The pyramid healthcare model
James Fleck, MD, PhD: Anticancerweb 31(10), 2023
A chronic disease is a long-lasting and often progressive illness that usually impacts mortality rate and quality of life. It is considered non-communicable; however, it is often associated to unappropriated lifestyle, which can be influenced by social habits. Tabaco use, predominantly ingestion of processed food, inactiveness, excessive alcohol consumption, depression and anxiety are the most frequent causes. Unfortunately, around 50% of US adult population suffer from at least one chronic disease. They are the main responsible for premature deaths, despite draining most of the 4.3 trillion dollars from US national health budget (18% of GDP). The leader killers are cardiovascular diseases, cancer, chronic respiratory illnesses, type 2 diabetes, Alzheimer and progressive kidney failure. All of them are preventable and their risk-factors preferably addressed through a longitudinal cohort epidemiological intervention. One of the best examples of this approach was the Framingham Heart Study, currently celebrating 75-years, since the accrual of the first participant in 1948. The study was launched after the sudden death of president Franklin Delano Roosevelt in 1945 due to cardiovascular disease and poor understanding of risk-factors in the middle of the twentieth century. An invitation letter was sent to 10,000 adults, living in the small city of Framingham (28,000 inhabitants in 1948), which led to an accrual of 5,200 volunteers in its original cohort. This 52% acceptance rate illustrates a population high health appeal, observed after a public initiative. Population expectations were achieved, as the progressive amount of data collected made it possible to identify specific Population Attributable Risks (PARs) associated to heart failure. A significant decrease in cardiovascular mortality rate was observed in the following years. A similar public initiative should be taken toward all chronic diseases. But first we must look at the scale of the problem, where the gaps in care and communication lie, identify and locate the turning points and define what actions should be recommended.
Currently, the US healthcare budget is 4.3 trillion US dollars (USD). This represents 18% of the country's Gross Domestic Product (GDP), which is the highest in the world, both in terms of amount of money and as a percentage of GDP. However, the majority of the budget goes to high complexity, which generally benefits the minority of the at-risk population (15%) and an even smaller percentage of the general adult population. Figure 1 represents the numbers and dynamics of healthcare in the US. Approximately, 4 trillion USD (93.25% of US health budget) are used to help patients in secondary and tertiary care, which leave only 300 billion USD (6.75% of US health budget) to spend in primary health care. Available expenditure per capita clearly expresses the magnitude of the difference, which is 25 times greater in secondary and tertiary care than in prevention and basic health care. Currently, most resources in clinical research have focused on high complexity, which is highlighted by the good recovery rate of interventions. Sophisticated diagnostic and treatment methods help patients to be discharged from hospitals and live a reasonably good quality of life at home. Unfortunately, few of these interventions have already been evaluated for cost-effectiveness (ICER/QALY), which makes it difficult to assess the adequacy of public or private resource allocation. Gaps in the US healthcare system are also responsible for the unacceptable 15% of the US healthcare budget absorbed by administrative costs.
Figure 1: The pyramid illustration model, showing numbers and dynamics of healthcare in US
The pyramid illustration model can also function as an algorithm used to optimize the healthcare system of any country in the world, respecting human, economic and geopolitical specificities. To operate the health pyramid algorithm, the first step is to fill in the size of the target population. The subsequent numbers in ascending order are determined by each country's health policy (health appeal). Public and private initiatives are expressed in the conversion rate, observed between increasing stages of complexity in the healthcare system. Turning points are represented allegorically by rotating keys, where interventions will promote effective changes in outcomes. The ideal healthcare system should maintain a high health appeal at all progressive stages of care and actively seek positive intervention tools capable of reducing conversion rates as much as possible. Improving health appeal in both general adult population and at-risk population would create a better setting for longitudinal cohort epidemiological studies, identification of risk factors, and future cognitive and care interventions across the entire chain of public health events. Acting predominantly on the basis of the pyramid model is expected to reduce the burden of chronic diseases. The pyramid health algorithm requires 100% enrollment from the target population. This objective can only be achieved with people's participation. Intervention requires an inclusive tool. Hopefully, public and private initiatives should join forces to encourage the use of a personal health record (PHR) by the target population. Consequently, the target population would have a guiding role in the management of the public and private health system. Figure 2 shows how the PHR would be useful in both health registry and healthcare. At the illustration, PHR is preceded by the letter e because it is in an electronic (digital) format, specially designed to facilitate the upload of all health data.
Figure 2: The Global e-PHR template designed to facilitate the upload of all health data in a uniform record format suitable for medical rationale
The system is centered on people's needs and rights. The record belongs to each person, like an identity document (ID). To be user-friendly and cost-effective, the electronic tool is divided into two data capture steps. The first, called e-PHR Registry, requests all personal data, including primary health data (PHD). Figure 3 illustrate a preliminary suggestion how to include the most relevant PHD. People's registration follows the General Data Protection Regulation (GDPR), which can be found in the privacy mode, containing all the terms of the agreement. By signing electronically, people consent to the anonymized use of their data for epidemiological research. The first turning point in the management of the healthcare system is located here. Hopefully, the e-PHR registry would increase the appeal of healthcare in the target population, which would likely be followed by a responsible decrease in its conversion rate to an at-risk population.
Figure 3: Most relevant Primary Health Data (PHD) to uploaded into the e-PHR Registry
A population at risk is defined by the need for outpatient or inpatient care. At this point, the e-PHR Registry becomes the e-PHR Full. Physicians are oriented to create a problem list, according the worldwide known problem-oriented medical record described by Lawrence Weed. A data base composed by clinical history, physical exam and some preliminary laboratory data would support each listed problem. Based on each numbered problem, physician would define his intervention planning, which should be kept under surveillance and recorded in progress notes until it is resolved. Additionally, reports, images and videos of diagnostic exams would be uploaded by consulting physicians into the designed mode. The record automatically provides a timeline of events and a summary to facilitate access. Patients have a real-time view of their respective e-PHR. Access is based on a unique username (UN) and password (PW). Only the patient has the right to share his/her clinical information. At the discretion of patients, data sharing should be restricted to professionals or institutions directly involved in their healthcare, respecting the privacy of their data, in accordance with GDPR regulations. Here it is possible to glimpse a second turning point. Data portability gives patients a leadership role. Better understanding their own health issues and fully exercising shared decision-making would empower patients and increase healthcare appeal. Global e-PHR based artificial intelligence (AI) bigdata analysis could be offered to healthcare professionals, enabling them to fill the gaps in prevention, diagnosis and early treatment. Anonymized analysis of this bigdata would allow the design of cohort and cross-sectional epidemiological studies involving 100% of the population at risk. These interventions could reduce the conversion rate to hospital and high-complexity care. The pyramid algorithm also points to a third turning point, working at the level of tertiary healthcare. It means the routine use of ICER/QALY criteria that guide highly complex recommendations for chronic diseases. ICER stands for incremental cost-effectiveness ratio and QALY stands for quality-adjusted life year. This concept should guide medical intervention, as it contemplates the increase in cost, time of life gained and quality of life obtained after each specific recommendation.
Finally, let's work on the top of the pyramid algorithm, where a fourth inflection point could be explored. High complexity is associated with the generation of a huge amount of health data, usually including multidisciplinary care, complex medical charts and reports. During inpatient care, a great effort must be made for interoperability between hospital medical record and the patient's e-PHR Full. This action should be encouraged, as the high recovery rate will lead people to return to outpatient and home care, where the clinical data included during hospitalization will be very relevant in maintaining the necessary care. It is possible to anticipate the increasing use of FHIR-HL7 as a leading action in interoperability, which would represent the forth turning point in achieving a comprehensive and cost-effective healthcare system and a desired model for addressing the burden of chronic diseases.
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