Wednesday, November 6, 2024

Determining the cost per lead and the lifetime value of patients

"Half the money I spend on advertising is wasted; the trouble is I don't know which half," John Wanamaker, early marketing pioneer.


Marketing your practice is an effective method of attracting new patients and retaining existing patients. It is important to find metrics and methods for measuring the effectiveness of your marketing efforts.

It is necessary to have metrics for gauging the effectiveness of your marketing efforts. Two helpful metrics for monitoring your marketing are the cost per lead (CPL) and the lifetime value of a patient (LTV).


Calculating the cost per lead (CPL)


The cost per lead (CPL) is how much it costs to attract the attention of a potential new patient to your practice. When you know your CPL, you can decide where to put your marketing dollars to maximize the value of your efforts. Understanding your CPL allows you to maximize the effectiveness of your marketing budget.


Deep six: The "gut" mentality


In the past, strategies to attract new patients were based on gut instinct when it came to understanding the results of marketing efforts. It was easy to track how much you spent, but measuring the effectiveness—as in which patients come to your practice directly from your marketing—was merely adding up the new patients entering the practice each month.

Now you can accurately track the patient CPL using market trackers. Market trackers such as LinchpinSEO.com and Hubspot.com can show if a patient is from an online ad, an email, a postcard, a newsletter, or even a billboard. These trackers can record the patient entering the practice and follow them to the end of their patient journey. Using these CPL trackers, you can know the exact patient value your marketing campaign brought in—without depending on your gut.

These trackers have calculated that the cost per lead ranges from $36 to $286, with an average of $162 per lead. These broad ranges give an idea of your practice's CPL.


CPL calculation


To find your patient CPL, it's a simple math equation:

CPL = dollars spent on marketing/number of patients who enter or contact the practice from the marketing effort

For example, if your practice sponsors a seminar and you spend $500 on a mailing list for potential patients and 50 prospective patients call for more information or, better yet, make an appointment, then your CPL is $10.

Of course, only some people who called the office made an appointment. Ten of the fifty people who called the office made an appointment to see the doctor. In this case, this patient acquisition cost (PAC) is $50. ($500\10 = $50)


Why CPL matters


Measuring your CPL provides you with valuable information. For example, if you have hired a marketing team to help grow your practice, ask them about their experience in healthcare and their CPL and PAC. You want to hire a marketing team that provides these metrics to justify your marketing expenses and to monitor their progress.

Even if your practice is doing a great job generating leads, taking the time to calculate your CPL gives you worthwhile insight into how your marketing works. Your CPL is valuable for understanding the effectiveness of your marketing strategies. It's a great indicator of what's working and what's not, helping you choose how to focus your future efforts. If you're ignoring your CPL, you're essentially taking a blind guess as to how effective your marketing is and likely sinking money into inefficient methods or ignoring the ones performing best.

Calculating the CPL of non-digital marketing, such as a direct mail campaign, takes more work, but it's still manageable. For example, a dermatologist spends $1,500 on a direct mail campaign advertising Botox injections. Through tracking the results — by training your staff to ask how patients found you or asking potential patients to bring the mailer in for a free consultation — you see that 40 people call for more information or ask for a free consultation to find out if they're a good candidate for the procedure. Dividing 1,500 by 40, you'd find that the CPL for the campaign was $37.50. You can follow a similar process to calculate the CPL for blogging and other web content, dividing your writing costs by how many leads you found via your blog.

Measuring your CPL has one other significant benefit: comparing your marketing results with industry benchmarks to learn how your results compare to those of other practices.

Remember that your CPL is just one metric to help track your success — it doesn't tell the whole story. Some leads may be more valuable, so a higher-than-average CPL might be worth marketing more expensive procedures. At the same time, some marketing methods might have a higher CPL but be more efficient at turning leads into paying patients.


Calculating the Lifetime Value of a Patient (LTV)


The calculation for LTV consists of multiplying the average value of an appointment by the average number of appointments per year by the estimated number of years a patient is likely to remain in your practice. This equation below should help you make the calculation. LTV = V x N x Y

Bottom Line: The time has arrived for those practices who desire to market and promote their practices to move from trial and error to using technology to monitor the success of marketing campaigns. Get started by measuring your CPL and the LTV of your patients. Now, you can remove a blind guess as to how effective your marketing is and likely sink money into inefficient methods or ignore the marketing efforts that are performing best. Let me conclude that these marketing metrics are only part of your efforts to attract new patients. Additionally, monitoring your online reputation and patient satisfaction scores improves the patient experience, builds long-term loyalty, and enhances your overall reputation. If you have any comments or suggestions regarding this concept, please let me hear from you.

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Tuesday, November 5, 2024

From symptom to treatment: Navigating the patient journey with AI

With health systems worldwide grappling with mounting pressures tied to workforce shortages and staff burnout, emerging technologies – specifically artificial intelligence (AI) integrated with virtual triage and care referral – are emerging as powerful tools to streamline patient care, improve outcomes, and reduce costs. As a medical advisor focused on AI applications in healthcare, I have been integrally involved in research and development that is transforming the patient journey, with a particular focus on care acuity alignment and early detection of critical conditions.

Recent research has broken new ground in two areas: care acuity alignment with respect to care de-escalation and escalation, and early detection of leading chronic diseases, including heart attack, stroke, pulmonary embolism, pneumonia, and asthma.


Aligning care with patient acuity levels


AI plays a crucial role in helping patients navigate the complex healthcare landscape, especially when they are uncertain about their care needs. AI has value for patients who are unclear about whether they need care in the first place, and if they do, what level of care is appropriate. Through virtual triage encounters, AI-powered systems can assess symptoms, generate potential diagnoses, and recommend appropriate levels of care acuity.

A recent study published in the International Journal of Healthcare evaluated the impact of AI-based virtual triage and care referral (VTCR) on patient care intent and seeking in an ambulatory setting. Researchers analyzed 8,088 online encounters to understand how VT influenced patient behavior in engaging various levels of care acuity. The technology is designed to evaluate patients’ care intentions and to help align patients with the appropriate care that is needed based on their clinical presentation. VTCR was found to reduce unnecessary in-person visits and promote virtual care among patients seeking care in a leading ambulatory care system.

Among the results:
  • A 19.1% increase in patients opting for virtual care options such as e-visits and telephone consultations.
  • 12.5% decrease in outpatient care seeking, including in-person and video consultations.
  • 35% of all patients altered their care seeking behaviors following VT recommendations, and among patients whose care intent differed from VT, 50% altered their care seeking in alignment with the recommendation of VTCR.

VTCR technology reduces patient uncertainty by providing clear guidance on whether self-care at home is sufficient, if a visit to their regular physician is necessary and sufficient, or if urgent care and emergency department attention is required. The AI explains its recommendations, helping patients understand the rationale behind the suggested care path.


Early detection of critical conditions


One of the most promising applications of AI in healthcare is its ability to facilitate early detection of potentially life-threatening conditions. Recent research has focused on five high-morbidity, high-mortality conditions: heart attack, stroke, asthma, pneumonia, and pulmonary embolism.

Findings revealed a significant disconnect between patients’ self-perceived acuity levels and the actual urgency of their conditions as determined by AI-based virtual triage. Substantial numbers of individuals did not intend before triage to seek the level of urgent care that they needed clinically.

By identifying these discrepancies, AI can prompt patients to seek appropriate care sooner, potentially saving lives and reducing long-term health consequences. For conditions like stroke and heart attack (myocardial infarction), where time is critical, early intervention facilitated by AI VTCR could mean the difference between long-term disability and a full recovery.


Enhancing clinical decision-making


For healthcare providers, AI-powered virtual triage systems offer valuable insights that can expedite and improve clinical decision-making. By providing a ranked list of potential diagnoses based on the patient’s reported symptoms, AI gives clinicians a head start in their assessment.

It saves time and helps to organize information, offering a clinical workflow advantage by potentially expediting the ordering of therapeutics and delivery of care to the patient. This efficiency not only benefits the healthcare system but also ensures patients receive timely, appropriate care.


Challenges and considerations


While the potential of AI in healthcare is vast, its implementation comes with challenges. Healthcare providers and administrators may initially approach AI with skepticism, requiring education and transparency about the technology’s capabilities and limitations. It is crucial to prioritize patient safety in AI design. For instance, some systems are built to slightly “over-triage” to emergency departments, erring on the side of caution rather than risking under-diagnosis of serious conditions.

Looking ahead, several exciting developments in AI-powered healthcare are on the horizon:Integration of objective clinical data: 
  • As more patients use devices like pulse oximeters and blood pressure monitors at home, AI systems will incorporate this data to enhance diagnostic accuracy and care recommendations.
  • Improved language models: Advancements in large language models (LLMs) will increase the fluidity and power of AI in healthcare applications as well as increasing patient comfort and satisfaction with VTCR.
  • Expanded condition coverage: AI systems will become more adept at identifying and managing rarer conditions and mental health issues.
  • Addressing socially stigmatized conditions: AI may provide a more comfortable platform for patients to discuss sensitive health issues like sexually transmitted diseases or substance abuse.
  • Focus on chronic disease management: As the leading source of morbidity and mortality in most nations, an increasing focus on chronic diseases in AI-based VTCR will play a larger role in long-term patient care and monitoring.
  • Tackling healthcare access and resource inequities: AI-powered virtual triage can improve healthcare access for underserved, high inequity populations, addressing long standing disparities in care.

We are at the beginning of the healthcare journey with AI-based VTCR. As AI continues to evolve, its ability to align care with patient needs, detect critical conditions earlier, refer patients for needed care, and support clinical decision-making will increase dramatically.

AI is poised to revolutionize the patient’s and the clinician’s journey, from initial symptom assessment to long-term care management. By leveraging VTCR technology to improve care acuity alignment and early detection of serious conditions, healthcare providers and plans can enhance patient outcomes, reduce costs, and create a more efficient, equitable healthcare system for all. For senior healthcare executives, embracing and integrating these AI technologies will be crucial in staying at the forefront of patient care and operational efficiency and organizational performance in the coming years.

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Monday, November 4, 2024

Can artificial intelligence contribute to improved clinical reasoning?

Researchers from the University of Minnesota Medical School, Stanford University, Beth Israel Deaconess Medical Center and the University of Virginia analyzed the efficacy of GPT-4, an artificial intelligence (AI) large language model (LLM) system, as a diagnostic tool to assist physicians’ diagnoses. The study, published in JAMA Network Open, found that physicians’ access to GPT-4 as a diagnostic aid did not result in significantly improved clinical reasoning compared to physicians left with conventional resources, including UpToDate and Google.

“The field of AI is expanding rapidly and impacting our lives inside and outside of medicine. It is important that we study these tools and understand how we best use them to improve the care we provide as well as the experience of providing it, Andrew Olson, M.D., a professor at the University of Minnesota Medical School and a hospitalist with M Health Fairview. “This study suggests that there are opportunities for further improvement in physician-AI collaboration in clinical practice.”

The study analyzed 50 total U.S.-licensed physicians across family, internal and emergency medicine. The median diagnostic reasoning score per case was 76% for the group with AI access, and 74% for the group only referencing conventional resources. The AI group spent an average of 519 seconds per case, compared with 565 seconds per case for the conventional resources group.

Researchers were able to conclude that access to GPT-4 did not significantly increase physicians’ diagnostic reasoning, although, on its own, the LLM did surpass the performances of both clinicians using conventional diagnostic online resources, and clinicians assisted by the program. These findings could prove the necessity of further research to understand how clinicians should be trained to use these tools.

Independently, the LLM demonstrated higher performance than either physician group, thereby indicating the need for training and development to achieve the full potential of physician-AI collaboration in clinical practice. At the forefront of these efforts, the four institutions behind the study announced a collaboration on a bi-coastal AI evaluation network, ARiSE, designed to further evaluate generative AI outputs in healthcare

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