Tuesday, June 15, 2021

When AI Meets SDOH

Artificial intelligence can help identify and address the social determinants of health.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Machine learning is getting better at predicting things. There are now algorithms that improve the detection of diabetic retinopathy, predict the onset of sepsis, and help determine a critically ill patient’s risk of dying.  But a piece of wisdom from Warren Buffet comes to mind: “Predicting rain doesn’t matter. Building arks does.” Even the most impressive algorithm is relatively useless if it doesn’t allow us to build better “arks” to address the medical  disorder or  complications that the digital tool identifies.  And building the best healthcare interventions requires that clinicians not just identify the right signs, symptoms, and biomarkers, whether they be high cholesterol levels, elevated A1c, or a lump in a woman’s breast.  It requires we understand what’s happening in patients’ everyday lives outside  the clinic, the so-called social determinants of health (SDOH), and then using that data to inform treatment.

A great deal has been written recently about SDOH. Health professionals are slowly beginning to realize that we cannot “remove health and illness from the social contexts in which they are produced,” according to Simukai Chigudu, Oxford Department of International Development, University of Oxford.1  That begs the questions: What social issues are mostly likely to influence our patients’ clinical course and what do we do about them? How can AI help alleviate the impact of these issues?

The Centers for Disease Control and Prevention (CDC) has numerous data sources to help incorporate SDOH into public health initiatives and medical practice. But as the agency points out, moving from data to action is the hard part. CDC has several programs designed to focus clinicians’ attention on key social issues, including socioeconomic status, educational level, and work history. One initiative, for instance, zeros in on the role of EHRs. Its purpose is to support the incorporation and use of structured work information into health IT systems. How might this SDOH element inform a physician’s different diagnosis? Consider a patient with hypertension who doesn’t respond to a low sodium diet or anti-hypertension medication. Awareness of his 10 year history as a house painter might point the clinician in the direction of lead poisoning, a possible cause of hypertension. Similarly, a nurse practitioner may be at a loss to figure out why a patient with type 2 diabetes has recently seen a spike in her A1c levels.  If at EHR system is linked to work history, when the NP enters the reason for the clinic visit into the EHR field for chief complaint, this might trigger a pop up box that states that the patient works the night shift and that shift work can affect diabetes control. The system would then provide recommendations on diabetes management among shift workers. The same CDC program is also working on a work information data model, as well as national standards for vocabulary, system interoperability, and instructions for health IT system developers. 

At Mayo Clinic, we are also studying the impact of SDOH on health and disease. Young Juhn, MD, MPH, Director of the AI Program and Precision Population Science Lab of Department of Pediatric & Adolescent Medicine at the Clinic, has studied  the effects of socioeconomic status on health since in 2006 when his research work was supported by the NIH. With the support from the NIH, he developed and validated a housing-based socioeconomic measure called the Housing Based Index of Socioeconomic Status or HOUSES index, which is being used in epidemiologic research to help understand health disparities and differences in a variety of health outcomes in both adults and children. The index has enabled researchers to overcome the absence of socioeconomic measures in commonly used data sources (e.g., medical records or administrative data), conduct geospatial analysis in health disparities research, and apply a life course approach.

The HOUSES index is an objective way to measure the individual-level socioeconomic status of  patients because it is based on real property data for individual (not aggregated) housing units and is derived from public records; it uses 4 data points: the number of bedrooms in a person’s residence, as well as the number of bathrooms, square footage of the unit, and estimated building value of the unit. The index can help target patients who are most at risk of poor health outcomes and inadequate access to health care , demonstrating the real value of adding SDOH into the mix by addressing the limitations of the existing SDOH. For example, Stevens et al have shown that patients with a higher HOUSES score (quartiles 2-4) had 53% lower risk of  kidney transplant rejection (adjusted hazard ratio 0.47), when compared to those with the lowest score (quartile 1).2 Dr Juhn and his colleagues have found that HOUSES can  predict 44 different health outcomes and behavioral risk factors in both adults and children.

Of course, clinicians still have to be reasonable in their expectations. Even if an algorithm were outfitted with every conceivable SDOH, it still may not reduce disparities in healthcare. Patients and providers may choose to ignore the recommendations of the improved algorithm because they believe the recommended diagnostic test is too expensive or unjustified, for example, because it is too difficult for patients to get to the testing facility, or because a patient’s lack of health literacy prevents them from seeing the value of said test.

Despite these shortcomings, SDOH-enhanced algorithms have the potential to improve patient care. While physicians and nurses have gained tremendous insights into health and disease by measuring countless clinical parameters during office visits, it’s clear now that’s not enough.  The clinical picture generated with these metrics is too often hazy and needs to be supplemented by a long list of social metrics that can influence a patient’s access to care and their long-term outcomes.


References

 1. Chigudu S. Book: An ironic guide to colonialism in global health. Lancet. 2021. 397:1874-1975.

 2. Stevens M, Beebe TJ, Wi Chung-II et al. HOUSES index as an innovative socioeconomic measure predicts graft failure among kidney transplant recipients. Transplantation 2020; 104:2383-2392.

Friday, June 11, 2021

The Digital Reconstruction of Healthcare is Upon Us

The transition from brick and mortar to digital medicine will profoundly impact the way clinicians and patients interact—and will likely improve clinical outcomes.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Paul Cerrato and I are excited to finally announce the publication of our 5th book together: The Digital Reconstruction of  Healthcare: Transitioning from Brick and Mortar to Virtual Care. In March, we posted the table of contents of the new book. Now that it’s reached the “newsstand,” we wanted to whet readers’ appetite by sharing some additional excerpts.

The logical place to start any discussion on this topic is to explain why  digital reconstruction is necessary, which we address in Chapter 1:

Episodic Medical Care Often Falls Short

White coat hypertension, the tendency for patients to only present with elevated blood pressure during a doctor visit, illustrates a problem that permeates the entire healthcare ecosystem. Any sign or symptom that a patient exhibits during an office or clinic visit may not be a true presentation of their underlying condition. Unfortunately, this phenomenon not only affects a person’s blood pressure but other common parameters. White coat hyperglycemia has also been documented. And since psychosocial stress is likely a contributing cause of such white coat reactions, white coat hypercholesterolemia, asthma attacks, and numerous other conditions probably exist as well, all triggered by stress hormones. Conversely, any normal readings during a physical examination or laboratory test do not necessarily mean a patient is in good health.

The common denominator in all these scenarios is episodic care. In such situations, clinicians are making a judgement about a patient’s health status based on cross-sectional data, which can be misleading. But given the financial restraints and incentives that exist in healthcare today, it has been the only viable option—until now. With the emergence of virtual care and remote patient monitoring (RPM), gathering long-term data for many clinical parameters is no longer out of reach. That steady stream of online data can be inserted into predictive analytics algorithms to help locate patients at high risk. Some thought leaders refer to this shift in priorities as the movement from episodic to life-based care.

Such digitally enhanced patient engagement is the future of healthcare. No responsible practitioner would conclude a diabetic patient is in good metabolic control based on a single blood glucose reading, and yet that is often the same reasoning we use when a routine metabolic panel comes back stating LDL cholesterol, serum calcium, white blood count, blood pressure, and numerous other parameters are all “within reference range.” We now have the technology to move beyond this outdated mindset. That technology enables us to detect longitudinal patterns of change in patients’ health status. By way of example: Longitudinal data on systolic blood pressure has been linked to patients’ risk of cardiovascular disease.

The Power of Remote Patient Monitoring

Many patients and healthcare professionals have yet to appreciate the power of remote patient monitoring. When executed correctly, it can be truly transformative, combining medical self-care, objective physiological data, and expert advice to improve both preventive and therapeutic care. And as RPM continues to mature, it has the potential to completely reinvent healthcare, especially among those motivated patients who see it as a source of self-empowerment. The power of RPM in the hands of motivated asthmatic patients was well illustrated in an experiment conducted by University of Wisconsin and Centers for Disease Control and Prevention researchers. Using an electronic medication sensor that was attached to inhalers of 30 patients, Van Sickle et al. tracked patients’ use of inhaled short-acting bronchodilators for 4 months. To evaluate patients’ health status, investigators asked them to fill out surveys, including the Asthma Control Test (ACT). One month into the study, they also received weekly emails that summed up their medication usage for the preceding week and offered suggestions on how to comply with the National Asthma Education and Prevention Program guidelines. No changes were observed in ACT scores after the first month, but they increased by 1.40 points each month after that. Patients also reported significant decreases in daytime and nighttime symptoms. They also noted “increased awareness and understanding of asthma patterns, level of control, bronchodilator use (timing, location) and triggers, and improved preventive practices.” That last statement is worth closer inspection.

Very often, patients do not understand the triggers that cause symptoms, unless they are actually attuned to subtle changes in their physiology. Providing graphic displays of their symptoms paired with the medication usage can be eye opening for many patients who never noticed patterns of use before. These newfound revelations were summed up by several patients participating in the study:

“I learned that I used my inhaler more than I remember. I was able to see and relate to my doctor that my asthma is not under control.’’ Participants also reported that the receipt of information about the time and location where they used their inhaler helped to highlight locations and exposures to triggers that led to symptoms. ‘‘I’ve been more keen to note surroundings when I feel shortness of breath,’’ one participant said. ‘‘It opened my eyes to triggers I wasn’t aware of in the past.’’

The results of this experiment highlight 2 important lessons for patients and clinicians, summed up in a few choice words from Kamal Jethwani, MD, MPH, from Partners HealthCare: “The future of health is proactive, self-managed wellness. We want to put the onus back on the person. We’re saying: It’s your health, and I’m no longer your babysitter.”

Friday, May 21, 2021

AI-Enhanced Cardiology Takes Another Step Forward

Combining a convolutional neural network with routine ECGs detected low ejection fraction, a signpost for Asymptomatic left ventricular systolic dysfunction


John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Asymptomatic left ventricular systolic dysfunction (ALVSD) may not be the most familiar disorder in medicine, but it nonetheless increases a patient’s risk of heart failure and death. Unfortunately, ALVSD is not that easily detected. Characterized by low ejection fraction (EF) — a measure of how much blood the heart pumps out during each contraction — it’s readily diagnosed with an echocardiogram. But because the procedure is expensive, it’s not recommended as routine screening for the general public. A recently developed AI-enhanced algorithm that’s used in conjunction with an ECG can identify low EF, one of many advances that will eventually make machine learning an essential part of every clinician’s “tool kit.”

The new algorithm, a joint effort between several of Mayo Clinic’s clinical departments and Mayo Clinic Platform, was published online by Nature Medicine. The EAGLE trial included over 22,000 patients, divided into intervention and control groups and managed by 358 clinicians from 45 clinics and hospitals. The algorithm/ECG was used to evaluate patients in both groups but only those clinicians allocated to the intervention arm had access to the AI results when deciding whether or not to order an echocardiogram. In the final analysis, 49.6% of patients whose physicians had access to the AI data underwent echocardiography, compared to only 38.1% (Odds ratio 1.63, P< 0.001). Xiaoxi Yao, with the Kern Center for the Science of Health Care Delivery, Mayo Clinic, and associates reported that “the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm) and among those who were identified as having a high likelihood of low EF.” Using the AI tool enabled primary care physicians to increase the diagnosis of low EF overall by 32% when compared to the diagnosis rate among patients who received usual care. In absolute terms, for every 1,000 patients screened, the AI system generated five new diagnoses of low EF compared to usual care.

Earlier research on the neural network used to create the AI tool had shown that it’s supported by strong evidence. A growing number of thought leaders in medicine have criticized the rush to generate AI-based algorithms because many lack a solid scientific foundation required to justify their use in direct patient care. Among the criticisms being leveled at AI developers are concerns about algorithms derived from a dataset that is not validated with a second, external dataset, overreliance on retrospective analysis, lack of generalizability, and various types of bias, issues that we discuss in The Digital Reconstruction of Healthcare. The EAGLE trial investigators addressed many of these concerns by testing its algorithm on more than one patient cohort. An earlier study used the tool on over 44,000 Mayo Clinic patients to train the convolutional neural network and then tested it again on an independent group of nearly 53,000 patients. And while this study was retrospective in design, other studies have confirmed the algorithm’s value in clinical practice by using a prospective design. The most recent study, cited at the beginning of our blog, was not only prospective in nature, it was also pragmatic, which reflects the real world in which clinicians practice. Traditional randomized controlled trials consume a lot of resources, take a long time to conduct, and usually include a long list of inclusion and exclusion criteria for patients to meet. The EAGLE trial, on the other hand, was performed among patients in everyday practice.

Friday, May 14, 2021

A Unique Partnership Delivers Acute and Holistic Home Care

Mayo Clinic, Kaiser Permanente, and Medically Home join forces to offer patients the best of both worlds, forging a partnership that has the potential to redefine the hospital industry.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

One of the problems facing most patients who require hospital admission is removing their familiar surroundings and emotional supports. While these resources might be considered less critical than specialized clinical expertise, there's little doubt that these "less important" factors play a crucial role in the healing process. Even the most dedicated nursing staff can never replace having a loved one available 24/7 at home. Nor can the most nutritious hospital food ever replace appetizing home-cooked meals. Equally important are the familiar wake/sleep cycles that patients are accustomed to at home, which usually must give way to hospital routines that demand blood pressure checks at three in the morning.

A new partnership[FJA1]  between Mayo Clinic, Kaiser Permanente, and Medically Home launched recently to expand access to care that combines the comforts of home with the expertise of hospitalists, helping patients receive the holistic care needed to speed long-term recovery and the acute care services to address their immediate medical needs. Stephen Parodi, MD, executive vice president of the Permanente Foundation, summed up the philosophy behind the new initiative succinctly, "Treating patients in their home allows physicians to treat the whole patient. We see their individual needs and can integrate critical information, such as diet, physical environments and social determinants of health, into their care plans."

The challenge, of course, is how to turn this philosophy into a cost-effective, safe program. In fact, that transition has already begun.  As we discussed in a previous blog, Mayo Clinic launched its advanced care at home program last summer at Mayo Clinic in Florida and Mayo Clinic Health System in Eau Claire, Wisconsin, to deliver complex, comprehensive care and restorative services to qualifying patients in their homes. These services, which are provided in-person and virtually, include:

  • Infusions.
  • Skilled nursing.
  • Medication delivery.
  • Laboratory and imaging services.
  • Behavioral health.
  • Rehabilitation services.

Similarly, Kaiser Permanente launched its hospital-at-home program in two regions last year, admitting patients from multiple hospitals across both its Northern California and Oregon locations. In this model, Kaiser Permanente has a single medical command center in each region, supporting multiple hospitals to care for patients longitudinally across their acute and restorative phases.

The new partnership will scale Medically Home’s operations, allowing more providers to offer this unique care model. The model includes a 24/7 medical command center (Figure 1) staffed with clinicians in regular communication with a care team in the community that contains EMTs and nurses who provide bedside care. Among the elements that make the new program unique:

  • Required protocols for high-acuity care in the home.
  • Rapid response logistics systems and providers of care in the home.
  • Integrated communication, monitoring and safety system technology in the home.
  • A software platform, the Cesia® Continuum, for orchestrating high-acuity care in patients’ homes. (Figure 2)

One of the problems with writing about a significant health care event while at the same time being a key player in the event is some outsiders will question our objectivity and immediately assume we exaggerate its importance to gain a competitive advantage. The plain truth is this partnership is not just about Mayo Clinic, Kaiser Permanente, and Medically Home. The partnership's ultimate goal is to bring better care to patients across the country and the globe. With that in mind, the program will provide all the necessary outcomes data, tools, systems, training and technology to enable the model’s widespread adoption.

Figure 1


Figure 2


Wednesday, May 5, 2021

Health Data Privacy Gets the Attention It Deserves

The Partners in Privacy Conference gathered world-class experts to address some of health care’s most vexing problems.


John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

The challenges involved in keeping patient and consumer health data private may seem daunting. Still, a recent virtual conference hosted by Mayo Clinic brought together over 80 world-class experts to address the issues. Their insights are worth a closer look.

During his opening remarks at Partners in Privacy Conference: The Ethical and Responsible Use of Data to Drive Cures (April 22, 2021). Gianrico Farrugia, M.D., CEO of Mayo Clinic, acknowledged the delicate balance required to respect the public’s desire to keep its data confidential and the health care community’s desire to use that data to improve patient care, “What the right thing is is not a simple question to answer  it is complex and can vary in different countries and even under different circumstances. A person may be less or more willing to share information and have a different view on data privacy at different times in their lives.  What degree of data privacy seems right to a healthy 40 year is likely not going to be the same for that same person with advanced cancer or neurodegenerative disease. Moreover, the world of research and collaboration is changing. There are global opportunities for new partnerships among medical centers, industry and government that increasingly involve data sharing.”

With these concerns in mind, Dr. Farrugia introduced the keynote speaker, Micky Tripathi, Ph.D., MPP, the National Coordinator for Health Information Technology for the U.S. Department of Health and Human Services. Micky briefly reviewed the achievements of the HITECH Act and the implementation of EHRs around the country. Still, he also pointed out that the speed with which this rollout occurred has made us all realize that “technology has outpaced policy.” One of the ways in which this disconnect is being addressed is through the 21st Century Cures Act. As of April 5, 2021, the law now requires that providers, health care information networks, and technology developers give the public friction-free access to and control of their health data through apps. But that access also means patients can more easily share that information with third parties that are not required to follow the rules spelled out in HIPAA. And even when such data remains within the confines of a health care provider organization bound by HIPAA regulations, keeping it private and secure remains a challenge, despite the fact that there are ground rules on de-identifying it. 

These challenges were among the many questions addressed by the four breakout groups that followed Micky’s presentation. We discussed privacy laws and regulations; state-of-the-art methods for protecting data privacy used to advance health care; consumer and patient attitudes about privacy; and balancing privacy protection with the benefits of research and commercialism.  The lessons learned from the conference will be presented in a white paper that is currently being developed by the thought leaders involved in the project. But in lieu of that, consider a few takeaways:

  • Patient consent will evolve. In the future, there will be more granular control options; we will likely go from a black and white consent decision to a few more controls based on the use of the data and the actors who have access to it.

  • There will be more transparency in data use. That can take the form of a “nutrition label” type description for every algorithm that clearly spells out the data used to create it, as well as its performance and characteristics.

  • Technology is evolving, with machine learning and natural language processing accelerating very quickly. It will fundamentally change how we deliver care, but it will also mean more data being used for more purposes. It is incumbent upon us to keep that data safe and to respect patient preferences as we develop algorithms. Fortunately, privacy and security technology is evolving as well, including advances in de-identification, encryption, allow lists and tokenization. However, the conference attendees emphasized that these approaches are imperfect, which means we will need a multi-layered approach.

Cris Ross, the CIO at Mayo Clinic, summed up many of the observations gleaned from the conference: “We came into this conference wondering whether matters of law, regulation, policy, technology, and market practice are fully established, or if there’s a need for further exploration and consensus. We concluded there is a need for more exploration, and a need for a group like this to help create a consensus, with the goal of advancing cures with the ethical use of data and preservation of patient privacy?" Cris emphasized that this virtual meeting demonstrated that this need exists and we would like to issue a call to other leaders in this field to advance the agenda. Partners in Privacy Conference: The Ethical and Responsible Use of Data to Drive Cures was only the first step in a journey that will require the input and expertise of stakeholders around the nation and the world.

Monday, April 26, 2021

It’s OK to Break the Rules Now and Then

Technological innovation sometimes requires we take risks — and question the tenets of evidence-based medicine.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

It’s challenging at times to know when to follow the rules and “color inside the lines” and when to ignore those lines and forge ahead. That’s true whether we’re navigating everyday life, creating new technology, or devising the best patient care initiatives. Which brings to mind a quote from Elbert Hubbard: “The greatest mistake you can make in life is continually fearing that you'll make one.” 

Over the years, we have discussed the strengths and weaknesses of evidence-based medicine and randomized controlled trials (RCTs) in several books and articles, our point being that fear of investing in a treatment approach because it not supported by the RCT “gold standard” can create a kind of inertia that ultimately hurts patients.1,2  And even if we put aside the fear factor that Hubbard mentions, mounting evidence strongly suggests that an in-depth analysis of large data sets can supplement — and in some cases be substituted for — RCTs to support the clinical decision-making process.

That doesn’t imply that RCTs should be abandoned.  The list of treatments that have been supported or retired due to a well-designed RCT is long. For decades, surgeons used radical mastectomy to treat breast cancer until a controlled trial demonstrated that less disfiguring alternatives were just as effective in managing the disease.3 Similarly, clinicians used to freely prescribe hormone replacement therapy to women in menopause until the Women’s Health Initiative, also an RCT, demonstrated that it increases the risk of heart disease, stroke, and breast cancer. But on the other hand, there have been many recent non-RCT investigations that have taken advantage of the power of massive data sets and have generated actionable insights. With the VA Boston Health System, Julia Prentice and her colleagues, using administrative data from more than 80,000 veterans, have shown that among patients with Type 2 diabetes, sulfonylurea drugs increased the risk of dying or being hospitalized when compared to patients on thiazolidinediones.4 Similarly, David Graham created a stir when he analyzed the patient records of approximately 1.4 million patients who belonged to Kaiser Permanente in California. They aimed to determine if rofecoxib (Vioxx) increased the risk of acute myocardial infarction and sudden cardiac death. Graham et al. reviewed the equivalent of 2,302,029 person-years of follow-up. They detected 8,142 cases of serious coronary heart disease (CHD), 2,210 of which were fatal. The odds of developing CHD among patients taking any dose of the medication were 59% greater than it was among controls. Among patients who took high doses, namely more than 25 mg daily, the odds of heart disease were 258% greater. 5

More recently, nference, a data analytics firm with a partnership with Mayo Clinic, spearheaded several data-intensive studies that did not use the traditional clinical trials protocol. One study used deep neural networks to evaluate 15.8 million clinical notes in an EHR from over 30,000 patients who underwent COVID-19 diagnostic testing.6 When investigators compared patients with clinically apparent COVID-19 with negative patients about a week before they had PCR testing to confirm the diagnoses, they found loss of taste and smell was more than 37-fold more likely to occur in those whose infection was confirmed versus those who tested negative. Shweta et al. state, “This study introduces an augmented intelligence platform for the real-time synthesis of institutional knowledge captured in EHRs. One caveat that the researchers acknowledge in the report was that they had yet to conduct prospective validation of the augmented EHR curation approach.

A second nference-based investigation reviewed the records of patients who had received more than 94,000 doses of the Pfizer COVID-19 vaccine, more than 36,000 doses of the Moderna vaccine, and 1,745 doses of the Johnson & Johnson vaccine. The study’s goal was to determine the incidence of cerebral venous sinus thrombosis (CVST), which has been reported in a small number of patients after receiving one of the vaccines.7 The preprint study found no significant association between any of the vaccines as CVST.

One of the strengths of RCTs is their prospective nature, a design that is more likely to eliminate confounding variables and bias when compared to retrospective studies. But at the same time, several RCTs have fallen short because they were underpowered, resulting in false-negative results. Also, RCTs are expensive and often require many years to generate results that clinicians can use at the bedside. On the other hand, retrospective analyses can generate results much more quickly, and under the right circumstances, can provide actionable insights and inform the clinical decision-making process.

Thomas Frieden, MD, MPH, a former director of the CDC, has pointed out the real-world advantages of retrospective cohort studies, which have been used to assess the prognosis and treatment of various types of cancer. That, in turn, has led to better treatment protocols. Similarly, such cohort studies have successfully been used to evaluate survival among pediatric cancer patients and made clinicians aware of the “increased risk of post-treatment cardiac complications, enabling better clinical care.”8 Frieden summed up the controversy this way, “Elevating RCTs at the expense of other potentially highly valuable sources of data is counterproductive. A better approach is to clarify the health outcome being sought and determine whether existing data are available that can be rigorously and objectively evaluated, independently of or in comparison with data from RCTs, or whether new studies (RCT or otherwise) are needed.”

When comparing research methodologies, it’s important to remember that’s it’s not a sports competition; there doesn’t have to be a clear winner and loser. Big data analytics and RCTs each have their strengths and weaknesses and can be deployed accordingly. When there's enough time and resources available to conduct a controlled trial, it is often the best way to evaluate potentially useful treatment approaches. Still, when clinicians need to quickly make diagnostic and therapeutic decisions, especially during an international crisis, we don't always have the luxury of time.

 

References

1. Cerrato P, Halamka J. Realizing the Promise of Precision Medicine. 2017, Academic Press/Elsevier, Cambridge, MA, pp. 87-91.

2. Cerrato, P, Halamka J. The Transformative Power of Mobile Medicine. 2019, Academic Press/Elsevier, Cambridge, MA, pp 57-58.

3. Treasure T, Takkenberg JM. Randomized trials and big data analysis: we need the best of both worlds. Eur J CardioThoracic Surg. 2018; 53:910-914.

4. Prentice JC, Conlin PR, Gellad WF et al. Capitalizing on Prescribing Pattern Variation to Compare Medications forType2Diabetes. Value in Health. 2014; 17:854-862.

5. Graham DJ, Campen D, Hui R, et al. Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-oxygenase 2 selective and nonselective non-steroidal anti-inflammatory drugs: nested case-control study. Lancet 2005;365:475–581.

6. Shweta F, Murugadoss K, Awasthi S el al. Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis. eLife. Published online July 7, 2020. https://elifesciences.org/articles/58227

7. Pawlowski C, Rincon-Hekling J, et al. Cerebral venous sinus thrombosis (CVST) is not significantly linked to COVID-19 vaccines or non-COVID vaccines in a large multi-state US health system. medRxiv. 2021, April 23. https://www.medrxiv.org/content/10.1101/2021.04.20.21255806v1

8. Frieden TR. Evidence for Health Decision Making —Beyond Randomized, Controlled Trials. N Engl. J Med.2017;377:465-475.

Monday, April 19, 2021

Can AI Reinvent Radiation Therapy for Cancer Patients?

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Of all the advances in health care artificial intelligence (AI), medical imaging is probably the most remarkable success story. Two prominent examples come to mind: Machine learning has helped improve the screening and diagnosis of retinal disease and is making inroads in skin cancer detection. Given these developments, it’s not surprising to find researchers and clinicians developing the digital tools to improve radiotherapy, which combines imaging technology with high doses of ionizing radiation, delivered through a device called a linear accelerator.

Radiotherapy is one of the most common cancer treatments, used to treat more than half of cancers, yet this labor-intensive expertise is in short supply. 1  The digital tools can meet unmet patient needs for the treatment and increase the accuracy of the delivered therapy.  

To fully appreciate the impact that AI-enhanced algorithms have on radiotherapy, it helps first to understand how the equipment and technology used to deliver radiation to a patient’s tumor functions. Ionizing radiation achieves its purpose by disrupting cellular DNA, which prevents cancer cells from growing and dividing, which in turn causes solid tumors to shrink in size. Unfortunately, the same radiation that disrupts tumor growth can also have a detrimental effect on nearby healthy tissue, resulting in various of complications.

To minimize this risk, computerized programs are employed to outline all the anatomical structures closest to the tumor to be irradiated so that the electron beam will more precisely target the tumor and spare the healthy tissues — a procedure called contouring. But there is significant disagreement among providers on how to perform the procedure. Diana Lin, with the Department of Radiation Oncology, along with several of her colleagues, point out that such variation is common and “can affect the resulting plan quality and patient outcomes.” 2 Their systematic review also found that variations in target volume delineation was responsible for greater treatment toxicity and decreased survival. The medical literature also reveals that major deviations in target delineation occur in up to 13% of radiation therapy plans.

Although computer programs are available to help reduce inconsistencies and improve contouring, these digital tools are far from perfect. Chris Beltran, Ph.D., chair of the Division of Medical Physics at Mayo Clinic, Florida, points out that the relevant organs and tumors “are critical inputs for the computer models that are currently used to generate radiation dose plans. If organs are not properly identified, the radiation plan may not protect these critical structures or adequately treat the tumor. While this computational modeling reduces the risk to healthy tissue, machine learning is now being investigated to make contouring more accurate.

Mayo Clinic and Google Health recently announced a joint initiative focusing on research into applying AI to radiation therapy planning. Radiation therapy experts from Mayo Clinic, including radiation oncologists, medical physicists, dosimetrists and service design, are collaborating with Google Health’s experts in applying AI to medical imaging. In this first stage of the initiative, Mayo Clinic and Google Health teams are using deidentified data to develop and validate an algorithm to automate the contouring of healthy tissue and organs from tumors and develop adaptive dosage and treatment plans for patients undergoing radiation therapy for cancers in the head and neck area. The goal of the IRB-approved project is to develop an algorithm that will improve the quality of radiation plans and patient outcomes while reducing treatment planning times and improving the efficiency of radiotherapy practice.

Because the head and neck contain several sensitive organs that are in close proximity to one another, the Mayo Clinic/Google project began its investigation in this area of the body. Radiation oncologists today painstakingly draw lines around sensitive organs like eyes, salivary glands and the spinal cord to make sure radiation beams avoid these areas. And while this works well, it takes a really long time to get it exactly right, says Cían Hughes, M.B., Ch.B., informatics lead at Google Health. We see huge potential in using AI to augment parts of the contouring workflow, and hope that this work will ultimately enable a better patient experience and help patients get the treatment they need sooner.

The potentially revolutionary impact of this new initiative becomes obvious when one considers the fact that virtually all linear accelerators are equipped with an open-source API, which means it may be possible for hospitals around the world to use this new technology to dramatically improve the radiological contouring and making these treatments available to underserved patient populations.


Reference

1. Thomadsen B. The shortage of radiotherapy physicists. J Am Coll Radiol. 2004 Apr;1(4):280-2. doi: 10.1016/j.jacr.2003.12.036. PMID: 17411581.

2. Lin D., Lapen, K, Sherer MV et al. A Systematic Review of Contouring Guidelines in Radiation Oncology: Analysis of Frequency, Methodology, and Delivery of Consensus Recommendations. Int J Radiology Oncol. 2020; 107:828-835.

Friday, April 9, 2021

A Heart Held Humble Levels and Lights the Way

Observations on executive training & executive rest

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Humility is not a very popular word among business and health care executives. Often considered a sign of weakness, this personality trait is not often applauded in executive training programs or boardrooms. A revealing piece in Harvard Business Review sums up the problem in its title: “If Humility is So Important, Why Are Leaders So Arrogant?” The article goes on to discuss the push among HR consulting firms and psychology experts to develop the H Factor, a combination of honesty and humility. Despite this celebration of humility, “it flies in the face of daily headlines in the Wall Street Journal and the realities of our business and political cultures,” says the HBR article.

Several management experts have tried to explain this paradox. Edgar Shein from MIT Sloan School of Management posits that the prevailing mindset about managers is that life is a competition and being a successful leader is all about getting results at all costs, which in turn requires telling others what to do. There’s little room for humility and gentleness in that formula for success.

Which brings us to the blog’s title: a heart held humble levels and lights the way. It’s a quote from Along the Road, a song by Dan Fogelberg. It suggests that informed humility accomplishes two goals: It levels us, i.e., it provides balance in making decisions, and it lights up the path as we move forward to accomplish our mission. At the Mayo Clinic Platform, the pursuit of balance and enlightenment is accompanied by complete transparency about our goals, dreams, and fears. That certainly requires humility.

At some health care organizations, executive coaching is stigmatized. If a leader falters or is assigned to a role beyond competency (the Peter Principle), a coach is assigned. We have a different notion. If we're charged with leading a new team on a new journey with new rules — the COVID new normal — we must embrace the best of what collaborators and partners have to offer. We think bringing on an external coach as a sounding board during the next six months of great change will be empowering to us all. The idea is that we'll meet with the coach twice a month to present our strategy, structure, staffing, and process ideas to benchmark against the experience of high performing organizations and teams. It's likely we'll receive feedback and inspiration that exceeds our own life experiences.  At the same time, we'll understand more about how we can improve efficiency, communications, and decision-making. The process will be very personal, and we’ll have to grow as people and leaders. Our life experience has shaped our personalities and our approach to problems, and although it has served us well in the past, we’ll need to focus on how to we change to lead the team through the challenges ahead. 

Our life experiences have taught us to take accountability for every situation. As we build a scalable platform team, it will be more important to orchestrate and delegate, replacing individual efforts with repeatable processes. Building a sustainable organization that scales from dozens of projects to hundreds requires leadership evolution. Coaching can help with such polishing, especially when working at an accelerated pace. Such coaching is not only seldomly disclosed, but also rarely documented. However, we’ll keep diaries of what we learn each month and how it changes our behavior.  We'll share that broadly. Some may suggest that this exposes our vulnerabilities — that’s a good thing! 

Along the road to informed humility, we also recognize the need for rest. Despite what many executives imagine, the human body and mind are not perpetual motion machines. The workaholic CEO may be admired in much of corporate America, but as health professionals, we know better. The evidence demonstrating the detrimental effects of overwork on the brain and immune system is overwhelming; it would be irresponsible for us to ignore it. The American Institute of Stress calculates that 77% of Americans “regularly experience physical symptoms caused by stress.” The problem is so prevalent in society, there is even a medical specialty devoted to it: psychoneuroimmunology, which rests upon one simple truth: Thoughts have physiological consequences. And ignoring this truth may not have immediate repercussions for executives, but its insidious effects eventually take their toll. Solutions abound: Stress management techniques like mindfulness meditation, walks in the woods, crossword puzzles, long, hot baths, music — each of us responds to different modalities.

After all these decades, Fogelberg’s lyrics still offer sage advice to executives who want to inspire others and serve as role models:

Along the road
Your steps may tumble
Your thoughts may start to stray
But through it all a heart held humble
Levels and lights your way

Tuesday, March 30, 2021

Health Care Needs Better Marketing, Too

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Inspiration comes in all sizes and shapes. Neil deGrasse Tyson, a world-renowned astrophysicist and director of the Hayden Planetarium in New York City, continues to inspire us with words like, “The good thing about science is that it's true whether or not you believe in it.” Amidst all the confusion and debate in the popular press about health science, this form of uncommon sense needs more media attention. It’s a truism that may have prompted Dr. Tyson to pen a recent editorial in The Wall Street Journal entitled “Science Needs Better Marketing.” The same holds true for health care and the digital tools now available to support it.

We want the public to appreciate what health science does for us each day, and to understand that health science is not simply one more opinion that we can choose to ignore or accept depending on our personal belief system. But how do we, as scientists, accomplish that heroic feat without alienating consumers and patients? Or being accused of hyperbole?

Many consumers see marketing as simply a way to persuade them to buy the latest phone or fast car. But it can be used for nobler purposes, serving as an essential tool in creating aware, informed patients. Ansuya Bijur, Director of Marketing Coordination for Mayo Clinic Platform, points out that, “Patients need to make informed decisions about their care. Today, the consumer has access to a plethora of information from their smartphones. A search for ‘are vaccines safe’ on Google gets 1,400,000,000 results. That search includes everything from academic papers to news stories, social media posts by influencers, to a blog post by a mom in Kansas City detailing her personal experience. This is where marketing can help,” explains Bijur. “For example, a year-long marketing campaign tracking a COVID-19 survivor whose persistent symptoms are treated by a health care team who find novel therapies/procedures is credible, provides evidence, is unbiased and balanced.”

Similarly, there is credible evidence to show that the three vaccines in use are having an impact of COVID-19 hospitalizations and mortality in the U.S. Those with a short memory, or are too young to remember, will likely forget that this accomplishment is only one in a long list that includes the complete eradication of smallpox, the control of poliomyelitis — after the vaccine was introduced in 1955, cases dropped from 29,000 to fewer than 900 by 1962 — and numerous other infections that once killed millions across the globe but now respond to vaccines.1 Antibiotics are another victory for health science. Today’s headlines often feature the dangers of antibiotic overuse and the spread of drug-resistant microbes. While these concerns need our attention, it would be wrong to let them distract attention from the millions of lives saved by antibiotics. Anyone who has lived long enough to remember life in the first decades on the 20th century knows the helplessness one felt when a loved one died from tuberculosis, a postoperative Staph infection, and countless other killers, or watched a child with insulin-dependent diabetes disintegrate before their eyes because there was no insulin available.

Unfortunately, telling the success stories of medicine seems to have taken a backseat among many in the mass media to more headline-grabbing negative themes. No doubt, there are many legitimate stories about medicine’s shortcomings that need to reach the public. But journalists also need to have the critical thinking skills to step back on occasion and ask: Am I taking a negative slant because I know my editor or publisher wants more page views, which in turn translate into more ad dollars?  Am I using needlessly inflammatory adjectives to spark readers’ anger?

No one is suggesting that the media roll back the clock to the days when doctors were viewed as “saints in medical garb” (M*A*S*H’s Hawkeye Pierce).  But today’s culture has moved so far in the other direction, rewarding a cynicism that goes beyond reason.  The comedian Stephen Colbert, in one of his more serious moments, accurately described the problem, “Cynicism masquerades as wisdom, but it is the farthest thing from it. Because cynics don’t learn anything. Because cynicism is a self-imposed blindness, a rejection of the world because we are afraid it will hurt us or disappoint us. Cynics always say no. But saying ‘yes’ begins things. Saying ‘yes’ is how things grow.”

Colbert and Tyson apparently share sentiments. In the WSJ editorial, Tyson says, “… let’s not forget the efforts of lab scientists. Nobody writes stories about not dying by not contracting COVID-19. So it’s time to praise the researchers who developed vaccines in record time. If heroes save lives, then they are superheroes who have saved the lives of millions  because of science.” 

Mayo Clinic Platform can add several accomplishments to the list of health care success stories. In 2020, it launched Advanced Care at Home, a one-of-a-kind hospital at home program that has managed nearly 1,000 patients to date. And the Platform is currently finishing the largest-in-history analysis of patient records to find and weed out systemic racism and other forms of inequality, using health care data from 50% of the U.S. population. All the evidence points in one direction: Like many of our colleagues in hospitals and clinics around the world, we are making a difference in patients’ lives, a difference that more people should know about!


References

1. Desmond A, Offit PA. On the shoulders of giants—from Jenner’s cowpox to mRNA Covid vaccines. N Engl J. Med. 2021; 384:1081-1083.

Tuesday, March 23, 2021

Diabetes Meets Machine Learning, Part 2

Continuous glucose monitoring takes center stage

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

In February, we discussed the benefits of using machine learning (ML) to improve screening for diabetes and for managing the disorder. In some situations, ML can more accurately detect the presence of prediabetes, for instance. Similarly, there’s research to show that the right algorithms can improve the treatment of Type 1 diabetes.  But there’s also mounting evidence to suggest ML can benefit Type 2 patients.

One of the problems clinicians and patients have in managing Type 2 diabetes is the fact that medical care is episodic.  Patients may see their physician or nurse practitioner once every few months, which means most of the time, they are on their own. And while it is true that glucose meters let patients monitor their blood glucose (BG) levels on a daily basis, those readings have their limitations. Many patients hesitate to take enough readings because finger pricks hurt. It can also be difficult to determine the need for changes in one’s diet, medication, and physical activity based on a few daily BG readings. And while glycated hemoglobin A1c provides a picture of one’s long-term metabolic control, it doesn’t provide immediate feedback on how to respond to day-to-day changes in food intake, stress levels, exercise and the like.  

Mounting evidence suggests that continuous glucose monitoring (CGM) is a game changer that will profoundly improve a Type 2 diabetic’s ability to control their condition — at least for those patients willing to commit the time and energy needed. Advances in hardware technology and artificial intelligence have ushered in a new approach to metabolic control that takes advantage of CGM. As the name implies, CGM provides patients with continuous, immediate readings of their BG levels, about once every 5 minutes. This is accomplished with the help of a subdermal sensor and a wireless connection to a glucose meter. Some systems will also send this data to a tablet or smartphone, which can then be used to make more informed decisions on how to adjust one’s carbohydrate intake, for instance, or to alert a patient about a particular food or beverage that’s spiking their BG levels. The system will also alert users about unexpected hypoglycemic episodes before they become dangerous. 

The Endocrine Society and the American Diabetes Association consider CGM standard of care for insulin-dependent Type 1 diabetes,1 but until recently, CGM platforms were considered too expensive and not efficacious  enough to justify their use in type 2 diabetes. That thinking is changing, however. Tejaswi Kompala, MD, and Aaron Neinstein, MD, with the Division of Endocrinology, University of California, San Francisco, point out: “High costs and uncertainty over efficacy and necessity have kept CGM from widespread use in people with T2D. However, the newest CGM models, the Abbott Freestyle Libre and Dexcom G6, have begun to overcome many of these technical barriers to use of CGM systems. The sensors are inserted painlessly, are small enough to fit easily under clothing, can remain in place for 10 to 14 days.” 2

Devices like the Freestyle Libre really are an important advance in patient self-care because they eliminate the need to fingerprints. * Combining this technology with machine learning and other types of AI is  helping clinicians and patients interpret a massive influx of data into actionable insights. Several vendors, including Livongo, Canary Health, and Omada Health,* have entered this space, providing patients with easy-to-navigate digital tools that take advantage of the new technology. Livongo, for instance, combines clinical decision support with patient support that includes customized glucose meters and nurse coaches who send personalized messages to patients in need of advice. The Livongo program uses 4 technologies it refers to as AI+AI, which represents Aggregate, Interpret, Apply, and Iterate. It aggregates data from a variety of sources, including its custom-built devices, as well as a patient’s age, gender, zip code, medical claims, and pharmacy claims. The aggregated data is interpreted to create a unique data set it calls Health Signals, which are derived from its applications, devices, coaches, and other sources. It uses these signals to build relevant healthcare messages and outputs for its apps. The Apply in AI+AI refers to the applications linked to its glucose meter, BP cuff, and digital scale, as well as the “human applications,” namely, its coaching system and care coordination team. Finally, the system folds the signals it has generated back into the AI engine to make the system smarter.3

The recent entry of UnitedHealthcare, the largest healthcare insurer in the US., into this area should send a strong signal to clinicians and Type 2 patients alike: This approach has merit!  Major 3rd party payors don’t invest their dollars without doing their due diligence to determine that their offerings will likely be cost-effective. Brian Thompson, CEO of the company’s Medicare and Retirement unit, summed up the potential for ML-enhanced diabetes care: “Continuous glucose monitoring can be a game changer for people enrolled in our Medicare Advantage plans, as the data can be translated into personalized information that can be acted upon in real time.”

 

*Products and services mentioned are not endorsements.  Mayo Clinic has no financial relationship with any of these vendors.

 

References

1. Peters AL, Ahmann AJ, Battelino T et al. Diabetes Technology—Continuous Subcutaneous Insulin Infusion Therapy and Continuous Glucose Monitoring in Adults: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab 101: 3922–3937, 2016.

2. Kompala T. Neinstein A. A New Era: Increasing Continuous Glucose Monitoring Use in Type 2 Diabetes. Evidence-Based Diabetes Management, March 2019, Volume 25, Issue 4.

3. Cerrato, P. Halamka, J. Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. CRC Press/HIMSS, 2020, p. 38-39.

Friday, March 19, 2021

When Technology, Policy, and the Urgency to Change Converge


Our new book, The Digital Reconstruction of Healthcare, is about to be published by Taylor and Francis, as part of its HIMSS book series. We wanted to give readers a preview of what’s to come so we are posting the Preface of the book ahead of time.

In our last two books, we began the conversation discussing the power of words, including misdiagnosis, cynicism, and optimism.1,2 In this book, our focus is on reconstruction, and all its implications for healthcare. To some, it might suggest the tearing down of an existing structure, a complete replacement of the healthcare ecosystem as we know it. Neither of us believe that’s warranted. Our goal, instead, is to address the unsustainable situation that we currently face in the United States and around the globe, and the emerging digital tools that are transforming patient care.

These solutions are not intended to demolish the foundation upon which medicine is built, but neither are they designed to patch up crumbling walls or apply duct tape to the ineffective, cost-prohibitive practices currently in place. To extend the metaphor: The foundation of healthcare may remain solid but many of the walls, floors, windows, and doors that sit on this foundation are rotting and need to be replaced. The next eight chapters will provide evidence from multiple sources, including deep learning specialists, consultations with thought leaders and government officials around the world, peer-reviewed studies, unpublished data, and cutting edge initiatives at Mayo Clinic and several other healthcare leaders — in addition to our combined 60+ years of experience working in healthcare. The preponderance of evidence from all these sources makes a compelling argument: Business as usual is no longer an option; the digital reconstruction of healthcare is no longer on the world’s wish list. It’s becoming a sustainable reality — and one that is all the more necessary in light of the COVID-19 pandemic. That reality will include the partial shift from caring for patients in hospitals, clinics, and medical offices to meeting their needs through telemedicine, hospital-at-home programs, and remote patient monitoring.

In Chapter 1, we address the question: “Is digital reconstruction necessary?” and include a review of the evidence on the effectiveness of digital healthcare, the shortcomings of episodic patient care, diagnostic errors, and our inadequate infrastructure.

Chapter 2 looks at the merits and limitations of telemedicine, hospital and home, and remote patient monitoring. It offers advice on making informed telemedicine choices and the impact of COVID-19, and provides a review of the scientific evidence. We also take a closer look at Mayo Clinic’s Advanced Care at Home program.

Chapter 3 discusses the digital assault on COVID-19, including the development of better predictive and diagnostic tools, expanding the knowledge base to address the pandemic, and the importance of taking a holistic approach to the infection.

Chapter 4 once again explores the value of big data, artificial intelligence, and machine learning, a topic we have looked at in several previous books. The discussion analyzes the evidence in diabetes, cardiovascular disease, cancer, gastroenterology, and psychiatry. We also address one of the most difficult issues in medicine: when does correlation imply causality. Finally, we devote a section to advanced data analytics, including a summary of how Mayo Clinic’s Clinical Data Analytics Platform operates.

Chapter 5, “Exploring the Artificial Intelligence/Machine Learning Toolbox,” is a primer on artificial neural networks, random forest modeling, gradient boosting, clustering, and linear and logistic regression. We are working from the assumption that many readers do not have a background in statistics or data science and hope these brief tutorials translate these complex topics into plain English.

Chapter 6 dives into the many conversational technologies emerging in healthcare. We begin with the role of natural language processing and then discuss the potential of voice technology to help diagnose disease and the role of Siri, Google Assistant, Alexa, and other patient-facing tools. Finally, we emphasize the urgent need to fight misinformation — with truth and trust.

Chapter 7, “Securing the Future of Digital Health,” tackles one of healthcare’s most vexing problems: Cyberattacks. We outline the need for comprehensive risk analysis, staff education to reduce the risk of phishing attacks, along with several basic precautionary steps, including encryption, strong passwords, firewalls, and the like. We also include a section on one of the most vulnerable parts of the healthcare ecosystem: “The Internet of Medical Things.”

Finally, in Chapter 8, we explore international initiatives to digitally reconstruct healthcare. Specific programs in the United Kingdom, China, and the Netherlands are discussed, as are the needs of low-resource nations.

The emergence of the numerous digital health solutions discussed in the following pages does not imply that information technology will singlehandedly rebuild the healthcare ecosystem. Healthcare needs much more than that. Call it “intensive lifestyle management.” Unfortunately, too many IT enthusiasts see technology as a savior and are eager to invest billions of dollars setting up countless initiatives, platforms, and networks in the hope that they will create a more cost-effective system. That kind of magical thinking is doomed to failure over the long-term. If properly deployed, technology will augment other resources much like AI-fueled algorithms are now augmenting the diagnosis of eye disease and cancer. Society will still need to address the underlying cultural, financial, and clinical root causes behind our failing healthcare system — issues that are beyond the scope of this book. We both have the humility to recognize that digital health and all the tools it brings to bear, are only part of the solution. Our experience and research, nonetheless, demonstrate that they are a crucial part of that solution.

 

Paul Cerrato, MA

John Halamka, MD, MS

  

References

1. Cerrato, P. Halamka, J. Reinventing Clinical Decision Support: Data Analytics. Artificial Intelligence, and Diagnostic Reasoning. Taylor & Francis, HIMSS, 2020. Boca Raton, Fl.

2. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine: Leveraging Innovation, Seizing Opportunities, and Overcoming Obstacles of mHealth. Academic Press/Elsevier. 2019, Cambridge, MA.

Tuesday, March 16, 2021

The Year of Living Dangerously


It's been exactly one year since our lives changed. On March 10, 2020, Governor Charlie Baker declared a state of emergency for Massachusetts, changing the way many of us travel. On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic, its first such designation since declaring H1N1 influenza a pandemic in 2009. On March 15, 2020, I flew to Minnesota and prepared my Rochester apartment for a lockdown. I said my goodbyes to colleagues on March 16 and flew back to Boston. We've run the Mayo Clinic Platform at a distance for the past year.

During the pandemic, those old enough to have overcome adversity have done better than the young and less experienced. As we age, we do what must be done, responding to the unexpected and gaining resilience. Consider how resilience has created many good things over the last 12 months.

What have we gained?

Despite our geographic distance, the Mayo Clinic Platform team has truly become a family. The differences between our personal lives and work lives have melted away because we live and work in a continuous stream, navigating each day's events to care for everyone who depends on us at Mayo, at home and in the external world. We've increased our productivity, agility, and pace, doing more each week than would have been physically possible in person. We've also recognized that it's possible to have too much of a good thing, so we have put guardrails on our schedules, including protected Saturdays. As conferences and presentations have become virtual, the number of opportunities for communication has markedly increased. Lost days due to travel are gone, and a one-hour keynote takes just one hour.

We've mastered new remote working technologies, streamlined processes and shortened turnaround times. We've changed the way we recruit and hire talent, reducing our dependence on geographic proximity. We've also created a cadence that begins each week with decision-making and goal clarity and then empowered Platform staff to do what must be done. As servant leaders, we're always available, but staff working remotely have more independence than working in an office. This has enhanced their self-reliance and confidence. We've also brought every deal, every product launch, and every project to completion on time. Finally, we've all stayed healthy, and none of us have experienced any health-related consequences of COVID.

But what have we lost?

As work moved virtual, we lost the "over the cubicle" effect. We don't have casual conversations by the coffee machine or when crossing the hall.  Humans are a social species. We're used to proximity and communication with facial expressions, posture and the handshakes/hugs that offer reassurance. Without that, virtual-only communications can lack context and create anxiety.

Since everyone is connected all the time, there is an expectation of instant response and same-day issue escalation. That forces multi-tasking because we're doing two jobs  the meeting in front of us and the hundreds of mini-meetings occurring in email, text and calls.

That multi-tasking and the focus required to use video-based work tools has caused extreme fatigue.  That fatigue can affect our mood. Societies throughout the world are experiencing an epidemic of depression during COVID. As part of the response to stress, alcohol sales have skyrocketed and that will have its long-term consequences.

We've also lost the ability to decompress, gather for spontaneous conversation, or spend hours immersed in a book in front of a fire. Our mobile devices have become an extension of our brains. Sometimes the best thing you can do is just be together without an agenda. It's what I call the gift of time.

I've described the five stages of COVID as isolation/PPE, testing/contact tracing, therapies/trials, vaccines/passports and transition to a new normal. My wardrobe is now complete with many different mask types. I have my contact tracing apps. I've co-chaired the collection of data for evaluating numerous therapies. I've received my vaccines and have a credential on my phone. That means it's time to transition to a new normal — thoughtfully and incrementally. I return to Rochester the first week of April, following Mayo's guidelines for masking, expected behaviors and Mayo Clinic work patterns. The Subaru I keep in Minnesota has a dead battery after a year of limited use. We'll fix that. I've used the Way.com platform to "rent" a parking space at the Courtyard by Marriott so I can keep the car at the airport for $4 per day, coming and going in alignment with evolving work patterns. I will need to restock my refrigerator since year-old pickles, peanut butter and pepperoncini do not make a balanced meal.   

I'm ready to restart the pattern of travel, resuming daily living activities in Minnesota and rebuilding what we lost during COVID while also holding the gains we made. I can honestly say that I have no regrets about our lives together over the past year. We did what needed to be done. We were strong but still admitted our vulnerabilities. We helped those around us on the journey and never made decisions based on self-interest. 

As we reflect on the past year, I think we can say that it changed our lives and made us more resilient for whatever comes next. This year will be one we tell our grandchildren about.

Wednesday, March 10, 2021

Vaccine Credentials Done Right

Vaccine credentials could be a safe and convenient way for people to resume a more normal daily life, but it’s critical that we do it in the right way. Public and private sector collaboration is essential, and we must be driven by science and our best understanding of what vaccines mean for immunity under different conditions. It’s reasonable to expect that some businesses, schools and countries may require it – just like yellow fever vaccine certificates.

So if we are moving toward those uses, we need to create a way patients can share their vaccination status if they wish to do so, with tools designed to be voluntary and not discriminate. But first we need to develop international standards for organizations administering the vaccines to make credentials available in a format that’s accessible, interoperable and digital. We’re working on that with other health care providers, EHR vendors, state immunization systems, pharmacies and tech companies. This work will enable people who have received the COVID-19 vaccine to access, store and share their records in a secure, verifiable and privacy-preserving way.

At this time, patients who have been vaccinated at Mayo Clinic can choose to show their vaccination status on their phones using a health app, if they wish to. The app will say they’ve been vaccinated, which vaccine they received and the date they got it. Over the next several months, the Vaccine Credential Initiative, a collaboration of academia and industry, aims to create a standardized way of proving immunization, using a displayable QR code. Very importantly, that standard format, called the SMART Health Card, has been endorsed by stakeholders throughout the U.S. The vaccine credential will be issued by the doctor’s office, hospital or pharmacy that gives the vaccine, and the data can be uploaded to an app on a phone. Name and birth date are included, but no other medical information is shared, and the vaccine credential is always under the control of the person who received the vaccine. The code sits in a digital wallet, just like a ticket to a sports event that gets scanned when you enter. And also importantly, the vaccine credential will be something that can be printed and shown in paper form, for those who don’t have or use smart phones.       

And to be clear, this effort is not about requiring vaccination but offering patients a verifiable way to share their vaccination status if they wish to do so. There is always a choice. Of course, we must address privacy concerns and questions that may arise and always ensure that we have the right protections in place so everybody is comfortable with how their health information is shared and used.

Again, it’s very important to recognize that these tools are designed to be voluntary and must not be used to discriminate. The efforts are well-meaning, but we must follow guidelines for appropriate use and ensure they are not used to limit access to jobs or essential needs like getting groceries or seeking health care. 

So, a vaccine credential could help reopen the economy, while not creating another divide as millions of people don’t have smart phones. The digital divide is real, and all our solutions must work on a variety of phones as well as on paper for those without technology. Tech tools may be preferable for many consumers, but we also have to consider how we’re going to handle those who don’t have ready access to a smartphone or other device. 

Although we don’t expect that most of the world to be vaccinated until the end of 2021, I wouldn’t be surprised if some businesses require proof of vaccination in the future – either on paper or digitally – just like some countries now require proof of a negative COVID-19 test to enter. Or people may have to agree to on-site testing or proof of recent quarantine. Our work in health care is to ensure that patients have the tools they need to support whatever approach they choose.


Tuesday, March 2, 2021

Listen Better, See Deeper

Combining Medical Attentiveness with Artificial Intelligence


John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.

Embracing an “ecology of attention” will significantly improve patient care, according to Mark Kissler, MD, at the University of Colorado.1 Kissler and his colleagues point out that clinicians spend much of their time multi-tasking and navigating around interruptions. While such juggling acts are often unavailable, it’s important to occasionally step back and ask: Is this the best use of my time? Equally important: Do the distractions cause “lapses in judgement, insensitivity to changing clinical conditions and medication errors.”1 If so, there are practical solutions that can help refocus our attention.

Kissler et al. offer several recommendations.  Initially, we need to recognize the difference between reachability and deep interpersonal availability. Most clinicians want to be reachable to help solve problems within their scope of practice, but spreading oneself too thin can jeopardize one's communication quality. Designing the physical spaces in which we interact with patients and colleagues is another area where we can build better attentiveness. For many years, the business and tech worlds believed that bullpens and shared office space would foster communication. Still, we are now beginning to realize that all the distractions may impede creativity and productivity. Finally, the University of Colorado team emphasizes the need to build attentiveness into one’s organizational culture: “Provide clinicians with the tools and language to prioritize attention in their daily practice.” That can be accomplished by developing a culture that encourages staffers to listen with curiosity, communicate with empathy, and remain open to others' perspectives, even when that perspective contradicts our understanding of the facts.

Of course, as every clinician knows, even the most attentive listener can still miss things. The medical interview can only uncover so much, necessitating a careful physical exam and diagnostic testing when appropriate. While imaging studies have always been a part of the diagnostic process, machine learning has taken these procedures to a new level, with companies like Zebra Medical, GE, Siemens, and AIDOC introducing useful services. AIDOC, for instance, has created a suite of services that combines three layers: an algorithmic layer, a product layer, and a clinically viable solution layer. All three are combined and implemented directly into the workflow. According to AIDOC, the platform reduces “turnaround time and increases quality and efficiency by flagging acute anomalies in real-time. Radiologists benefit from state-of-the-art deep learning technology that is "Always -on," running behind the scenes and freeing them to focus on the diagnosis itself.”

Surveys suggest a need for an always-on service to help radiologists cope with the unrealistic workload that many face daily. One study found that: “ Based on 255 uninterrupted eight-hour workdays per year, radiologists need to review one image every three to four seconds to meet workload demands.” The hectic pace likely contributes to misdiagnoses and loss of life. The diagnosis of lung cancer with imaging is one of the most challenging issues to contend with. It is estimated that misinterpreted chest X-rays are responsible for 90% of presumed errors in diagnosing pulmonary tumors.2  Mounting evidence suggests that ML-enhanced imaging data analysis may catch the disease at a much earlier stage, reduce hospital length of stay and health care costs, and save lives. For example, a prospective, randomized clinical trial that evaluated AIDOC-assisted CT scanning during the intracranial hemorrhage diagnosis found that algorithms gave clinicians an earlier heads-up.  Specifically, the researchers looked at 620 consecutive head CT scans. They collected the turnaround times (TAT) for positive ICH findings, i.e., how long it took to complete the CT scan to report the findings to clinicians who needed the results to make a treatment decision.3 Wismuller and Stockmaster compared TAT when CT results were flagged in radiologists’ worklists to CT results that were not flagged. When radiologists were told about the potentially dangerous findings early on, TAT was 73 minutes +/- 143 minutes, compared to 132 minutes +/- 193 minutes when they were left in the dark early on.

These prospective results were supported by retrospective analysis of a much larger data set. A study presented at the 2019 Society of Photo-Optical Instrumentation Engineers conference analyzed over 7,000 head CT scans from urban academic and trauma centers. Using convolutional neural networks, AIDOC generated a specificity of  99%, the sensitivity of 95%, and overall accuracy of 98% in diagnosing intracranial bleeds when compared to ground truth from expert neuroradiologists.4

Reports like this certainly don't imply that machine learning-enhanced algorithms will someday replace physicians. High-quality patient care will always require clinicians who are empathetic listeners. Nor do they suggest that AI will replace experienced radiologists. But they suggest that those who ignore digital medicine innovations will eventually be replaced by those willing to combine traditional approaches with emerging digital techniques that augment human decision-making.

 

References

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2. Del Ciello A, Franchi P, Contegiacomo A et al. Missed lung cancer: when, where, and why? Diagn. Interv Radiol. 2017;23:118-126.

3. Wismuller A, Stockmaster L. A Prospective Randomized Clinical Trial for Measuring Radiology Study Reporting Time on Artificial Intelligence-Based Detection of Intracranial Hemorrhage in Emergent Care Head CT. Presentation at SPIE Medical Imaging 2020 Conference, Houston, TX, February 15-20, 2020. https://arxiv.org/pdf/2002.12515.pdf

4. Ojeda P, Zawaideh M, Mossa-Basha M, et al. The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies. Proceedings SPID Medical Imaging 2019. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10949/2513167/The-utility-of-deep-learning--evaluation-of-a-convolutional/10.1117/12.2513167.short