As per a report the number of people with chronic conditions is predicted to reach 171 million by 2030. This number is predicted to grow in the coming years, however the number can be controlled with adequate healthcare interventions.
Healthcare interventions are plans and strategies designed to curb infectious or fatal diseases amongst the population or patients. Data driven approach is proven to be an effective method to intervene for disease prevention.
Let’s understand types of healthcare interventions before moving towards how data analytics drives healthcare interventions.
Types of Healthcare Interventions
There are two major categories of healthcare interventions – preventive and therapeutic. Let’s understand them in detail.
Preventive Healthcare Interventions
Diseases and accidents are stopped at before their onset through preventive measures. There are three categories of healthcare preventions:
1. Primary Prevention
Primary preventive measures addresses diseases before its occurrence. This includes enforcement or legislation or ban for controlling usage of hazardous items such as asbestos, that may cause serious harm or injury. Healthcare education about diet, hygiene, and safe practices such as no smoking or exercise. Immunization of people against infectious diseases falls under primary prevention too.
2. Secondary Prevention
The aim of secondary prevention is to decrease the impact of an injury of disease which has already occurred. Preventions are devised by treating or detecting a disease instantly to slow its progress, implementing healthcare programs, and plans to prevent reoccurrence or reinjury.
For example, regular screening tests to detect cancers at early stages or recommending low doses of aspirin to prevent further strokes or heart attacks.
3. Tertiary Prevention
Tertiary prevention focuses to lessen the effects of a prolonged injury or disease by managing complex injuries and health issues, thus improving life expectancy. For example, chronic disease management programs for arthritis, diabetes, or depression. Support groups and rehabilitation centers for recovery and improving living.
Therapeutic Healthcare Interventions
Therapeutic interventions mitigate, treat, or postpone the impact of diseases, therefore decreasing the fatality rate or disability of diseases.
1. For Addiction
A group intervention approach is used to assist individuals who refuse to change harmful behavior such as drug or alcohol addictions. A support group through mediated and gentle meetings is staged to uplift these individuals.
2. Crisis Intervention
A qualified professional or a therapist supports individuals who have undergone crisis situations such as accidental traumas or grief. After crisis support provides a clear perspective to the affected individuals. Trauma experts, psychologists, or psychiatrists with appropriate skills and training are best suited for crisis interventions.
3. Behavioral Interventions
Behavior interventions are used to modify unhealthy and damaging behaviors such as depression by providing care and medications. Interventions are implemented through various ways such as recommending adequate diet or exercise to patients or incorporating strategies to eliminate problematic behavior in classroom.
How Data Analytics Drive Healthcare Interventions
Healthcare companies and data scientists use the data gathered through healthcare devices, EHR, or IoMT for treatment plans, procedures, and interventions. Healthcare providers can think about leveraging healthcare data analytics software for more personalized treatment plans.
Data analytics drives healthcare interventions in the following ways:
1. Detection and Prevention of Diseases
As per a study, childhood immunization prevented around 40,000 deaths and 20 million diseases in the U.S.A, hence saving $70 billion in total. Healthcare officials make use of data to prevent fatalities of new born babies by planning an immunization program.
Healthcare professional utilize data such as lab results, medications, surgeries, or allergies to detect and treat a disease instantly. For instance, mammogram results for detecting breast cancer in women.
Medical staff create health strategies and plans through data analytics to slow down or halt the progress of a disease. Providers can prevent reoccurrence or reinjury of an ailment through medication strategies.
Tools utilized to detect and prevent diseases via data analytics are –
a. Genetic Mapping
Genetic mapping is useful to detect the gene responsible for any single-gene inherited or rare diseases such as Duchenne muscular dystrophy or cystic fibrosis. Mapping guides researchers to the genes which play a role in the occurrence of common diseases such as diabetes, cancer, asthma, or psychiatric conditions.
Genetic testing reveals alterations in the genetic makeup or gene mutations that maybe the cause of a disease. The data collected through genetic mapping provides insights on plausible cases of diseases. Early detection gives a head start to change lifestyle and receive treatments.
For example, a mutation in the gene – LRRK2 indicates development of Parkinson’s disease, therefore anyone who inherits this gene has a chance of developing the disease. Similarly, celiac disease, breast cancer, psoriasis, or bipolar diseases are screened and mapped for genetic predispositions.
Gene editing methods such as CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) was created with the big data, to provide knowledge about how genome and diseases interact, in order to prevent diseases. Genetic mapping products such as 23andMe or Ancestry DNA are useful in obtaining the necessary genetic data for diagnosis.
b. HealthTech Tools
EHR (Electronic Health Records) enables a broad analysis of every disease based on the data gathered by the healthcare professionals. Symptoms, demographics, and treatments are observed by the medical professionals. At-risk demographics and risk aspects are targeted by the providers via medical tactics crafted for them, thus preventing or treating a disease. By leveraging EHR data analytics strategies such as sentiment analysis or cohort analysis, patients can be screened for a wide range of diseases and preventive measures can be taken for the same.
IoMT (Internet of Medical Things) enabled medical devices are a means of acquiring massive healthcare data for accurate treatments and comprehensive responses. For instance, Nokia Thermo, a smart thermometer is equipped with features such as tracking flu symptoms, which can help doctors to track flu to the patient. Thus, doctors can use the the data gathered from tracking symptoms to determine treatments and prevent outbreaks.
In the wake of the Covid-19 pandemic, the IoMT devices are utilized to track the symptoms of the Covid-19 virus in people, thus curbing infection rate. For instance, in December 2021, Telli Health, a leader in IoMT devices, announced the launch of it’s 4G cellular linked SpO2 pulse oximeter, for RPM (Remote Patient Monitoring) and SpO2, to track the Covid-19 patients.
Artificial intelligence based image readings are used to understand and stratify patients as per health risks. For example, smart thermometer developed by Kinsa, a health technology based in California, helps to detect Covid-19 hotspots. This product collects data via machine learning technology that processes data and enables providers to derive actionable insights.
c. Predictive Analytics
Healthcare providers utilize predictive analytics to prevent diseases via data driven approach. Analytical models utilize current data, scrutinize it, and interpret it for accurate predictions. For example, the STEM (Spatiotemporal Epidemiological Modeler) created by the IBM helps providers and scientists to estimate the trajectory of communicable diseases through computational tools and models.
Forecasting of diseases offers insights on the pathway and spread, thus alerting the healthcare resources and to create awareness. EPI (Epidemic Prediction Initiative) by CDC is an organization that utilizes data to anticipate trajectory and effects of diseases. EPI associates with various research teams to forecast short term and long term data and analyze them.
During the coronavirus pandemic, CDC collaborated with the University of Massachusetts Amherst to develop a Covid-19 Forecast Hub (a central repository for forecasts from research groups).
2. Augmenting Patient Engagement
To assure that patients participate in managing chronic diseases, healthcare facilities use patients’ data to design predictive risk scores and individual treatment plans.
“We see an improvement in outcomes when patients are actually engaged in their chronic diseases,” said Matthew Vitaska, Administrator of Outcomes Effectiveness and Patient Experience at Centura.
Furthermore, people suffering from mental diseases can avail benefits through treatment procedures offered by healthcare organizations.
For instance, Beacon Health Options, a behavioral health company in Boston, utilizes machine learning to gain insights from unstructured and structured data. This data is further used for patient care, who are suffering from mental sickness.
3. Detecting Patients with Chronic Conditions
As per a research by Marshall University, West Virginia, big data has a positive impact on chronic disease management and providing quality of care. Data improves patient outcomes and decreases hospital readmission too.
Physicians can utilize medical data to screen and detect patients with chronic illnesses. They segregate individuals who have the possibility of developing high-risk disease or who have a chronic disease condition.
After stratification, plans for arranging palliative care for patients are developed. For instance, rehabilitation centers for athletes undergoing recovery of a serious injury.
Detection of chronic conditions helps physicians to lessen the impact of terminal illnesses or injuries, thus scaling life expectancy and building quality life.
Final Thoughts
Data analytics is an effective tool to determine healthcare conditions and improve them for better quality of life. Through EHR and EMR, healthcare professionals can scrutinize healthcare data and predict diagnosis. Thus, preventing diseases and rendering solutions for the existing ones. To conclude, data analytics is quite effective in driving healthcare interventions.