Identifying the Best De-Identification Protocols

Keeping patient data private remains one of the biggest challenges in healthcare. A recently developed algorithm from nference is helping address the problem.

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

In the United States, healthcare organizations that manage or store personal health information (PHI) are required by law to keep that data secure and private. Ignoring that law, as spelled out in the HIPAA regulations, has cost several providers and insurers millions of dollars in fines, and serious damage to their reputations. HIPAA offers 2 acceptable ways to keep PHI safe: Certification by a recognized expert and the Safe Harbor approach, which requires organizations to hide 18 identifiers in patient records so that unauthorized users cannot identify patients. At Mayo Clinic, however, we believe we must do more.

In partnership with the data analytics firm nference, we have developed a de-identification approach that takes patient privacy to the next level, using a protocol on EHR clinical notes that includes attention-based deep learning models, rule-based methods, and heuristics. Murugadoss et al explain that “rule-based systems use pattern matching rules, regular expressions, and dictionary and public database look-ups to identify PII [personally identifiable information] elements.” The problem with relying solely on such rules is they miss things, especially in an EHR’s narrative notes, which often use non-standard expressions, including unusual spellings, typographic errors and the like. Such rules also consume a great deal of time to manually create.  Similarly, traditional machine learning based systems, which may rely on support vector machine or conditional random fields, have their shortcomings and tend to remain reliable across data sets.

The ensemble approach used at Mayo includes a next generation algorithm that incorporates natural language processing and machine learning. Upon detection of PHI, the system transforms detected identifiers into plausible, though fictional, surrogates to further obfuscate any leaked identifier. We evaluated the system with a publicly available dataset of 515 notes from the I2B2 2014 de-identification challenge and a dataset of 10,000 notes from Mayo Clinic. We compared our approach with other existing tools considered best-in-class. The results indicated a recall of 0.992 and 0.994 and a precision of 0.979 and 0.967 on the I2B2 and the Mayo Clinic data, respectively.

While this protocol has many advantages over older systems, it’s only one component of a more comprehensive system used at Mayo to keep patient data private and secure.  Experience has shown us that de-identified PHI, once released to the public, can sometimes be re-identified if a bad actor decides to compare these records to other publicly available data sets. There may be obscure variants within the data that humans can interpret as PHI but algorithms will not. For example, a computer algorithm expects phone numbers to be in the form area code, prefix, suffice i.e. (800) 555-1212. What if a phone number is manually recorded into a note as 80055 51212? A human might dial that number to re-identify the record. Further we expect dates to be in the form mm/dd/yyyy. What if a date of birth is manually typed into a note as 2104Febr (meaning 02/04/2021)? An algorithm might miss that.

With these risks in mind, Mayo Clinic is using a multi-layered defense referred to as data behind glass. The concept of data behind glass is that the de-identified data is stored in an encrypted container, always under control of Mayo Clinic Cloud. Authorized cloud sub-tenants can be granted access such that their tools can access the de-identified data for algorithm development, but no data can be taken out of the container. This prevents prevents merging the data with other external data sources.

At Mayo Clinic, the patient always comes first, so we have committed to continuously adopt novel technologies that keep information private.

Learning from AI’s Failures

A detailed picture of AI’s mistakes is the canvas upon which we create better digital solutions.

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

We all tend to ignore clichés because we’ve heard them so often, but some clichés are worth repeating. “We learn more from failure than success” comes to mind. While it may be overused, it nonetheless conveys an important truth for anyone involved in digital health. Two types of failures are worth closer scrutiny: algorithms that claim to improve diagnosis or treatment but fall short for lack of evidence or fairness; and failure to convince clinicians in community practice that evidence-based algorithms are worth using.

As we mentioned in an earlier column, a growing number of thought leaders in medicine have criticized the rush to generate AI-based algorithms because many lack the 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. A critical look at the hundreds of healthcare-related digital tools that are now coming to market indicates the need for more scrutiny, and the creation of a set of standards to help clinicians and other decision makers separate useful tools from junk science. 

The digital health marketplace is crowded with attention-getting tools. Among 59 FDA-approved medical devices that incorporated some form of machine learning, 49 unique devices were designed to improve clinical decision support, most of which are intended to assist with diagnosis or triage. Some were designed to automatically detect diabetic retinopathy, analyze specific heart sounds, measure ejection fraction and left ventricular volume, and quantify lung nodules and liver lesions, to name just a few. Unfortunately, the evidential support for many recently approved medical devices varies widely.

Among the AI-based algorithms that has attracted attention is one designed to help clinicians predict the onset of sepsis.  The Epic Sepsis Model (ESM) has been used on tens of thousands of inpatients to gauge their risk of developing this life-threatening complication. Part of the Epic EHR system, it is a penalized logistic regression model that the vendor has tested on over 400,000 patients in 3 health systems. Unfortunately, because ESM is a proprietary algorithm, there’s a paucity of information available on the software’s inner workings or its long-term performance. Investigators from the University of Michigan just conducted a detailed analysis of the tool among over 27,600 patients and found it wanting. Andrew Wong and his associates found an area under the receiver operating characteristic curve (AURAC) of only 0.63. Their report states: “The ESM identified 183 of 2552 patients with sepsis (7%) who did not receive timely administration of antibiotics, highlighting the low sensitivity of the ESM in comparison with contemporary clinical practice. The ESM also did not identify 1709 patients with sepsis (67%) despite generating alerts for an ESM score of 6 or higher for 6971 of all 38,455 hospitalized patients (18%), thus creating a large burden of alert fatigue.” They go on to discuss the far-reaching implications of their investigation: “The increase and growth in deployment of proprietary models has led to an underbelly of confidential, non–peer-reviewed model performance documents that may not accurately reflect real-world model performance. Owing to the ease of integration within the EHR and loose federal regulations, hundreds of US hospitals have begun using these algorithms.”

Reports like this only serve to amplify the reservations many clinicians have about trusting AI-based clinical decision support tools. Unfortunately, they tend to make clinicians not just skeptical but cynical about all AI-based tools, which is a missed opportunity to improve patient care. As we pointed on in a recent NEJM Catalyst review, there are several algorithms that are supported by prospective studies, including a growing number of randomized controlled trials.

So how do we get scientifically well-documented digital health tools into clinicians’ hands and convince them to use them? One approach is to develop an evaluation system that impartially reviews all the specs for each product, and generates model cards to provide end users a quick snapshot of their strengths and weaknesses. But that’s only the first step. By way of analogy, consider the success of online stores hosted by Walmart or Amazon. They’ve invested heavily in state of the art supply chains that ensure their products are available from warehouses as customers demand them. But without a delivery service that gets products into customers’ homes quickly and with a minimum of disruption, even the best products will sit on warehouse shelves. The delivery service has to seamlessly integrate into customers’ lives. The product has to show up on time, it has to be the right size garment, in a sturdy box, and so on. Similarly, the best diagnostic and predictive algorithms have to be delivered with careful forethought and insight, which requires design thinking, process improvement, workflow integration, and implementation science.

Ron Li and his colleagues at Stanford University describe this delivery service in detail, emphasizing the need to engage stakeholders from all related disciplines before even starting algorithm development to look for potential barriers to implementation. They also suggest the need for “empathy mapping” to look for potential power inequities among clinician groups who may be required to use these digital tools.  It is easy to forget that implementing any technological innovation must also take into account the social and cultural issues unique to the healthcare ecosystem, and to the individual facility where it is being implemented.

If we are to learn from AI’s failures, we need to evaluate its products and services more carefully and develop them within an interdisciplinary environment that respects all stakeholders.

A Paradigm Shift in Digital Health

Innovation is best scaled when pipelines are replaced with platforms.

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

The Digital Health Frontier includes cutting edge predictive analytics, machine learning enhanced algorithms and big data analytics. But for these innovations to have their full impact on patient care requires the right strategic and operational foundation. In the past, many technology-focused organizations have relied on a pipeline approach as the foundation to construct innovations and promote growth. But history suggests this approach is less sustainable than a platform approach. A recent article in Harvard Business Review (HBR) sums up the difference: “Platform businesses bring together producers and consumers in high-value exchanges. Their chief assets are information and interactions, which together are also the source of the value they create and their competitive advantage…. Pipeline businesses create value by controlling a linear series of activities — the classic value-chain model. Inputs at one end of the chain (say, materials from suppliers) undergo a series of steps that transform them into an output that’s worth more: the finished product.” While this explanation gets the point across, it’s rather abstract. To really appreciate the advantages of one approach over the other, we need an example or two.

Apple’s handset business follows the pipeline model, making sure there are adequate supplies available to build the device and then overseeing the various other steps to create a finished iPhone, as well as its distribution, sales, and servicing. But when Apple linked the phone to its App store, the situation changed dramatically, turning the operation into a sustainable platform that connected app developers with iPhone owners. In their HBR article, Geoffrey Parke and Sangeet Paul Choudary explain: “The resource-based view of competition holds that firms gain advantage by controlling scarce and valuable — ideally, inimitable — assets. In a pipeline world, those include tangible assets such as mines and real estate and intangible assets like intellectual property. With platforms, the assets that are hard to copy are the community and the resources its members own and contribute, be they rooms or cars or ideas and information.” Apple’s success and the loss of market share by pipeline-oriented companies like Nokia can be explained by such differences.

Like Apple, John Deere has successfully employed a platform approach. They own not just the physical assets — e.g. tractors and combines — but a vast collection of intellectual property — including APIs and apps to help farmers manage what is now being called precision agriculture. The company links third party providers and producers to their farming customers and reaps the benefits. With all these technological tools in place, farmers now have the ability to more efficiently monitor their equipment with data on fuel consumption, location, machine hours, and engine RPMs; and they can improve crop management with weather prediction data, community pricing and the like. Some of John Deere’s more advanced combines incorporate a grain quality camera, grain loss sensor, a Gen4 display monitor, and remote access to an operations center from inside the cab. For every new connected tractor sold, more data flows into the John Deere Platform, enhancing the value of the platform to partners creating new apps and analytics.

Mayo Clinic Platform (MCP) is taking a similar approach. Instead of creating dozens of pipeline businesses or building an organization chart to support pipeline businesses, we are leveraging external collaborators, network effects, and data flowing back to the Platform, which increases its value for producers of products and consumers of services. Mayo Clinic and Commure, a General Catalyst portfolio health care technology company, have launched Lucem Health to connect data from remote medical devices with AI-enabled algorithms. External collaborators who have partners with MCP include nference, Medically Home, Kaiser Permanente, and K Health. The strategic approach allows the Mayo Clinic Platform to offer products and services that fall into 4 broad categories of functionality: Gather, Discover, Validate, and Deliver. For example, in the Deliver “bucket” is the combined ECG/algorithm system that was recently validated and published in Nature Medicine. The digital tool was able to detect low ejection fraction, thereby improving the diagnosis of left ventricular systolic dysfunction. While the ECG/algorithm is improving direct patient care at Mayo Clinic, it can also be offered to external partners and embedded in their ECG waive form viewer, which in turn will improve the relationship between a community hospital and its patient population.  Similarly, the clinical data analytics tools developed by MCP are being made available to outside partners like K Health, which provides symptom checking, access to virtual visits with a clinician. The data analytics function is helping K Health improve its services to their clientele.

As the HBR article emphasized, the chief assets of a platform are information and interactions, which together are also the source of the value they create and their competitive advantage. Such value and advantages are what will sustain healthcare innovators through the next several decades.

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.

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.”

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.

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