key: cord-0874083-26som3m8
authors: Brahmbhatt, Darshan H.; Ross, Heather J.; Moayedi, Yasbanoo
title: Digital Technology Application for Improved Responses to Healthcare Challenges: Lessons Learned from COVID-19
date: 2021-12-01
journal: Can J Cardiol
DOI: 10.1016/j.cjca.2021.11.014
sha: 148b30b2b50a07ff17d6b9e717c6bfdaaaaa1426
doc_id: 874083
cord_uid: 26som3m8

While COVID-19 is still ongoing and responsible for over 5 million deaths, the scope and speed of advances over the last year in terms of scientific discovery, data dissemination and technology have been staggering. It is not a matter of “if” but “when” we will face the next pandemic; how we leverage technology and data management effectively to create flexible ecosystems that facilitate collaboration, equitable care, and innovation will determine the severity and scale. The aim of this review is to address emerging challenges that came to light during the pandemic in healthcare and innovations that enabled us to adapt and continue to care for patients. The pandemic highlighted the need for seismic shifts in care paradigms and technology with considerations related to the digital divide and health literacy for digital health (DH) interventions to reach full potential and improve health outcomes. We discuss advances in telemedicine, remote patient monitoring (RPM), and emerging wearable technologies. Despite the promise of DH, we emphasize the importance of addressing its limitations including interpretation challenges, accuracy of findings, artificial intelligence driven algorithms. We summarize the most recent recommendation of the Virtual Care Task Force to scaling virtual medical services in Canada. Finally, we propose a model for optimal implementation of health digital innovations with 5 tenets including Data Management, Data Security, Digital Biomarkers, Useful Artificial Intelligence and Clinical Integration.

Over the last century, the world has experienced a number of pandemics with varying impacts on healthcare and the global economy. The 1918 influenza outbreak was the most severe in recent history, resulting in an estimated 50 million deaths worldwide. A similar prevention approach was used during the beginning of the coronavirus 2019 (COVID-19) pandemic, with control efforts limited to isolation, quarantine and reducing the size of public gatherings. 1 COVID-19 is still ongoing and responsible for over 5 million deaths. During this time the scope and speed of advances in terms of scientific discovery, data dissemination and technology have been staggering. The World Health Organization (WHO) was first informed of the unusual rates of pneumonia in Wuhan, China on December 30th, 2019. Within 11 days, the genome of the coronavirus was sequenced and shared online for global collaboration efforts. 2 In this review, we address emerging challenges that came to light during the pandemic in healthcare delivery and innovations that helped health services adapt to COVID-19. We discuss the promise that digital health (DH) technologies hold and the importance of addressing its limitations in order to harness these innovations.

There have been a number of lessons learned from this pandemic that will prepare us for the next disruptor. It is not a matter of if but when we will face the next pandemic; how we leverage technology and data management effectively to create flexible ecosystems that facilitate collaboration, equitable care, and innovation will determine the severity and scale of future threats to our provision of healthcare.

The COVID-19 pandemic unmasked deeply-rooted inequities in health access and outcomes globally. Many contemporary studies have found that when adjusted for confounders, such as socio-economic status, insurance coverage and site of care, the differences in race are largely attenuated. Black, Hispanic and Indigenous individuals are over-represented in COVID-19 hospitalizations and mortality. In the USA, 1 in 390 Indigenous Americans and 1 in 555 Black American have died compared to 1 in 665 White Americans. 3 Members of these racialized communities are more likely to rely on higher risk employment, delay seeking health care due to financial constraints, reside in multigenerational housing, and receive unequal treatment once hospitalized. 4, 5 Despite stay-at-home orders to contain the spread of the virus, many essential workers were unable to work from home. Rogers et al, found that compared to Whites, Blacks were more likely to work essential jobs during COVID-19, including transportation, food preparation, health care support and cleaning services; thereby increasing their risk of exposure from the workplace and transmission in their respective communities. 6 Early on in the COVID-19 pandemic reports demonstrated increased morbidity and mortality among patients with cardiovascular disease (CVD). 7 These at-risk patients were advised to stay home as much as possible, to limit their chances of contracting COVID. However, this was not always possible for the reasons described above.

COVID-19 has seen a sharp reduction in hospital visits by patients with multiple comorbidities, those who historically had a higher utilization of hospital care than the general population. This suggests that both missed and postponed care led to a greater disparity in healthcare and may lead to poorer outcomes in the longer term. 8 

The pandemic highlighted the need for seismic shifts in care paradigms and technology used as a means to deliver this change. In the next section we evaluate challenges that emerged during COVID.

As there was a pivot to providing more care using telehealth technologies, both to protect patients by reducing their contact with in-person review and to mitigate extreme hospital workloads, a number of challenges related to uptake of digital solutions were encountered. These included access to reliable internet connections and a lack of readiness for adoption of virtual care in those from minority groups or older populations. 9

High-speed reliable internet has become an essential resource during this pandemic, whether to facilitate working from home, telemedicine, online purchases or social communication. It has recently been recognized as an additional social determinant of health. 10 Yet, only a quarter of Indigenous communities have access to broadband internet compared to 97% of urban households in Canada. 11 Similarly, in the US the digital disenfranchisement is highest among rural residents, Blacks and Hispanic communities and those with the lowest income (<$50,000/year). 12 The majority of access to internet services in Canada is through internet-enabled mobile devices.

Access to the internet from home reduces sharply for Canadians aged above 65 years. 11 Rural communities and First Nations reserves also suffer from a reduction in provision in broadband internet and LTE-mobile phone connectivity. 13 J o u r n a l P r e -p r o o f met during COVID, using vaccination as an example, with the initial lag, particularly among low-income and rural Canadian residents, who were more likely to have lower levels of health literacy. 20, 21 Data from Ontario in 2016 showed that 47% of provincial residents had low indices of health literacy, reducing their ability to navigate the healthcare system and be proactive in managing their own care. 22 These were important considerations in the evolving care paradigms seen during the COVID-19 pandemic.

To enable the evolution of patient care during a pandemic, there must be a change in how healthcare professionals (HCPs) deliver care. Few professional curricula include detailed education for doctors, nurses and other HCPs on how to successfully use digital technologies. The uptake of healthcare innovation is often championed by a few enthusiasts without consideration of how such technology might be best integrated into the routine workings of clinics. There is little included in medical curricula either at undergraduate or postgraduate levels or opportunities in continuing medical education to learn techniques required for providing routine care using digital technologies. In a recent self-reported survey of Canadian cardiology residents, respondents reported a lack of comfort and a need for telemedicine targeted education during medical training. 23 Accelerated specialty-focused telemedicine curricula have been feasible in the ambulatory pediatric setting and would be readily transferable to the adult setting. In this study, sessions delivered via Zoom included the basic principles of telemedicine communication, technical skills, and physical examination. Direct observation by attendees was also developed for evaluation purposes. 24 There was a significant increase in residents' self-reported efficacy in performing key components of telemedicine visits. Another study J o u r n a l P r e -p r o o f demonstrated that undergraduate medical students taught using DH technologies were more familiar with them in their later clinical practice and more able to use such technologies in their career compared to peers who did not receive DH-based lessons during their training. 25 Beyond simply incorporating important digital health and digital learning competencies into educational frameworks, such models of education will need to incorporate rapid knowledge translation and dissemination of new concepts and care paradigms that emerge at great speed in response to fresh challenges.

It is reassuring that early reports show that a move to telehealth and virtual clinics did not increase overall morbidity or mortality. 26 However, translating these digital innovations to leverage improved outcomes compared to previous models of care in the longer term remains to be seen. 27 For success we will need a workforce trained in digital healthcare delivery and leaders with a vision to deploy changes in practice, for DH to reach full potential.

Pivoting to Telemedicine during COVID Telemedicine has been in place for decades in Canada, while long on promise, it has been short on delivery. According to the Canadian Institute for Health information, pre-COVID-19, virtual care represented 0.15% of the 270.3 million billable services. 28 Virtual care was mostly used in the private sector domain, which offered services directly to patients and providers for a fee. There was tension between patient benefits (increased access to care, cost efficiency, convenience) and provider barriers (lack of reimbursement, jurisdiction licensure restrictions and lack of interoperability with various electronic health records). 28 COVID-19 resulted in J o u r n a l P r e -p r o o f universal adoption of telemedicine, effectively overnight. This was due to the need for a steep decline in face-to-face ambulatory visits due to pandemic restrictions, and was supported through the use of temporary provincial billing codes. 29 In the post-COVID-19 era, it is anticipated that patients will derive benefit from "blended" care with both in person and virtual care (including telemonitoring, advanced artificial intelligence (AI) and algorithm-based care) where appropriate.

The pivot to virtual visits during COVID-19 was necessary to provide appropriate and timely care. After COVID-19, we will need to better understand appropriate patient selection for virtual care and remote patient monitoring (RPM). Moreover, we will need to appropriately evaluate the outcomes and quality in order to define what is 'good' virtual care. A recent perspective article entitled, "Remote Patient Monitoring -Overdue or Overused" highlights that while RPM can reduce cost by reducing preventable admissions and offer convenience and heightened surveillance for clinical events, there is a desperate need for research to further differentiate which patients would most derive benefit after COVID-19. 30 In the next section, we will describe the pearls and pitfalls of RPM using HF as the canonical example.

The care of patients with HF requires frequent clinical assessments and laboratory investigations to initiate and titrate up guideline-directed medical therapy (GDMT), and for the surveillance of acute decompensation. RPM engages patients as equal partners in their care and should therefore theoretically appeal to care providers and to patients with HF. 31 Despite general enthusiasm, the majority of trials that depended on traditional physiological metrics (weight, blood pressure and heart rate) have not improved J o u r n a l P r e -p r o o f outcomes (Table 1 ). In the Tele-HF study, among 1653 high risk patients, collecting daily weights and symptoms and providing daily coaching did not result in a reduction in hospitalization when monitored for weight and symptoms compared with usual care. 32 In the first Telemedical Interventional Monitoring in HF study (TIM-HF), despite improved adherence rates (80% of the interventional cohort had at least 70% of daily data transfers), there was no difference in all-cause mortality. 33 Common themes in these neutral trials are low adherence to the technology, poor data accuracy and either delays or lack of actionability on data received. 34 For instance, in the Tele-HF trial, 14% of patients in the active treatment arm never used the monitoring device. Furthermore, by the end of the study, only half of the patients in the active arm were using the device three times per week as instructed. 32 Implant-based RPM technologies used in HF have been shown to be useful for monitoring patients, but there have been challenges with costs, workflow and responding to alerts consistently across trial populations. 35 There are often few designated protocols for action after an alert is received. This reduces the effectiveness of the system if appropriate alerts are not followed by equally appropriate alterations in patient management, including altering medications or reasserting the need for adherence to lifestyle measures such as limiting dietary intake of salt and water.

Successful remote monitoring requires adherence to schedules of data transmission: it requires the patient to actually use the technology. For example, in one of the only positive telemonitoring trials, the second Telemedical Interventional Management in Heart Failure II (TIM-HF2) trial, 97% of patients were 70% compliant with daily data transfer. In this trial, 1571 patients were randomised to either usual care or usual care supported by RPM. Overall, there was a borderline significant reduction in the J o u r n a l P r e -p r o o f primary endpoint of days lost to death or cardiovascular hospitalisation from 6.6% in the usual care group to 4.9% in the RPM group (ratio 0·80, 95% CI 0·65-1·00; p=0·046) but no significant difference in mortality. Interestingly, in extended follow-up one year after RPM was stopped, there was no longer a difference between patients previously managed with RPM and those receiving usual care, suggesting that the effect of RPM only occurred while the technology was in use. 36 This suggests that sustained benefits to patient care require ongoing engagement with RPM by both patients and clinicians.

J o u r n a l P r e -p r o o f 

As the world of DH continues to grow, a number of opportunities for patient management are emerging. These technologies were not as widely used during the pandemic as RPM for HF, nor have they yet provided solutions to manage the workload associated with COVID-19, but they all have potential for improving care in the future. In this next section we review wearable devices, which can provide continuous monitoring of patients and the role of AI in patient management.

The interest in using technological innovations such as "smart wearables" to monitor health has grown significantly over the last five years. 50 Smart wearables are consumer grade devices with sensors that can be worn as an accessory or J o u r n a l P r e -p r o o f embedded into clothing. These devices include smartwatches, rings, wristbands and pedometers. Common sensors in smart wearable devices are summarized in Table   2 . Data from these devices can be processed through software algorithms to provide potentially important insights into personal health. Contrary to traditional medical models, wearables have been introduced to consumers before validation of effectiveness, safety and reliability in the health care setting. 50, 51 Currently 21% of American adults regularly wear a smartwatch or wearable fitness tracker. 52 Use is more common among women than men (25% vs. 18%), among Blacks/Hispanics than Caucasians (23% and 25% vs. 20%) and among those with a higher income (31% >$75,000 vs. 12% <$30,000). 52 

Physical inactivity is associated with an estimated 5 million deaths per year worldwide. 54 Increasing physical activity levels at the population level could have a substantial effect on chronic disease and increase longevity. The lockdowns and stay at home orders during COVID-19 reduced the opportunity for the general public to participate in organised sport or even attend fitness centres. Early reports indicate that patients were enthusiastic in using DH solutions to maintain their activity levels during COVID-19, and it is likely that this will continue beyond the pandemic. 55 However, pre-pandemic studies evaluating the efficacy of wearables have been mixed. In a randomized control trial conducted to address weight loss using a wearable tracker in 470 young adults over 24 months, the intervention group lost less weight than the standard group. 56 Simply wearing a device tracker may not offer an advantage over standard behavioral approaches. A wearable tracker coupled with gamification has shown more effective results in the short-term for improving physical activity. The Behavioral Economics Framingham Incentive (BE FIT) study was a non-competitive team-based intervention which resulted in a coffee mug as a prize over a 12-week intervention. The intervention group reached a higher step count compared to the control group, but the effects tapered during the postintervention period. 57 A systematic review has shown that wearable physical activity monitors can increase cardio-respiratory fitness, but have little effect on sedentary time of individuals, limiting its benefits. 58 Cardiac rehabilitation, already an underutilized resource, saw further limitations to access imposed by the COVID-19 restrictions. Over 50% of Canadian programs ceased to provide any care during the first wave of the pandemic. 59 Virtual care rehabilitation, however, became a priority during this time, with the rapid delivery of cardiac rehabilitation to high-risk populations. Successful programs were able to leverage home monitoring with individualized exercise prescriptions. Wearable devices were able to be used to monitor "moderate" activity, albeit slightly differently depending on the wearable. 58 Using a baseline step count could support clinicians determine weekly targeted increases in both step count and heart rate goals. 60

Physical activity is an important assessment in patients with HF. Traditionally clinicians used the New York Heart Association (NYHA) functional class. However, this profiling is subjective with poor clinician interobserver reliability. 61 In a feasibility J o u r n a l P r e -p r o o f study, we found that a daily step count of 5000 can differentiate between a patient with NYHA class 2 and 3 symptoms. 62 Further, a study of 170 patients with HF found a step count of ≤4,889.4 steps/day to be a strong and independent predictor of prognosis (HR 2.28, 95% confidence interval: 1.31-6.30; p = 0.008). 63 The Ted

Rogers Understanding Exacerbation of Heart Failure study (TRUE-HF; NCT05008692) will prospectively assess whether smartwatch physiologic and sensor data alone or in aggregate can predict objective cardiopulmonary exercise testing among 200 HF patients. 64 Wearable technologies will also be instrumental in optimizing guideline-directed medical therapies, but their utility during the pandemic has been limited. The NanoSENSE study (NCT03719079) is investigating the SimpleSENSE monitoring undergarment and closed-loop machine learning platform (Nanowear Inc., NY, USA) in 500 patients across 5 US centres to validate its ability to identify patients at risk of HF decompensation. 65 The cloth-based nanosensor array incorporated into an undergarment allows monitoring of cardiac output and stroke volume alongside thoracic impedance, posture and activity levels to provide a multi-parametric signal of changes to a patient's HF state. With further evidence to support their efficacy, wearables may play a more useful role in future pandemics.

Atrial fibrillation (AF) lends itself well to remote monitoring in light of its paroxysmal nature and critical need for stroke prevention. Smartwatches with PPG technology coupled with algorithms to detect irregular rhythms may become integral in the screening of AF in targeted populations. During the pandemic, TeleCheck-AF was a multicentre international project launched to maintain care for patients with AF in 25

European centres. By using teleconsultations supported by an on-demand PPGbased heart rate and rhythm monitoring app (FibriCheck®), clinicians were able to monitor and adjust medications in the follow-up of these patients. Initial deployment of this technology was reported to be uncomplicated but long-term outcome data is still pending. 66 Previous studies have shown some utility of wearables in detecting arrhythmia. The Huawei Heart Study enrolled 187,912 individuals to screen for AF using a band or a wristwatch. Overall, 0.23% participants received an irregular heart rhythm notification. The subsequent ECG resulted in a positive predictive value of 91.6%. 67 The Apple Heart Study was a fully virtual study, enrolling 419,297 individuals to screen for AF using a PPG-based smartwatch. An irregular pulse was found in 0.52% of the participants. In patients who had an irregular heart alert and returned the ECG patches, 34% had confirmed AF. 68

Hypertension remains an important risk factor in CVD and early experience of the pandemic saw a drastic reduction in patients seeking review and optimization of antihypertensive therapies. One study reported a 50% reduction in primary care office visits for blood pressure checks, with no difference between those with good blood pressure control and those with uncontrolled hypertension. 69 Technologies allowing home monitoring of blood pressure could help provide care to these patients where in person care is deferred.

Unfortunately, a recent review of home blood pressure measurement devices found that only a small fraction of these devices are validated. The majority of these devices are sold in online marketplaces, with 972 unique devices identified from 59 individual businesses. Of the 532 wrist-wearable devices identified none had been validated for accuracy or performance, thus reducing the potential effectiveness of blood pressure monitoring for users. 70 

Recommendations focus on development of blood pressure management programs, rather than the measurement devices themselves, advocating for a closed loop monitoring system that can automatically send data to the clinic to inform ongoing patient management. 71 New technologies are emerging, with promise shown in using smartphone videocapture to estimate blood pressure using transdermal optical imaging. While still in its infancy, better ways to detect and intervene earlier may provide improvements in preventing subsequent complications of hypertension. 72

Continuous blood glucose monitoring devices, with or without coupling to insulin delivery pumps have been FDA approved for nearly a decade. 73 Used to measure capillary blood glucose levels in patients without intercurrent illness, these devices allow patients with diabetes to assess their glycemic status, receive automatic alerts when out of range and guide their diabetes management. There was no widespread deployment of these devices during the pandemic, but the review of data from these devices suggested better diabetic control during COVID-19 instigated lockdowns. It remains unclear whether this was due to better diet and self-care generally, or whether lockdowns provided individuals with an opportunity for better focus on their own health. 74 Artificial Intelligence AI is defined as the ability of a machine to imitate intelligent human behaviour, whereas machine learning (ML) is the application of AI that allows a system to automatically learn and improve from experience. Deep learning is a subset of machine learning which involves the application of ML that uses complex algorithms J o u r n a l P r e -p r o o f and deep neural networks to train a model. 75 AI could provide support in a future pandemic through enhancing clinical assessment by the non-specialist, when access to specialist resources is limited by restrictions on travel or capacity at specialty providers. Technology-enhanced medical examination devices, such as the EKO digital stethoscope provide an opportunity for enhanced clinical assessment. By training a deep neural network to identify murmurs from recording through the device coupled with underlying echocardiographic abnormalities, the device could identify common murmurs with an acceptable positive predictive value. Such tools could aid learners developing their skills and assist non-experts to obtain useful clinical information to aid decision-making from a distance. 76 Within cardiology, echocardiography is a highly specialized field that is infrequently available in rural/remote settings due to the absence of trained personnel. AI to guide the acquisition of images is a novel focus, particularly with the emergence of One of the most relevant limitations of this review was the inclusion of discontinued and outdated devices such as Apple Watch Series 2, which is several generations behind the currently available Apple Watch Series 6 device. 78

There have been a number of questions raised over the accuracy of wearable devices, particularly PPG technology. In the WATCH AF study, 22% of participants were excluded from analysis due to poor quality PPG recordings. 79 The detection of AF is always more challenging at higher heart rates, therefore, the effectiveness is often context-dependent, a challenge in detecting arrhythmia which has a low incidence across the general population, while older populations who have a higher incidence of AF are less likely to be using wearable devices. 80 Further areas of inaccuracies with PPG technology deriving heart rate and oxygen saturation relate to 1) skin tone 2) motion artifacts and 3) signal crossover. 81 

Machine learning was used during the pandemic, largely to identify early signs of COVID-19 illness. Many of the reported studies were hampered by methodological flaws that prevented generalizability. Algorithms used small datasets with low-quality data, limited diversity, and in certain reports the same data set was used for training and validation. 85 These limitations mirror the experience of ML-driven cardiology research. In 2020, nearly 1 in every 1,000 new papers indexed on Medline was about AI and/or ML in cardiology. 86 Despite the early excitement, even from the World Health Organisation, of using AI to deal with the pandemic, it provided few useful solutions. 87 Patient populations included in training databases still do not accurately reflect the populations being treated in routine clinical practice, a feature that has affected conventional clinical trials for decades. 88 Until these data sources J o u r n a l P r e -p r o o f are inclusive and bias-free, the algorithms developed based on them will remain flawed. AI-enhanced clinical decision tools could help us achieve more meaningful and efficient interactions with patients. Unfortunately though, current AI technologies in cardiology are still at the "hype" stage and will require rigorous validation with robust scientific research in diverse populations to demonstrate safety and generalizability. 89

There is a critical role for an implementation plan at the pre-prototype stage as a roadmap outlining the timelines, resources and deliverables. 90 Table 3 .

J o u r n a l P r e -p r o o f 

Without consideration of how these technologies can be integrated into the clinical pathway for patients, take up of innovation will be sub-optimal. Issues such as data management, storage, workflow for analysis, clinical decision making and enacting changes in management plans need to be addressed. How data filters into the current model of multi-disciplinary management of patients has yet to be tackled. It may be that current models of care are inadequate for dealing with future challenges, and these models will need to be proactive to maintain efficacious. The final, but noless-important, driver of change in care pathways is the remuneration of clinical activity undertaken by both the HCP and the healthcare organisation. Without adequate reimbursement, there will always be barriers to implementation. By demonstrating the value to the healthcare economy of digital innovation through J o u r n a l P r e -p r o o f appropriately designed clinical evaluation studies, regulators and payers should be willing to embrace novel, evidence-based technologies.

COVID-19 will be remembered for the devastating number of deaths it caused, the long-term health consequences, and the pernicious socio-demographic divide it unmasked. It will also be remembered as a time of opportunity, where unprecedented digital solutions were rapidly implemented into clinical practice and medical education. Digital health-technology needs to be held to the same rigorous standards as current healthcare tools with embedded quality measures to promote high-quality care, ensure that devices and digital biomarkers are valid, AI is ethical and clear, confirm patient data can be securely stored and transferred to clinicians

where it can provide a meaningful trigger to engage evidence-based interventions to change patient management. Digital health has all the potential to deliver safe, efficient and effective care to the population as a whole, reaching parts of society who have previously been underserved to try and reduce health inequity at the same time.

Fast forward to 2025: we are now living in a post-COVID era with nearly 6 billion people vaccinated. A new "normal" has emerged, a transformed healthcare system.

We follow over half of our patients using digital technologies as stand alone or in blended model of care. Reimbursement for virtual care is a permanent fixture.

Technology is increasingly addressing health inequity and disparities in care. We hope.

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