|Year : 2022 | Volume
| Issue : 1 | Page : 13
From vertical to horizontal health care: The next-generation medicine
Luis Pino1, Ivan Triana1, Jorge Mejia2, Eduardo Largue2, Denisse Rubio3
1 MedzAIo - MAIA® Exponential Medicine S.A; Fundación Santa Fé de Bogotá, Cancer Institute, Bogotá, Colombia
2 MedzAIo - MAIAR Exponential Medicine S.A., Bogota, Colombia
3 Universidad de los Andes, Bogotá, Colombia
|Date of Submission||27-Dec-2020|
|Date of Decision||30-Jul-2021|
|Date of Acceptance||21-Jun-2021|
|Date of Web Publication||07-Jun-2022|
Fundación Santa Fé de Bogotá, Cancer Institute, Bogotá
Source of Support: None, Conflict of Interest: None
Background: The purpose of this study was to establish the newest trends in medical health-care systems. Methods: This is a theoretical reflection about next-generation medicine, which is the first step to begin with an exponential medical health care and break with past models. Results: In the past, the medical health care relied on an evidence-based practice to provide the best treatment options for patients, however, since 2010, a strong economic wave has shaped the perspective into a value-based medicine framework. We are facing new social dynamics and megatrends in our society. The emergence of 4.0 technologies is leading us to a pathway where a next-generation medicine will create an exponential value for the overall health-care ecosystem. Originality: Next-Generation Medicine (NGM) integrates health care into digital ecosystems linked by innovative interfaces, advanced analytics, centric customer models, and digital epidemiology surrounding a new concept of health and disease management. NGM is based on four core capabilities of physicians: creativity, collaboration, communication, and critical thinking added to advanced digital operations that create a systemic risk management. This integration is developed using bidirectional and integrative digital platforms operated by artificial intelligence/Machine Learning (ML) connected to the Internet of things and data collection in the cloud or in the edge computing. It is time for health-care visionaries to set prejudice aside and start contemplating the amazing landscape that next-generation medicine could offer.
Keywords: Artificial intelligence, Digital Platform, Exponential medicine, Nomad health, Technology
|How to cite this article:|
Pino L, Triana I, Mejia J, Largue E, Rubio D. From vertical to horizontal health care: The next-generation medicine. Digit Med 2022;8:13
| Introduction|| |
Throughout history, medicine has changed significantly from Hippocratic medicine to evidence-based medicine and later to value-based medicine. However, with the social, economic, and technological changes, worldwide medicine has developed in what is known as new-generation medicine (NGM). It should not be mistaken for digital, smart, or precision medicine, because the NGM concept encompasses an exponential health care.
Disease dynamics, changes in health-care ecosystems, financial burden of new technologies, and patient's expectations have triggered the demand of NGM in the medical field.
Nowadays, health-care services experience numerous disadvantages. First, there is just one model for all patients, and this is not individualized. Second, it is expensive (there is not a balance between medical resources and clinical efficiency), and last, it is fragmented as it is not based on holistic data.
NGM requires an overarching approach where medicine turns into a continuous cycle of innovation moving from medical institutions toward patients and communities. It must experience an advance due to the mind shift in lifestyle choices associated with the harsh consequences of COVID-19 pandemic. By 2050, 1 out of 4 people in Europe and North America will be over 65 years old, which means that health-care systems will face a higher burden of disease.,, Similarly, Latin America will face that challenge but with fewer resources, and that is the reason to optimize, automate, and prevent to achieve good outcomes.,
The cost such patients represent to the systems encourages a replacement of episodic care-based philosophy to one that is much more proactive and focused on long-term care management, a real smart disease management.
Without major structural and innovative changes, health-care systems will struggle to remain sustainable. “A good healthcare is expensive, but a bad one is even more.” In addition, health-care systems need a greater medical staff. By 2030, it is foreseen a global deficit of 9.9 million physicians, nurses, and midwives, although 40 million new health-care jobs could be created. According to the World Health Organization, we need to attract, train, and keep the maximum number of health-care professionals, ensuring their time at the most valuable field: caring for patients.,,
Our team welds a medical, technological, and analytical capacity to create a platform that adjusts to every single need of NGM. It emphasizes in cancer care, but it is available to any field within health care. It browses through a software of artificial intelligence (AI) called MAIA® (medical artificial intelligence aid) that allows the digital performance via Google Cloud® Computing. We have developed smart disease management for health-care frameworks such as liver transplant or multiple myeloma and a genomic test platform to integrate molecular oncology operations, among others. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the daily life of health-care practitioners, allowing them to spend more time looking after patients and consequently boosting staff morale besides improving its satisfaction. It can even expedite lifesaving treatments in the market.
It has been questioned AI impact on patients, practitioners, and health systems, as well as its potential risks. In fact, there are ethical debates about how AI and the data should be used.,, However, NGM goes further than AI; it includes a right clinical application of other technologies such as Distributed Ledger Technology (DLT) and optimization algorithms for the improvement of health-care services. It signifies the use of AI to humanize the medical practice, to assure physicians more qualified for a better and compassionate care.,
The NGM is based on some principles that health-care companies must incorporate into their DNA, mainly the “additional product” and the clever management of health-care waste. The use of 4.0 technologies is a tool for process management and a real digital transformation (not only telemedicine).,,
This conceptual framework is a challenge due to, unlike other sectors, the operations are focused on the health-care service that is not tangible, which results hard to standardize and has a highly subjective and social component.
These characteristics may have to interact with the intensive use of technical knowledge as well as a technological environment designed to change the way how risk management is understood and executed [Figure 1].
|Figure 1 Impact of NGM (artificial intelligence-based) in health-care services.|
Click here to view
The comprehensive management of health-care risks is responsible for maintaining population health conditions at the best possible level (primary risk), and for minimizing the burden of the disease by providing health services at different levels of complexity (technical risk).,
In our system, the primary risk management is delivered to health-care insurance companies and the technical risk is managed by hospitals. NGM must create an integrated ecosystem through platforms that encompass all of these intelligent operations.,,,
One of the goals of next-generation medicine is breaking the concept of health as a resource located in service providers to transfer it to human interaction itself. In fact, our main project named MAIA is an AI engine that connects all that happens outside the medical record. It is a fracture in the way medicine is conceptualized by medical teams. This change implies the connection of the four pillars: innovative interfaces, advanced analytics, customer-centric models, and digital epidemiology.
Based on the above, new technologies must be redesigned to capture liquid data from any social and personal source, analyze data with a predictive and prescriptive horizon, create experience in the digital world, and navigate in a data-driven new medical world.
| Development|| |
We propose these new advanced digital operations:
Advanced operations for the comprehensive management of primary risk [Figure 2]
|Figure 2 Advanced clinical operations in NGM for primary care. (primary risk management)|
Click here to view
Digital funnel profiling
This operation consists of a health risk profiling of individuals and populations through platform based-surveys. The data provided (adequately anonymized) by people using digital capture tools are analyzed using trained algorithms for the classification of specific risks, mainly focused on health promotion and maintenance. There must be a real-time geolocation connected to a command and control panel (dashboard) for each insurance company.,
These operations are widely adaptable to cover automatic evaluations of other significant risks such as cancer with proven evidence of clinical detection scales (e.g., breast cancer and lung cancer). Soon, individual and population profiling will include the recording of molecular biology information related to proven specific risks (genomic risk profile). These operations will use digital “funnels” to capture an immense amount of data but filtering and directing them to continuity routes of health through specialized mathematical algorithms. This output will be connected to electronic medical records from the primary care services. Therefore, this interconnection will represent a framework for individual health-care interventions intended to minimize health risks.
This new operation will optimize subprocesses such as the preaffiliation health declaration, the determination of individual risk, the population characterization and georeferencing, the development and management of specific protection programs, and the implementation of comprehensive care routes plus the articulation of Plan de Intervenciones Colectivas (PIC) actions (the collective or community health intervention plans).,
Current screening operations for cancer and other diseases do not have the expected impact on overall mortality, possibly because the technologies do not have a 100% accuracy (for example, cervical-vaginal cytology) or due to a process failure. Smart screening operations include molecular sample test detection (saliva, stool, and blood) or self-sampling kits (DNA for human papillomavirus), activities that now are not accessible in low-middle income countries.,
Smart screening operations include mobile media such as mobile digital mammography, which require platforms to facilitate the flow of information and for the screening (and telescreening) of patients with abnormal tests for a final diagnosis. This information gathering must be kept in the cloud to be interoperable in a unified electronic medical record system.,
One of the goals of this procedure is to guarantee the continuum of the patient's follow-up and avoid new procedures outside a useful window of time. For example, some patients carry out 2 or 3 screening tests a year to “optimize” their results, and this is an obvious waste.
These operations involve a remote access to health-care transitions in nonhealth-care centers such as shopping malls, administrative offices of health companies, telephone booths, and public transports among others, where there is a connection to mobile devices for the evaluation of a specific health update. An example of this is the use of tablet-like devices in adapted booths that will enable the shoot of high-resolution photographs for people with suspected skin cancer injuries. They are analyzed using AI tools (trained with deep learning) to determine the risk of malignancy.,,
The result of the algorithmic analysis of the injury must be linked to the person's insurer backup to guarantee the continuum of the health-care process, whether it is a follow-up of the injury or a consultation with the expert. Patients would get an immediate schedule to their mobile devices and E-mail.,,,,,,
In our country, we are planning to use diagnostic algorithms (some already approved by the Food and Drug Administration) for lung nodules through deep learning. Initiatives in this field are led by universities, however, this diagnostic phase must be linked to digital operations, to create a real exponential value. These developments would be accessible to the radiology departments of any health-care company with connectivity.
Another process of these operations is the compilation of people's mobile data through body-wearable devices interconnected using the Internet of things (IoT), which allows the information flow of clinical relevant variables to these interactive platforms for a prompt and formal health-care attention. Moreover, these technologies are also part of the advanced programs for disease management that will be discussed later. This subprocess will be very important for future clinical trials using an adaptive model with rapid methods.
These operations include the entire scheduling process (like the previous one for skin cancer) from multisource information. People should have the possibility of accessing to schedule their health interventions from different portals (payers' agencies, hospitals, and even from a taxi with a digital screen connected to an assistant bot).
Traditionally, patients themselves have been the interface and navigator of the health-care systems. In this way, the present proposal allows them to be the heart of a digital universe that facilitates their health-care transitions.
Advanced operations in clinical execution [Figure 3]
It has been the most remarkable transformation in the last 3 months due to the COVID-19 pandemic. There are already multiple telemedicine options through different streaming platforms, but, as Bertalán Meskó says: “The evolution of this healthcare attention has been so overwhelming that it may be the giant leap for telemedicine or its disastrous end.”
Digital interactions for the practice of traditional medical consultation still show many limitations:
- Low connectivity: In low-middle income countries, less than 20% of people in rural areas have access to a computer and Internet.
- Digital skills of people are still scarce.
- Telemedicine platforms are still not linked to other vital operations and resources such as electronic medical record, business intelligence platforms, and administrative backups.
Therefore, telemedicine is a holistic NGM operation that keeps in the field of optimization and can be applicable to all comprehensive risk managements (primary and specialized health care).
Physician–patient communication is just one point within the health-care service, whereas telemedicine also includes a telesupport (MD-MD) to expand diagnostic capabilities and scientific support in remote areas. Apart from that, it allows for the development of virtual medical boards.
Some physicians complain, and with good reason, about the inability to perform a correct physical examination on telemedicine. This will be solved with the integration of some distant technologies such as cutaneous photoplethysmography that use smartphone cameras to measure variables such as temperature, blood pressure, arterial oxygen saturation, and even stress levels. IoT is also a technology to be included to reach this goal.
All of these trends will arise in the next months, nevertheless we are emphasizing in the fact that whether telemedicine is not part of a much larger digital health ecosystem, it will be just a weak interaction overall.
Smart medical surveillance
Traditionally, disease management programs have been based on disease niches or complex patients with the purpose to improve health outcomes, reduce complications, and generate containment of health-care costs. These programs rely on mixed health-care teams (at home and hospitals) that support the patient's needs and include an essential educative component for caregivers.
Intelligent medical surveillance programs creating smart disease management will allow direct, fluid, efficient, and real-time communication between health-care teams, patients and caregivers, pharmaceutics, payers, and even the community elements required. All these enable a better data collection of “live” information that will improve immediate decision-making and a program feedback.,,,,
It also facilitates a prompt, efficient, and interconnectable pharmacovigilance process. Indeed, converting a time-consuming process into a digital one, will optimize the report of adverse events. In the near future, pharmacogenomics tests, which are predictors of adverse reactions and efficacy, will be linked up, thus generating a really personalized prescription.,
It should be noted that this digital architecture (biodigital architecture) does not only works for patient programs, but also it can be implemented to other health-care programs such as “fitness” communities in which the IoT component is a tool for the evaluation of adherence and accomplishment of goals.
Virtual or simulated reprogramming
This operation is based on virtual or augmented reality and some other technologies employed for:
- Optimization of user's experience at different services
- Programs for addiction and phobia treatments
- Programs for teenager's health and healthy lifestyle
- Management of chronic pain
- Palliative and end-of-life programs.
In this manner, the application simulates multisensory settings to transform the medical intervention perceptions. Imagine a teenager who has just started smoking and could simulate the harmful effects of nicotine, such as digital clubbing, dyspnea, or cachexia. Certainly, the impact will be greater than with a photo of an unknown individual in a cigarette pack.,
Furthermore, there are numerous digital operations that will be part (and that already are) of the NGM. Nevertheless, they have been omitted due to minor applicability, if none, in the health-care field.
Smart health-care systems and hospitals are still under construction. A digital transformation of the health-care sector will be tremendous. Some authors divide this escalation into three phases:
First, solutions are likely to address routine, repetitive, and largely administrative tasks, which take long time, thus optimizing health-care procedures and being more likely to be incorporated. Besides, it will include AI applications based on images, which are used in specialties such as radiology, pathology, and ophthalmology.
Second, we expect more AI solutions that endorse a hospital care to a home care, with remote monitoring, AI-powered alerting systems, or virtual assistants, as patients increase their self-care. At this second phase, a broader use of NLP solutions could be also included both at the hospital and home settings, and AI could be expanded into specialties where technological advances are now being implemented, such as oncology, cardiology, or neurology. These will demand a broader integration of AI in clinical workflows through health-care professionals and providers, as well as improved solutions for using existing technologies in new contexts. All these can be achieved with a combination of technological advances (e.g., in deep learning, NLP, and connectivity), training within organizations, and a cultural change.,,
Finally, we foresee more AI solutions within a high evidence-based clinical practice, always seeking for improved and scaled clinical decision-support tools. People would see AI as an essential part of the health-care value chain, from how we learn, investigate, and deliver care, to how we improve population's health. Specifically, in an European context, previous conditions for AI to offer its complete potential are the integration of broader data sets across organizations, strong governance to continuously improve data quality, and greater confidence from organizations, practitioners, and patients in both the AI solutions and related risk management.,
| Conclusions|| |
It is time for health-care visionaries to set prejudice aside and start contemplating the amazing landscape that next-generation medicine could offer. Today, we are relating a challenge transformation that in the future will be just medicine.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Meskó B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth 2017;3:38.
Konttila J, Siira H, Kyngäs H, Lahtinen M, Elo S, Kääriäinen M, et al.
Healthcare professionals' competence in digitalisation: A systematic review. J Clin Nurs 2019;28:745-61.
Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, et al.
Translating cancer genomics into precision medicine with artificial intelligence: Applications, challenges and future perspectives. Hum Genet 2019;138:109-24.
Tran C, Dicker A, Leiby B, Gressen E, Williams N, Jim H. Utilizing digital health to collect electronic patient-reported outcomes in prostate cancer: Single-arm pilot trial. J Med Internet Res 2020;22:e12689.
Pino L, Triana I, Mejia J. MAIA (Medical Artificial Intelligence Assistant) as interface for a new cancer healthcare integrative platform. JCO Glob Oncol 2019;5(Suppl):25.
Kagan Trenchard E, Semlies L, Gierlinger S. The digital revolution will see you now: Transforming patient experience in the digital era. Patient Exp J 2019;6:12-5.
Lizana FG, Santamera AS. Revisión de intervenciones con nuevas tecnologías en el control de las enfermedades crónicas (In Spanish). Madrid: Instituto de salud Carlos III;2005:7-41.
Rolfhamre P, Janson A, Arneborn M, Ekdahl K. SmiNet-2: Description of an internet-based surveillance system for communicable diseases in Sweden. Euro Surveill 2006;11:15-6.
Leeming G, Cunningham J, Ainsworth J. Ledger of me: Personalizing healthcare using blockchain technology. Front Med 2019;6:171.
Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: A comparative epidemiological study at three geographic scales. PLoS Comput Biol 2013;9:11.
Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009;457:1012-4.
Topol E. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. New York: Basic Books; 2013. p. 336.
[Figure 1], [Figure 2], [Figure 3]