while remaining vigilant of its limitations. The future of work is not just about AI; it’s about how we, as Fullstack Employees, navigate this evolving landscape.
What is the role of digital within oncology?
In recent years, digital has evolved into its own distinct asset class for healthcare organizations of any size, and is a necessary strategic pillar in any long-term strategy. For an especially complex service line like oncology, organizations that prioritize digital are uniquely positioned to meet increased needs for exploration and education, support patients and their caregivers through lengthy and stressful care journeys, and empower care teams to spend more time engaged in direct patient care. Digital solutions can relieve staffing shortages, offer new services, create workflow efficiencies, expand access and help realize downstream revenue.
Stakeholders' needs vary, but digital can improve experience for all
Oncology-focused solutions exist within a broad framework and support patients, caregivers, and care teams through screening, diagnosis, treatment, second opinions and referrals, and ongoing support and coordination. Some example areas include:
For caregivers • Education
• Caregiver skills training
• Community resources
• Coping resources
• Emotional and grief counseling
For patients • Core patient capabilities (transactions and interactions)
• Whole person care (social, financial, emotional, and spiritual needs)
• Clinical trial and second opinion navigation
• Treatment support
• Survivorship support
For care teams • Care coordination
• Vital sign surveillance/remote monitoring
• Virtual tumor boards
• Patient navigation
• Clinical decision support
• Care team communication
• Clinical and quality analytics
The case for digital within oncology
Responding to business trendsCancer incidence is rising, but so are overall survival rates–compared to a 10 percent projected five-year increase across all tumor sites, overall cancer death rates have decreased by 32 percent. As cancer care continues to evolve, investments in digital oncology solutions have also grown–in one noteworthy investor survey, more than half of participants said that oncology was the most promising clinical area for startups.1 With the advancement of technology, cancer care is changing to become more proactive, thus increasing survival rates. More health systems and cancer centers are leveraging digital tools to meet consumer expectations, deliver a personalized, whole-person experience that includes the convenience that patients and caregivers want, accommodates pressure from payers to curb spending, and responds to competition from non-traditional cancer care providers.
Creating positive digital experiences for all
A positive digital experience is a key predictor of an enhanced care experience overall for nearly 40 percent of patients, and half of consumers agree that bad digital experiences detract from the overall patient experience. Health systems and cancer centers that leverage the appropriate digital capabilities are better equipped to deliver the convenient and personalized experience that consumers expect while supporting all stakeholders–patients, caregivers, and care teams–through the cancer treatment journey.
While stakeholder needs vary widely, digital tools can deliver a variety of advantages that improve the experience for all:
Caregivers • Consistent and up-to-date information on patient condition and treatment progress
• Tools and information to help them advocate for patients who are unable to manage their own care
• Information about community or peer group support
Patients • Navigational support for complex clinical and financial journeys
• Real-time access to care teams and ongoing symptom management
• Access to holistic care and community resources
Care teams • Effective coordination and communication between care team members
• Enhanced relationships with referring providers
• Reduced administrative burden
Addressing equity challenges
Equity remains a significant challenge. Early screening often misses the country’s uninsured population as they visit the doctor infrequently to avoid expensive bills. Additionally, patients who do not live in urban centers still lack access to advanced testing. Moreover, many low-income Americans live in ZIP codes that lack accessible healthy food options, increasing their risk of developing obesity and physical inactivity-related cancers.
Health systems can leverage digital capabilities to extend their reach to these traditionally underserved populations. Digital health companies are developing culturally and linguistically tailored platforms that accommodate patients with varying digital literacy levels, extend hours of availability through virtual channels, and investing in tools that address social determinants and close care gaps.
Opportunities for digital in oncologyPatient:
Whole person care
- Digital capabilities to address the patient’s physical, emotional, social and financial needs that play a critical role in reducing anxiety, improving outcomes, and minimizing disruptions to daily life. For example, digital offers the ability to deliver behavioral health care and support a patients’ spiritual coping.
- Key example sub-capabilities: financial navigation, social needs, spirituality and mindfulness, behavioral health resources
Core patient capabilities
- Digital offers the ability to create a more seamless and personalized experience for patients to engage in administrative transactions and clinical interactions.
- Key example sub-capabilities: provider search and match, appointment coordination, virtual visits and remote monitoring, patient payments
- Digital capabilities to ease access and decision making. For example, stakeholders can more easily connect with leading experts for second opinions and expand patient access to clinical trials.
- Key example sub-capabilities: virtual/video second opinion consultations, simplified scheduling, records and specimen sharing, digital concierge services
- Digital capabilities to improve patient experience, adherence and outcomes during treatment. For example, providers can supplement care with symptom management between clinic visits, efficiently manage complex care plans, and engage in shared decision-making with patients and their loved ones.
- Key example sub-capabilities: virtual check-ins, reminders and notifications, patient education, care coordination documentation, metrics collection, actionable insights, population-level reports and analytics
- Digital capabilities for ongoing support and coordination for example. providers can leverage digital to develop effective programs to support the needs of survivors, while survivors can connect with others and enjoy a supportive community.
- Key example sub-capabilities: follow-up care planning and data collection tools, survivor discussion boards and chat rooms
- Digital capabilities allow caregivers to meaningfully and actively participate in the cancer care journey.
- Key sub-capabilities: Caregiver skills training, education, community resources, coping resources, emotional and grief counseling
- Digital capabilities to optimize care team workflows and improve care team experience
- Key sub-capabilities: Care coordination, remote monitoring, virtual tumor boards, clinical decision support, clinical and quality analytics, care team communication
Organizing for digital oncology successSuccessful digital strategy depends on clear priorities and an enterprise-level commitment to “being” digital instead of simply “doing” digital.
- An enterprise strategy that leverages digital to enable all elements.
- Strong alignment and accountability across all senior leadership.
- Governance structure that supports sufficient staffing and skill mix.
- Meaningful resources budgeted for digital each year.
- Aggressive top-down goals and KPIs.
- Gaining a clear picture of expected patient demand and projected growth.
- Examining available provider capacity to accommodate virtual care offerings.
- Getting buy-in from clinicians and staff.
- Upgrading or consolidating technical infrastructure and hardware as needed.
Long-term success for health systems depends on deploying solutions that prioritize patient and provider experiences equally. With careful preparation and a methodical approach to vendor assessment and selection, health systems can increase patient satisfaction, deliver enhanced convenience, enable greater access, improve operational efficiency, and expand catchment areas through enhanced digital oncology capabilities.
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What is conversational AI?
Conversational AI is an advanced technology that allows machines to understand, process, and respond to human language in a way that is both contextually relevant and interactive.
Within the healthcare sector, this technology is increasingly deployed to automate routine tasks and deliver a more robust and personalized patient experience. Conversational AI leverages a combination of machine learning, natural language processing, and occasionally deep learning to interpret and respond to user inputs, with a conversational user interface that mimics human interaction. Digital health companies deploy conversational AI through chatbots, voice assistants, messaging apps, or any other type of platform where natural language interactions occur.
Conversational AI solution types
Pre-care interaction Solves for important pre-care interactions including symptom search, appointment scheduling, and health information. Care process Supports symptom checking, patient education, and communication between patients and care providers. Post-care management Promotes effective post-care management with follow-up appointment scheduling, reminders, and care plan adherence support. Enduring relationship Provides ongoing care plan education, monitors symptoms, manages care gaps, and delivers patient surveys.
The case for conversational AI
Demand frequently outstrips available resources within the healthcare sector. A 2022 Beryl Institute poll found that 76 percent of Americans had not had a positive healthcare experience during the previous three months, and 60 percent of Americans had negative experiences. And according to a report from digital health company Cedar, 28 percent of patients had changed providers during the previous year due to poor digital health experiences.
Conversational AI offers opportunities to dramatically improve low satisfaction rates and reduce negative experiences through timely and personalized patient interactions. These real-time tailored conversations can assist patients throughout their care journeys, from pre-care to follow-ups and ongoing engagement, significantly improving the patient experience. In addition, automated tasks reduce workload on care teams and help health systems scale their services more effectively.
How conversational AI can drive value
Improved patient acquisition • Helps patients access information on demand
• Provides personalized recommendations and content to meet their needs
• Rapidly scales to accommodate new patients in a cost-effective manner
Reduced cost to serve • Automated interactions can decrease labor costs related to patient questions and messages
• Algorithms provide cost-effective continuous improvement
Reduced churn • Supports patients with personalized interactions on demand Improved employee satisfaction • Automates routine tasks and relieves care team burdens
• Delivers intelligent support for clinical interactions
Increased per-patient revenue • Continuously gathers information and analyzes patient preferences to refine interactions and recommendations
Key attributes of conversational AI solutions
Health systems should prioritize these capabilities when they invest in conversational AI solutions:
Contextual understanding: The solution should understand and respond to patient inputs in a manner that takes the context into account, providing relevant and accurate information or advice.
Interoperability: Conversational AI solutions should seamlessly integrate with the EHR and other existing systems, databases, and digital channels.
Data security and privacy: Solutions must protect patient privacy and comply with HIPAA and all other applicable regulations.
Naturalistic interaction: The user experience should mimic human conversation and include an easy, intuitive interface.
Scalability: The solution should be capable of handling increasing volumes of interactions as use cases expand and adoption grows.
Adaptability: A good solution should keep pace with rapidly evolving AI technology and continuously improve over time.
Organizing for success with conversational AI
What health systems should consider as they assess their needs and investigate conversational AI solutions:
- Leadership buy-in. Leadership across the enterprise should understand the value of AI and the importance of investing in the right technology and driving a patient-focused culture.
- Cross-functional teams: Successful incorporation of conversational AI into healthcare operations often requires a cross-functional team of IT professionals, healthcare providers, and data scientists or AI specialists (whenever possible).
- Training and change management: As with any new technology, there will be a learning curve. Successful adoption depends on adequate training and support for staff to manage the change effectively.
- Integration strategy: Small projects or pilots can help health systems learn, adapt, and scale, delivering valuable insights to aid with robust implementation strategies.
- Patient engagement: At its heart, conversational AI is a tool to enhance the patient experience. Regularly gathering patient feedback and making necessary adjustments is key to ensure the technology meets their evolving needs.
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What is digitally-assisted provider documentation?
Digitally-assisted provider documentation solutions leverage artificial intelligence (AI) and other advanced technologies to deliver real-time support during the documentation process in order to reduce care team burden, improve clinical documentation quality at the point of care, avoid common deficiencies, and ensure accurate coding.
The vendor landscape encompasses a broad array of solutions with varying levels of human involvement, from traditional in-person scribes to manually document encounters to fully automated AI-driven documentation solutions. Vendors and solutions exist within four overlapping sub-categories:
- Traditional in-person scribe services. These companies provide medical scribes to document clinical encounters, transcribe dictation, and assist with patient throughput. Providers can easily customize documentation to their preferences.
- Virtual/remote scribe solutions. Virtual solutions securely connect providers with remote scribes during clinical encounters to complete EHR documentation in real time.
- Tech-Enabled Human (HITL). Human in the loop (HITL) solutions assist users with tools such as real-time clinical support, natural language processing, and ambient clinical documentation. Providers initiate encounters and some customization options may be available.
- Intelligent Documentation (HOTL). Human out of the loop (HOTL) solutions capture clinical conversations to generate real-time notes within the EHR. HOTL solutions are entirely tech-driven, with no human involvement and minimal or no customization options.
The case for digitally-assisted provider documentationPhysician burnout is a serious problem, and EHR documentation deserves a large chunk of the blame.1 One 2016 study found that physicians across multiple specialties spent 37 percent of each patient visit on EHR tasks, with an additional two hours devoted to EHR tasks each evening.2 Another study estimated that U.S. physicians spent approximately 125 million hours on documentation outside of normal office hours in 2019.3 This heavy documentation burden is also linked to increased errors, less time for meaningful interactions with patients, and job dissatisfaction.4
While human scribes can improve efficiency and make provider workloads more manageable, the traditional on-site scribe is increasingly viewed as a human band-aid for a larger informatics problem. And for some organizations, the disadvantages of human scribes (which include frequent turnover, higher labor costs compared to digital, and widely variable skills) outweigh the benefits.
Digital health companies have placed their bets squarely on AI as the most viable path forward. Even the most sophisticated tech-enabled human (HITL) solutions have required some level of human-led quality assurance, which incurred additional turnaround time and forced providers to complete documentation outside of clinic time. But with recent AI breakthroughs, the most advanced intelligent documentation solutions bypass human involvement in the documentation process and decrease average turnaround time–usually four to 24 hours–down to about 10 seconds.
Value for investmentAverage annual costs vary and account for scribe or solution-related fees and physician labor costs related to usage.5
Traditional in-person scribes Virtual/remote scribes Tech-enabled humans Intelligent documentation Annual scribe/solution cost $40,000 $35,000 $20,000 $10,000 Provider time (estimated value)* $15,000 $15,000 $22,000 $29,000 Total investment $55,000 $50,000 $42,000 $39,000 Reduction in documentation burden (est.) 90% 80% 60% 50%
*Approximate value of provider time for each solutionThese investments are modest compared to the costs associated with physician burnout and turnover. Physician recruitment and training costs can range from $250,000 to $1 million,6 depending on specialty, and physician vacancies can incur revenue losses of $130,000 to $150,000 per month.7 Physician burnout can also lower productivity and is associated with more errors. On top of the potential cost control benefits, digitally-assisted provider documentation solutions can drive additional revenue and provide necessary support to the human workforce.
Financial gains • Improved physician productivity: Approximately $30,000 per year for a single additional patient appointment each day (assuming $125 reimbursement)
• Reduced medical coding expenses
• Quicker and more accurate reimbursements
Non-financial gains • Decreased documentation burden
• Less after-hours “pajama time”
• Improved patient experience
• Improved access with incremental visits
Key attributes of digitally-assisted documentation solutions
New and future digitally-assisted documentation solutions will continue to leverage AI and reduce provider burden to the greatest possible extent. The best solutions digital solutions will include:
Workflow enhancement: Solutions should streamline and simplify clinical and coding workflows while improving documentation quality.
Coding recommendations: Automated coding tools should generate codes directly from clinical documentation, provide real-time guidance, and flag inconsistencies.
Diagnosis recommendations: Solutions should reduce physician cognitive load with real-time clinical decision support.
Ordering and referrals: Intelligent ordering and referral input tools within the streamlined workflow.
Patient education: Real-time patient education recommendations during clinical conversations and simplified ordering/assignment.
Organizing for success with digitally-assisted provider documentation
What health systems should consider as they assess their needs and investigate digital documentation solutions:
- Determine an appropriate strategy for the enterprise: a single one size fits all solution for all providers, or a hybrid/platform approach with multiple digital documentation support modalities to support individual providers and specialties.
- Carefully design pilots to understand impact and workflow, and test the validity of the business case. Pilots should be targeted to provider groups that will demonstrate solution impact compared to baseline.
- Identify provider expectations for documentation support, such as work relative value units, encounter close rate, patient satisfaction, or other considerations.
- Build a framework for long-term success that includes scribe governance, key performance indicators, and periodic provider utilization and performance review.
5 Assumptions: $1.50 physician labor cost per minute, 20 patients per day for 48 working weeks, 30 scribe hours per week