Taking healthcare where it needs (TH-WIN)
James Fleck, MD, PhD: Anticancerweb 29 (01), 2023
Primary Health Care (PHC) has a resolution rate of 85%, but when care is restricted to general practitioners, it becomes less cost-effective and, consequently, does not arouse the interest of the private market. As health is everyone's need and right, PHC becomes a tripartite responsibility, including the government, private and social sectors. The lack of administrative creativity overloads and discourages PHC, a sector of undeniable relevance in public health. General practitioners and other health professionals who work exclusively in PHC feel undervalued due to overwork and low financial return, combined with interventions classified as low complexity. However, in a well-designed interactive health system, only 15% of PHC consultations will require highly complex procedures, often associated with higher costs. Currently, High Complexity Health Centers (HCHC) are oversized, as they also offer PHC. HCHC are widely available in large urban areas and their costs are provided by public and private resources, combined with the patient's co-payment, generating an undesirable global out-of-pocket crisis imposed on the patient and family. Logistical barriers generate inadequacy and delay in referral and counter-referral, underestimating the PHC and overestimating the HCHC. TH-WIN will break this paradigm by bringing high complexity decision-making to PHC, using new generation technical inputs to fill in the gaps.
TH-WIN identifies precise and confident patient’s clinical data records as the cornerstone for a well-succeeded integration of PHC and HCHC. The record should be standardized, hopefully using a global semantics. New ontologies are in advanced stage of implementation, which would provide a healthcare paradigm shift in electronic-personal health records (www.ephr.org), which would make them globally available. A joint effort of physicians and IT professionals would result in proper program and device implementations. The generated large amount of clinical data (Big Data) could be processed by artificial intelligence (AI), that would provide a new scientific methodology for AI-oriented algorithms, both for clinical and administrative purposes. State-of-the art medical knowledge could be universally available and a collective intelligence effort could orient point-of-care decision making, filling out the imprecise gap, currently observed in a PHC-HCHC variable intersection area (VIA) of any healthcare system. VIA work-up can be customized for different communities, states and countries fulfilling patient’s needs and rights. Referral and counter-referral would be based on telemedicine and telemetry.
TH-WIN would fight inequities, following the principle that healthcare should be universal. Recent COVID-19 pandemic showed how fragile we are. There is no geographic barriers anymore. The planet works like a small community. Everyone's safety is supported by a universal and well-balanced health care system. All communities, states and countries could be reached. The extent of interventions can be designed individually, based on local ICER/QALY criteria. Local, national and international committees would evaluate specific needs and proposed interventions. Taking healthcare to where it needs to be is a rational way of dealing with the current imbalanced distribution of the healthcare system by identifying opportunities for VIA-improvement.
* Photo by Tomasz Frankowski on Unsplash
The proposal presented by the author aims to integrate Primary Health Care (PHC) and High Complexity Health Centers (HCHC) by standardizing and analyzing patients' clinical data, with the goal of improving the quality of care and fighting inequalities in the health system. Additionally, the implementation of ontologies and artificial intelligence technologies could facilitate access to and management of this data. However, the text does not discuss the feasibility and practical challenges of implementing this proposal. It is important to consider that improving the healthcare system depends not only on technological advances but also on public policies and adequate investments to value and train the professionals involved.
A proposta de integração entre a Atenção primária e os centros de Alta complexidade possibilita que as informações clínicas dos pacientes sejam analisadas e avaliadas em prol de melhorar a assistência à saúde. Uma vez que APS são a porta de entrada para se saber da doença de uma população, o que possibilitaria que essas doenças complexas que muitas vezes são limitadas de atendimento por uma regulação, possam ser de fácil acesso. Deste modo, a implementação de ontologias e tecnologias de inteligência artificial poderiam facilitar o acesso e o gerenciamento desses dados em prol da saúde coletiva.
The proposed integration between Primary Care and High Complexity centers makes it possible for patients' clinical information to be analyzed and evaluated in order to improve health care. Since PHC are the gateway to knowing about the disease of a population, which would allow these complex diseases, which are often limited to care by regulation, to be easily accessible. In this way, the implementation of ontologies and artificial intelligence technologies could facilitate access and management of these data in favor of collective health
Given that the complexity of decision-making in primary health care depends on the specific circumstances and the needs of the patient, using Artificial intelligence (AI) in order to analyze large amounts of data and identify patterns and trends can actually increase the resolution rate of PHC. Although 85% is already a great rate, I think we can improve that by using modern technology. Moreover, technological innovations can be extremely uneven, therefore, it is great that the text defends the idea of using technology to generate more equity in the health system.
While the idea of TH-WIN is revolutionary and seems adequate in a context of inequality and overload of the primary care systems around the globe, we do have to bring up a discution that extrapolates the healthcare field. We ought to ask ourselves why did things get this way. We should consider the fact that implementing these kinds of things are subjected to all kinds of governments, politicians and financial profits, wich, I may say, are the exact reason we are where we are.
The integration between Primary Health Care (PHC) and High Complexity Health Centers (HCHC) provides standardization of patient data in order to better serve patients. In addition, it facilitates access to health, reducing existing inequalities in the health system. The implementation of ontologies and artificial intelligence technologies would be a way to better manage public health.
A fascinating insight into the prospective role of artificial intelligence and machine learning in healthcare. These technologies could lead to significant improvements in healthcare outcomes by allowing for more personalized and efficient treatments. However, it acknowledges the need for caution in implementing these technologies, particularly with respect to issues of privacy and bias. Overall, this article presents a compelling and thought-provoking view of the future of healthcare, highlighting both its potential benefits and challenges.
The integration of PHC (Primary Health Care) and HCHC (High Complexity Health Centers) is an incredible perspective for healthcare over the world. The use o AI to guide this integration is even more arousing, since it has the ability to see patterns we probably wouldn't have seen. Therefore, the future holds wonders with the association of new technologies and health care planning.
There is a need to strengthen and enhance Primary Health Care as an essential component of an effective healthcare system. This entails promoting creative administrative approaches that acknowledge the vital role of healthcare professionals in Primary Health Care, while providing fair working conditions and equitable financial incentives. Furthermore, it is essential to develop strategies for integration and coordination across different levels of care, ensuring that Primary Health Care can deliver comprehensive and high-quality care to the population, thereby addressing the requirements for more complex and costly procedures.
The use of artificial intelligence (AI) can increase the complexity of decision-making in primary healthcare by taking into account the particular conditions and patient needs. The resolution rate of PHC can be greatly improved by using AI to analyze massive amounts of data, find patterns, and decipher trends. While an 85% success rate is great, using modern technology can improve this result even more. The text, however, omits any discussion of the practical difficulties and viability of putting this suggestion into practice. It is crucial to keep in mind that developing the healthcare system depends not only on new technology but also on governmental policy and proper funding for the experts involved in providing treatment.
The integration of primary healthcare into oncology treatment, combined with Artificial Intelligence (AI), holds tremendous potential. AI can enhance early detection, streamline patient management, and support personalized treatment plans. By leveraging AI algorithms, primary care providers can access a wealth of patient data, enabling them to make informed decisions and provide comprehensive support throughout the cancer journey. The synergy between primary healthcare and AI empowers a more holistic approach to cancer treatment, leading to improved patient outcomes and a more efficient healthcare system.
This need for a collaborative effort between the public, private and social sectors to ensure comprehensive health coverage is very important. As much as it is already very well known about the need for investment in primary health, there is still no proper investment and, consequently, we still pay a very expensive social price for health.
The text emphasizes the importance of integrating Primary Health Care (PHC) and High Complexity Health Centers (HCHC). It highlights the need for standardized clinical data records, advanced technology, and collaboration between healthcare professionals and IT experts. The goal is to achieve universal and well-balanced healthcare, address inequities, and improve patient care through telemedicine and AI-driven algorithms. The TH-WIN initiative aims to optimize the healthcare system by filling the gaps between PHC and HCHC, ultimately providing better access and decision-making in healthcare delivery.
TH-WIN proposes a visionary approach to integrate PHC and HCHC, using new technical inputs and standardized health records. This shift empowers precise patient data and AI-driven point-of-care decisions. Telemedicine and telemetry-based referrals break barriers, ensuring equitable healthcare access. Emphasizing universal healthcare, TH-WIN involves government, private, and social sectors, promising to address the imbalanced distribution of healthcare and impact global public health.
The implementation of artificial intelligence (AI) in primary healthcare has the potential to enhance decision-making by considering individual patient conditions and needs. By analyzing vast amounts of data, identifying patterns, and uncovering trends, AI can significantly enhance the resolution rate of PHC. Although achieving an 85% success rate is commendable, the integration of modern technology has the capacity to further elevate these outcomes. Nevertheless, the text overlooks the practical challenges and feasibility of implementing this proposal. It is imperative to acknowledge that the development of healthcare systems relies not only on cutting-edge technology but also on governmental policies and adequate funding to support the expertise of healthcare professionals providing treatment.
Artificial intelligence (AI) has had a significant impact on healthcare and medical care, bringing a number of benefits and advancements to medical practice, research and patient care. Some of the key ways AI is being applied in healthcare include: Early Diagnosis and Detection: AI can analyze large patient data, such as medical images, laboratory tests and clinical history, to help doctors make more accurate diagnoses and identify early onset diseases. This allows for faster and more efficient treatment, increasing the chances of recovery. Personalized Medicine: AI enables the analysis of genomic and molecular data to better understand the unique characteristics of each patient. With this information, doctors can customize treatments to each person's specific needs, maximizing effectiveness and minimizing side effects.
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