Path A: Creating Digital Operations and Processes

Path A creates and integrates digital operations and processes delivering on the customer value propositions. Customers include the ecosystem in healthcare, including patients, consumers, clinicians, hospitals, providers, health services companies, and more. In this path, organizations focus on changing existing processes and their legacy systems supporting those processes to a digital state. Path A concentrates mainly on the as-is state to a future state of current operations and processes.

Healthcare digitization is held hostage to several IT applications and systems. For example, healthcare electronic medical records (EMRs) and electronic health records (EHRs) systems provide digitized records of a patient’s healthcare encounters. Making a record of patient encounters digital represented state of the art at one time. Today, time spent on EHR systems reduces the amount of time physicians spend with their patients, negatively affecting patient relationships. Storing data is not a clinical tool; it doesn’t provide healthcare. A Harris Poll¹ conducted on behalf of Stanford Medicine delivers a treasure trove of AI use cases for EHRs: disease diagnosis, disease prevention, and population health management. In this study 9 of 10 physicians want their EHRS to be intuitive and responsive, a perfect storm for AI adoption. Short term improvements include voice as a modality acting as a scribe during patient’s visits, a highly viable AI engineering effort using deep learning and natural language processing.

In many cases legacy systems must address poor system availability and stop the mindset that systems which read and write data cannot have the same reliability and availability seen in the platforms of the large technology companies, Google’s search engine or Amazon’s commerce site or Netflix streaming videos. High availability, high reliability and use of mobile and cloud technologies remains

Path B: Building New Capabilities

Path B builds a new set of capabilities around the desired customer end state and operating model in lock-step with Path A. Path B means thinking about how to use the Internet of Things and AI, creating new products and services. The ever-growing number of medical devices embedded with AI that live in homes or on the body makes creating new healthcare products and services more possible. Path B enables enhanced healthcare experiences but redefining even better experiences and healthcare outcomes.

Path C: Transforming Business Processes

Path C transforms business processes. Often requiring taking an enterprise view and eliminating duplicative processing or reimagining the new process. This path is fraught with risk and high rewards. Path C includes moonshot projects or as Safi Bahcall writes, Loonshots, crazy ideas that cure diseases, and transform industries. The Digital Twin app is a moonshot where the healthcare system engages consumers and people. The number of moonshots is a long list starting with real time healthcare.

AI Applied to Digital Healthcare

Elevating individual responsibility through AI helps create digital healthcare. AI adds another dimension to your care. Using the same example, hypertension, AI now points out to you as a patient and your doctor that a decrease in weight of 20 lbs can control your blood pressure without medication. In the office, a clinician is usually harried and potentially running behind schedule with numerous demands on their time. For example, physicians see patients, supervise other clinicians, such as students, physician assistants, responding to patient emails, phone calls, responding to insurance coverage issues, billing complaints, all while covering for other physicians for these same responsibilities who are out of office. Unfortunately, it is easy for patient care to sometimes slip between the cracks—patient care increases where AI’s role eliminates or streamlines these non-clinical tasks. AI fills the role of safety net for the clinician to ensure appropriate and timely care is not accidentally overseen.

AI can serve many functions in the clinical space. AI can perform as a safety net in the above example, reminding the clinician that counseling and perhaps enrollment in a health and wellbeing program for weight loss may help with non-medication management of hypertension. Also, AI can be used to provide doctors with reminders about best practice management for chronic conditions.

Your blood pressure is well controlled on medication therapy. You also start working on healthy weight loss and regular aerobic exercise. Your connected device provides timely input to your doctor that your regimen is working well, and your blood pressure is now not only controlled but borderline low. AI is used to identify abnormally low blood pressures, taking both the data from your biometric device and your input from your iPad that indicates you have been feeling dizzy intermittently. AI analyzes all this information and research of the clinical database and hypertension management guidelines to form a recommendation for intervention by your doctor.

Your doctor takes this packaged and easily digestible information to make an immediate decrease in your medication dosage. Over time, with close connected monitoring and AI assistance, you can stop all medication therapy. Your hypertension has resolved as long as you continue to adhere to your healthy lifestyle.

As one ages, your blood vessels become less compliant, genetics may catch up to you, and you may require medication therapy again. In your entire lifecycle with this condition, from elevated blood pressure to fully diagnosed hypertension to resolution of hypertension to chronic hypertension AI and digital connections have been used to provide personalized care with optimal outcomes.

AI, Digitization, and Corporate Healthcare Strategy

Predictably large companies are joining the digital healthcare bandwagon,such as Google with its deployment into the wearables market with Google Wear, and Fit (a health platform launched in 2014). Moreover, Google recognizes the impact of AI on digital healthcare and has been exploring and has created ventures that include the DeepMind unit, where AI is used to identify eye disease from imaging scans. Google’s parent company, Alphabet, has a health sciences arm named Verily. Verily is working on such exciting projects as the Aurora study, where AI is being used to identify physical biomarkers of mental trauma. They created their wearable, specifically for this study.

While some companies are investing in future AI with digital solutions, Apple, on the other hand, has focused on current-day applications. The Apple watch has been expanding the watch’s health care uses with additions of falls detection and an ECG monitor. The corporate goal is to create other offerings for Providers (such as Apple’s Health Kit, which we’ll discuss in the next sectiono) as well; the Apple watch would be the relied upon device to connect patients to their Providers for care. Amazon, in partnership with others, has created its own healthcare company, Haven, in an attempt to reimagine the healthcare market and how technology is used.

Corporate interest in healthcare is expanding for a variety of reasons. Healthcare has traditionally been recession-proof with high spending, and growing demands on healthcare resources lead to the need for cost-saving strategies. To date, the market appears to be focused on preventive strategies,such as the one given in our example above. The deterrent aspect would be where an overweight or obese patient is identified before the development of hypertension. Other preventive strategies using AI and digital health are being applied to chronic diseases like diabetes and hypertension. Preventing disease is much less costly than treating illness, and we’ll explore that topic further in the next section.

Digital AI and Prevention

Preventive medicine or lifestyle Digital AI solutions abound. They range from stress management solutions to monitoring of data associated with our health trackers to indicate a potential risk for disease development with counseling on management and prevention.

Weight management, stress, sleep, exercise, financial support are all the major categories of digital AI that are in place today. Consumers/patients use this technology to assess and take ownership of their health. Status today is that the majority of consumers are comfortable and embrace this technology for wellness.

Not only can hypertension possibly be averted through prevention with exercise, healthy weight, and diet, but other conditions can be as well. The range for the prevention of diseases is broad. To use another example, our digital AI can currently monitor our sleep. Rather than a patient being required to spend the night attached to monitors at a medical office, at-home sleep studies are now the standard. Digital AI has the capability of identifying abnormal sleep patterns or restless sleep. AI applied to this digital data, and overweight or obese status taken into consideration with alcohol intake, which may worsen sleep disorders can lead to assessments that notify the patient that discussion with a doctor is needed. An early sleep disorder is identified, or the consumer can change their lifestyle, modify their diet with weight loss, and have immediate data available to them and their provider on the management of their possible sleep disorder. The Apple Watch ECG is being used to identify potential heart rhythm disorders that predispose to stroke. The examples are numerous, and the extension from the use of digital AI for preventive medicine naturally extends to chronic disease management once it develops.

Digital AI and Chronic Disease

Type 2 Diabetes and hypertension are two of the most common chronic medical conditions today. The US Centers for Disease Control and Prevention (CDC) report that 1 in 9 Americans have Type 2 Diabetes, and 1 in 3 Americans have hypertension. Each of these conditions is a significant risk factor for heart disease, which remains the leading cause of death in America. 7 out of 10 people with their first heart attack have hypertension, and people with diabetes are twice as likely to have heart disease or a stroke as compared to people without diabetes, and at an earlier age. 60% of patients who have hypertension also have diabetes, and the health risk increases exponentially with the development of long term nerve, eye, and kidney damage.

Interestingly, diabetes and hypertension can be “silent” for a prolonged period until a crisis is reached. People with diabetes may slowly develop increased thirst and urination and not know that this may indicate an underlying disease state. Similarly, hypertension is known as the “silent killer” as frequently patients will present with stroke or other neurological symptoms later tied back to undiagnosed and untreated hypertension. Both are managed through a healthy diet, lifestyle, and medication therapies.

Patients self monitor their blood sugar levels and blood pressures. This data input to health care providers and systems can be enormous through the use of connected devices. Digital AI health solutions can track, understand, and report results from this patient-generated healthcare data (PGHD). For example, digital AI has been developed where abnormal results are sent to healthcare management systems that can then reach out and provide telephonic counseling to improve disease management. Alternative interventions through digital AI include the use of apps and virtual consultants to address abnormal results.

Besides digital AI to enhance chronic disease management, digital AI can also promote and improve self-education for the patient. For example, a patient can enter a query or question regarding their diabetic diet and what is allowed intake. The chatbot then utilizes AI and identification of keywords to provide advice on diabetic diet to the patient. In cases where keywords are not identified, or the question is not understood, the chatbot will direct the patient to their provider. PGHD is of benefit to providers as well. Doctors now have access to close to real-time data to perform micro-adjustments to their patients’ care, which leads to avoidance of complications of the disease and associated cost avoidance.

Digital AI provides benefits to healthcare payers as well. As mentioned in the telephonic case management program above, patient members are given customized guidance and counseling. At the same time, clinical decision support is used to guide the patient to the next best step in care action. Providers may be contacted for care integration. All of this leads to enhanced disease management and improved quality of life without requiring the patient to self manage, identify, or necessarily understand the underlying connections leading to integrated care and improvement in their disease management.

Through these digital AI modalities come several other benefits: health care costs are decreased, and quality of life increased for patients, increased accessibility and application of data to improve disease outcomes, decreased cost from complications associated with the unmanaged or mismanaged disease, permanent end-organ damage is mitigated by improved management, loss is avoided from hospital or emergency department visits that do not occur because digital AI allowed intervention and care before a crisis.

Digital AI and Mental Health

Digital AI is not limited to medical conditions alone. Mental illness is common in the US. Nearly one in five adults live with a mental illness, that’s 46.6 million people in 2017 per NIH statistics. There are current apps that allow AI, digital healthcare, monitoring behavioral health via monitoring one’s smartphone to follow how many social interactions have occurred that day, including social network site visits, texts, calls, physical activity level, and general smartphone use. That data is then analyzed by AI to determine if depression or other behavioral health disorders are on the increase, or managed. In this way, Digital AI allows self-assessment and connection to one’s provider for close to real-time monitoring of one’s mental health. Some chatbots provide mental health counseling and apps for cognitive behavioral therapy. The development and use of these tools is essential as there is a growing gap between the amount of mental health care needed and the number of providers who can care for them.

Technology usage for mental health care is on the rise, and people’s comfort level is growing. Digital AI for mental health makes sense as more consumers are taking responsibility for their mental healthcare. Again, the COVID 19 pandemic brought digital technology to the forefront as people were in isolation, with their states shut down. Estimates show the mental health burden rose in proportion to the prolongation of isolation associated with social distancing during the pandemic. Because the population was encouraged to stay at home and avoid social contact, digital healthcare rose. COVID 19 may inadvertently impact and drive the increased utilization of digital technologies based on necessity. Time will tell if the trend continues, but we believe the pandemic has changed the healthcare paradigm, Digital Healthcare made possible with AI.

Digital AI and Telemedicine

To reiterate, digital AI takes in all the data from connected devices which is then processed by AI to provide quick and timely interventions in healthcare, all of which is supported by AI analysis of the most up to date and relevant treatment options. Connected devices facilitate remote delivery of care, or telemedicine. Telemedicine, as evidenced by the COVID 19 pandemic has broad reach and has made billions of users aware of a new modality of healthcare. Digital AI has a special and significant role in telemedicine.

The transformation to our healthcare system is already happening. COVID 19 was the impetus to drive a rapid and exponential increase in utilization of telemedicine. Digital AI is being used in decreasing hospital readmission programs. In one hospital readmission reduction program, a patient is given a wifi enabled device at discharge from the hospital that transmits important patient data, such as vital signs (heart rate, blood pressure, temperature, etc) to their providers for ongoing outpatient management. AI is constantly monitoring this data and at the sign of any abnormality both the patient, their provider and any other caregiver can be notified to address this finding and hopefully avert a hospital readmission or emergency department visit. It’s all about timely intervention. An example of just such a partnership in the market currently is a pilot program between NHS (National Health Systems) hospitals at Dartford and Gravesham working with Current (formerly Snap40) aimed at remote monitoring of patient vital signs with AI analysis. The patients in this pilot were fitted with wi-fi enabled armbands and given a chatbot equipped tablet for medication reminders and remote communication with their providers. The patients received all their tools prior to discharge from the hospital. In this manner, provider and care teams were able to keep close remote eyes on their patients with the ability to perform micro-adjustments to care that kept patients healthier and out of hospitals, but still provided quality care.

Telemedicine has now been widely accepted and can provide the clinical support to the world of data that digital AI has opened. This integration provides numerous benefits:

• early diagnosis and timely intervention

• personalized care, and

• remote patient monitoring on a real time basis.

We’ve discussed early diagnosis and treatment several times throughout the book. Again, early diagnosis allows for interventions either in lifestyle, habits, diet or with medications prior to complications of disease development and prior to development of long term damage to the body related to uncontrolled disease. Traditionally, patients would have symptoms and no other associated information leading them to seek care at a doctor’s office. With digital AI symptoms are paired with personalized patient data generated by the patient’s connected devices. Therefore, telemedicine providers have more information at the time of patient evaluation. These teledoctors have: the patient to provide a description of their symptoms, data from connected devices with AI analysis pointing out any abnormal findings, and AI assisted analysis of clinical treatment guidelines, to ensure the most up to date and best care, or best next treatment, or evaluation step for each patient based on their own individual data.

Personalized care via digital AI is possible as all data generated from connected devices or alternate data input from other devices, such as ipad with symptom generators and checklists, are derived from an individual patient. Thus, doctors can make interventions or treatment decisions based on an individual patient’s data. For example, traditionally, someone with possible high blood pressure or hypertension would be seen in the clinic with maybe some accompanying blood pressure readings from pharmacy blood pressure machines. Their blood pressure would be taken in the doctor’s office as well. If abnormal, then a treatment decision is made as to whether the patient chooses to pursue diet and exercise, or if severe enough, to pursue medication therapy. The patient then continues to check their blood pressure outside of the office and has a follow up appointment with their doctor in a few weeks to months. At that point another treatment decision is made as to whether to pursue more aggressive therapy, to continue current treatment, or decrease current treatment. Digital AI allows abnormal findings to be sent in close to real time to their doctors for early intervention and management. Micro-adjustments and their impact on the disease are immediately available and allow for finer management based on individual patient data. In addition, AI can enhance the treatment algorithms to ensure the best quality of care and best next step in management for each individual patient. These treatment plans are then monitored through digital AI to ensure adherence, control of disease, and if any change occurs (such as an ability to lose weight resulting in need for less medication) treatments are changed accordingly.

Remote monitoring is a relatively new concept. Traditionally, in the hospital a set of vital signs are obtained on a scheduled basis. If the patient is sick enough, then monitors may be placed such that real time monitoring occurs and alarms go off for abnormal readings leading to clinical evaluation and possible adjustments in treatment. In the outpatient setting, there was no routine monitoring of any vital signs except in rare cases and this was done by the patient either having further appointments to have monitors placed on them for home evaluation, or patients having to stop in to clinics or other healthcare sites for follow up readings. Digital AI has transformed the process in the outpatient setting where devices are common and use has increased significantly. Now a constant stream of data in close to real time are available to both the clinician and the patient. Any abnormalities are analyzed by AI and early intervention and adjustments can be made. Timely intervention is key as timeliness is what prevents long term complications, and allows for fewer intolerable side effects of treatment (as monitoring would identify say too low a blood pressure).

Telemedicine is also quickly evolving almost in symbiosis with digital modalities. Co-evolution is a process whereby species develop based on the strengths of other species that support and help the dominant species to succeed. Consider humans and dogs. Humans were physically weaker, and dogs provided protection and support while humans returned dog’s support with food. Thus, humans never needed to develop the heightened senses of dogs for protection, and each provided for the need of the other. Similarly, telemedicine is now evolving its model. Telemedicine was once what is called a “one and done” visit. Previously used as an alternative to urgent care medicine, telemedicine is now evolving a primary care system. Several telemedicine providers are developing, or have deployed primary care provider telemedicine care platforms. To adequately perform ongoing telemedicine in a patient provider relationship that is ongoing and not a one time visit only, digital AI provides support for this type of patient engagement. Digital AI is the eyes and ears of the doctor for ongoing patient management.

Specialty telemedicine providers are also emerging. Specifically, diabetes specialists, or endocrinologists are now being utilized in several different programs to help with ongoing care and timely micro-adjustment of insulin regimens to provide best possible care for diabetics. Digital AI again empowers these doctors by notifying them of abnormalities, and adherence, as well as supporting doctor treatment/management decisions based on best care practices identified by AI.

Medication Adherence

In one study supported by the National Institutes of Health (NIH) a reinforcement learning-based medication health program was used to focus on medication adherence. Patients were randomized to either:

• Medication Event Monitoring System (MEMS) cap on pill bottles + text messaging reminding patients to take medication and managed by AI to determine type of messaging and frequency, or

• MEMS cap only.

The results showed improved medication adherence at 3 months for both study groups. Of note, medication adherence went from a baseline of 69% to 80% to 84-92% with use of digital AI.

Management of chronic conditions involves complex behaviors and patients vary in their medication adherence based on these behaviors. One study noted that after a heart attack episode at one month after hospital discharge <50% of these patients continued their daily low dose aspirin as prescribed during the heart attack. Another study showed that of 5,000 hypertensive patients, most patients took their medications only intermittently and 50% had stopped taking their medications without informing their provider. In the NIH study mentioned above there was greater medication adherence shown when accompanied with AI augmented text reminders. Mobile health services, such as text have shown up to two-fold increase in medication adherence.

Of note, the NIH study used AI to determine if patients were non-adherent with medication use and what type of messaging and what frequency of texting would lend itself to increased adherence. In one month AI had decreased text messages by 46%, due to AI learning that more frequent notifications did not lead to increased adherence. AI was also able to tailor notifications to those patients with the greatest need for reminders, and decreased, or stopped notifications to patients that were adherent as indicated by MEMS.

Digital Medication

Digital AI use in medication adherence is not limited to apps, sensors, and wearables between patient and provider/caregiver interactions. It extends into digital medication. Founded in 2001, Proteus Digital Health has created the world’s first Federal Drug Administration (FDA) approved digital medicine, a pill called Abilify MyCite. Abilify MyCite contains a sensor that digitally tracks if patients have ingested their pill(s). The way this works is outlined in Figure 4-3 below.

Figure 4-2. How Ingestibles Work

Abilify MyCite is specific to the medication Abilify used in the treatment of schizophrenia. This type of technology is very important as it is well known that schizophrenics that are not medication compliant have more psychotic episodes and may require more hospitalizations and intensive management, as well as being a potential harm to themselves and others.

AI applied to digital pills shows even more potential. Not only will medication adherence be tracked, but Providers and other caregivers will be able to apply AI to data intakes from wearables and other sensors to provide additional information on whether the medication is working and if real time micro-adjustments to treatment regimens need to be made. The potential for the benefits of digital AI are limitless.

Other ways in which Digital AI is transforming medication management include:

• Improving medication safety (Digital data in conjunction with utilization review with AI can detect medication errors);

• Predicting health risks and outcomes (Michigan is working on using patient’s medication histories along with electronic health records, and prescription drug monitoring programs to calculate drug overdose risk and predict risk of overdosing from a prescribed opioid); and

• Improving the medication prior authorization process (regardless of improvements, many medications still entail duplicate data entry, delays and rework after criteria for authorization are not met--AI can be used to extract the relevant data for the prior authorization along with digital information/data that can help augment and streamline the process of authorization by providing and importing the relevant biometric data that may be required for approval of therapy).