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Primary Self Care Algorithms

What is Primary Self Care?

Primary self care (PSC) exists at the intersection of primary health care and self-care. With an emphasis on self-management, self-testing, and self-awareness, PSC enables individuals to understand and optimize their health status through self-care interventions. While some PSC methods invite collaboration with clinical providers and others are entirely independent of healthcare systems, all can be applied to improve health outcomes at various stages of life1.

Why Primary Self Care?

According to The World Health Organization, there will be a shortage of 18 million physicians by 2030. Primary self care has the potential to fill this gap and reduce the burden on healthcare systems through better screening and prevention of diseases, all while providing more education on self-management of conditions. PSC empowers individuals to understand their health data and offers ways to effectively act on it. This leads to more engagement when patients interact with the healthcare system. Primary self care is also a way for individuals to personalize their care according to their needs and claim more decision making power in their health.

What is a Primary Self Care algorithm?

Primary self care algorithms are decision support tools for individuals to better understand their health and the steps needed to optimize their health status. PSC algorithms include clinical decision support (CDS) tools, patient decision aids, health assessments, and screening and testing services.

Why does the PSC Algorithms Project matter?

Despite being at the core of self care interventions, primary self care has been inconsistently defined. In light of the growing number of at-home healthcare technologies and services, there is a "pressing need for a clearer conceptualization of self care" as well as what self-care is not1. A publicly-available knowledge base of PSC algorithms can serve as an instrumental resource for patients, policy makers, and healthcare workers. This project will also reveal the current gaps in research and implementation of PSC models.

Top 10 Primary Self Care Algorithms

1. Vaccine Decision Aids

The global response to the COVID-19 pandemic highlighted just how critical vaccination efforts are in mitigating the spread and severity of infectious diseases. In 2020, COVID-19 rose to the third leading cause of death in the US and caused 6.26 million deaths globally2. In the decade, vaccine-preventable diseases like measles and pertussis have begun to rise as vaccine hesitancy spreads3. Despite the safety and efficacy of vaccines, getting people vaccinated has proven challenging due to a combination of mistrust in the scientific community, lack of education, and insufficient access.

Models

2. Blood Pressure Monitoring

Heart disease is the leading cause of death in the United States with more than 659,000 lives lost each year. Coronary heart disease, heart attacks, and other related heart conditions make up one in every four deaths, which amounts to spending $363 billion a year on healthcare services and medication10. The key risk factors of heart disease are behavior based: poor nutrition, physical inactivity, and high alcohol or drug consumption; early detection and promotion of healthy behaviors can prevent 34% of deaths due to heart disease11.

Models

3. At-Home Urinalysis

Kidney disease affects more than 15% of the US population, or 1 in 7 people. However, 90% of affected individuals are unaware of their condition as symptoms typically don’t appear until the late stages14. Key risk factors for kidney disease are comorbidities such as diabetes, hypertension, and obesity. Minority groups are at highest risk of developing severe kidney disease and kidney failure due to a higher prevalence of associated comorbidities and disparities in primary care access; as a result, these groups are less likely to receive pre-dialysis care and must wait longer for kidney transplants. Early detection of kidney disease is essential to ensuring that individuals can take preventative care measures or receive pre-dialysis treatment options.

Models

4. Mental Health Assessments

In the United States, 1 in 5 individuals live with a mental illness, with a higher prevalence among minorities and young adults; in 2020, only half of the 52.9 million affected people received any sort of treatment or counseling6. The stigmatization and poor education surrounding mental illness make it difficult for those seeking diagnosis and treatment. Suicide was the twelfth leading cause of death in the US in 2020 and most common in middle-aged white men7. Since three-fourths of mental illnesses begin before the age of 24, early identification and treatment are essential to lowering suicide and suicide attempt rates.

Models

5. Vision Tests

Vision impairment and blindness impact 2.2 billion people around the world, and more than half of these have not yet been diagnosed21. The most common vision loss diseases and leading causes of blindness are age-related macular degeneration, diabetic retinopathy, and glaucoma. Vision loss is also highly correlated to comorbidities like hearing impairment, kidney disease, stroke, arthritis, and heart disease22. Early screening and diagnosis of vision loss are key to correcting vision acuity before disease progression, and self-management of comorbidities can prevent further vision loss.

Models

6. Blood Glucose Monitoring

One of the most prevalent chronic conditions in the US, diabetes is the seventh leading cause of death. There are 37.3 million people currently living with diabetes in the country, and 38% of the adult population lives with pre-diabetes, which often goes undiagnosed until it progresses to Type 2 diabetes25. Early diagnosis and lifestyle changes can be effective in preventing diabetes progression, particularly for individuals with pre-diabetes. Comorbidities of diabetes include hypertension, chronic kidney disease, and cardiovascular disease, which means that early diagnosis, treatment, and lifestyle interventions are crucial.

Models

7. Breast Self-Exam

Breast cancer is the most prevalent cancer in the US and the second leading cause of cancer death in women. One in eight women (13%) are at risk of developing breast cancer during their lifetime. Black women in particular have the highest risk of developing breast cancer earlier than white women and experience a higher mortality rate than any other group. The incidence of breast cancer has been trending up in the past years, though deaths have been slowly decreasing, likely due to improved screening and early detection18.

Models

8. Birth Control Decision Aid

Globally, more than 200 million women face an unmet need for family planning options and contraceptives, and 1.4 million unplanned pregnancies occur each year28. Inadequate access to contraception is directly related to higher rates of unintended pregnancies and births, higher rates of STDs, and poorer educational and economic outcomes. There are lower rates of contraception usage among racial and ethnic minorities, a reflection of inequities in healthcare access and insufficient education around reproductive health. Contraceptive access and education are effective in reducing unintended pregnancies and in turn increasing high school graduation rates29. Women who want to avoid pregnancy often cite lack of access as a reason for not using contraception, but concerns about side effects and lack of knowledge are also barriers to access30.

Models

9. Air Quality Monitor

Chronic respiratory diseases are the third leading cause of death in the US and account for 7.5 million deaths worldwide each year33. These respiratory illnesses include chronic obstructive pulmonary disease (COPD), asthma, bronchitis, and other lung diseases. These conditions often start early in life and are exacerbated by environmental pollutants, poor nutrition, obesity, and lack of physical activity. Onset of these diseases can be determined by environmental factors such as irritants or allergens like smoke as well as lifestyle habits formed in adolescence, making it especially crucial that CRD risk factors are addressed early on.

Models

10. Pulse Oximetry

A pulse oximeter is an electronic device that clips onto a patient’s fingertip or ear to measure the saturation of oxygen in their blood. The reading is noninvasive and painless, and results take less than a minute35. Pulse oximetry is often considered the “fifth vital sign” as it is used for routine screenings of pulmonary health, diabetes, and sleep disorders.

Models

Application of Primary Self Care Across Life

0-5 years old6-1213-2021-3031-4041-5051-6061-7071-8081-9090+
VaccinationsRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequired
Blood PressureRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequired
UrinalysisRequiredRequiredRequiredRequiredRequired
Mental HealthRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequired
Vision TestRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequired
Glucose MonitoringRequiredRequiredRequiredRequiredRequiredRequired
Breast Self-ExamRequiredRequiredRequiredRequiredRequiredRequired
Air Quality MonitorRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequiredRequired
Pulse OximetryRequiredRequiredRequiredRequiredRequired

Database of Primary Self Care Algorithms

The AirTable below contains a database of Primary Self Care algorithms organized by the impacted bodily health system, the relevant social determinants of health, and the type of test or assessment. Relevant resources and information about each model are provided as well as the most prevalent health condition addressed.

Methods

Decision Matrix

The ranking of the 10 top Primary Self Care algorithms were determined by rating each algorithm against the following decision matrix adapted from methods of evaluating clinical decision support38.

CriteriaWeight (1-4)10-1
Audience
How many patients & clinicians do/or can utilize this algorithm?		2can be utilized by the majority of people, regardless of health statusutilized for common or highly prevalent conditionsutilized for only rare conditions
Effectiveness
How much of a difference can the algorithm make on someone’s health status regardless of condition progression?		4high impact at many stages in disease progressionmoderate impact at some stages in disease progressionlow or no impact at many stages of disease progression
Validity & Reliability
Has this algorithm been rigorously tested and have results been replicated?		4validated and replicated by many credible institutionsvalidated by some credible sources, limited replication studiesnot validated or replicated by credible sources
Complexity
How easy is it to learn to use and understand results from an algorithm?		2almost no learning curve, easy to implement and learn from resultsmoderate learning curve and results are mostly manageable to interprethigh learning curve to use and results are difficult to interpret
Cost
How expensive are any necessary tools/services?		3$ low cost or free$$$$$
Availability
How readily available and accessible is this tool?		1over the counter or diysome barrier to access (clinical treatment or prescription, specially ordered, etc.)still in development, not openly available
Support
How much clinical support or guidance is required?		2completely independentminimal community or clinical involvementrequires clinical support or intervention
Licensing
Is the algorithm open or closed source? 		2openunknown licensingclosed

Ranking of Top 10 Algorithms

CriteriaWeight (1-4)VaccinationsBlood PressureUrinalysisMental HealthVision TestGlucose MonitoringBreast Self-ExamBirth ControlAir Quality MonitorPulse Oximetry
Audience21111101110
Effectiveness4111001010-1
Validity/Reliability4111101-1100
Complexity311-11101-100
Cost31011101000
Availability11111111111
Support2-1011101-111
Licensing21110011110
Weighted Totals171615151111987-1

Next Steps

Future iterations of the PSC Algorithms Project will include an even more comprehensive database and an updated ranking of the top algorithms. The decision matrix will also be expanded to include the applicability in global settings, compliance, error rates, and variability in implementation; there will be a more in-depth look at licensing information as well. Additionally, this project will inform the editing of Wikipedia’s self-care page, which does not currently distinguish between primary self care and self-care.

The ultimate purpose of the Primary Self Care Algorithms Project is to support:

  1. Healthcare UX Designers creating intentional, patient-first tools and services.
  2. Providers tailoring medical care to patient needs.
  3. Patients seeking intuitive, value-adding tools as they take ownership over their health.

Authors

Arpna Ghanshani, GoInvo

Arpna is a designer with a background in data science and public health. She strives to create beautiful, data-driven primary self care services and improve access to healthcare. She joined Invo in 2022 while completing her BA in Data Science and BA in Public Health at the University of California, Berkeley.

Chloe Ma, GoInvo

Chloe is a designer and researcher specializing in medical and scientific storytelling. She drives to improve healthcare equity, education, and accessibility through good design. Chloe joined Invo in 2021 with a BS in BioChemistry and Molecular Biology from Dalhousie University and a MSc in Biomedical Communication from University of Toronto.

Juhan Sonin, GoInvo

Juhan Sonin leads GoInvo with expertise in healthcare design and system engineering. He’s spent time at Apple, the National Center for Supercomputing Applications (NCSA), and MITRE. His work has been recognized by the New York Times, BBC, and National Public Radio (NPR) and published in The Journal of Participatory Medicine and The Lancet. He currently lectures on design and engineering at MIT.

Contributors

Samantha Wuu
Huahua Zhu
Eric Benoit
Craig McGinley

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References

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