The role of data analytics in healthcare is pretty straightforward: Pull the signal from the noise.
Whether you need to advance research, improve the quality of care, avoid risk, or do anything in between; you have access to troves of data. However, just having the data won’t help your organization much if you don’t have an organized pattern to arrange, analyze, and translate it.
This is one of the many reasons why data analytics holds so much significance in healthcare. By making use of analytical techniques, healthcare providers can not only make sense of the past, but also chart a path for the entire healthcare community to benefit from.
This technology has also depicted promising results when it comes to driving successful patient interventions and ameliorating overall outcomes.
In this piece, we will be looking at a few ways data analytics aids in augmenting healthcare interventions and streamlining care delivery. But before that, let us get to understand-
Different Types of Healthcare Interventions
Interventions can be classified into two broad categories:
(1) preventive interventions are those that prevent a disease from occurring and thus reduce the incidence (new cases) of that particular disease, and
(2) therapeutic interventions are those that treat, alleviate, or put off the effects of a particular disease, once it is under way, and thus reduce the case fatality rate, or the disability or morbidity associated with that disease.
Some healthcare interventions may leverage both effects to treat patients.
Let us take a brief look at each of the categories:
1) Preventive Interventions
Preventive intervention includes a number of activities aimed at mitigating threats or risks to health. It can broadly be classified into three different sub-categories:
- Primary prevention
Primary prevention aims to avert a disease or injury before it even starts showing first signs of occurrence. This is done by altering unhealthy or unsafe behaviours that can lead to disease or injury, preventing exposures to hazards that cause disease or injury, and increasing resistance to disease or injury should exposure occur.
- Secondary prevention
Secondary prevention aims to lower the effect of a disease or injury that has already occurred. This is usually done by detecting and treating a condition as soon as possible to slow down its progress, educating the patient at a personal level to prevent reinjury or recurrence, and formulating programs to help return people to their original state and function to prevent chronic ailments.
- Tertiary prevention
Tertiary prevention aims to ease the effect of an ongoing illness that has enduring effects. This is usually done by helping people manage complex, often-long term health problems (e.g. permanent impairments, chronic diseases) in order to restore their ability to function normally, their life expectancy and their quality of life.
2) Therapeutic Interventions
A therapeutic intervention is nothing but an attempt made by groups or individuals to improve the well-being of a patient who is either in need of help but refuses it; or is otherwise unable to accept or ask for help.
Strategies to cure patients largely vary depending on the severity, type, and duration of therapeutic needs. Here are a few of the most commonly employed ones:
- For addiction
A usual approach to help someone who might have refused to participate in changing habitual and harmful behavior is group intervention in the past. A mediated, supportive meeting is often staged to support such an individual.
- Individual Behavioral Interventions
The strategies that fall under this category usually include time-limited activities, positive reinforcement, and immediate behavior reinforcement. When attempting to help an individual who has had difficulty with inappropriate reactionary behaviors in the past, these strategies are vital for growth and safety.
- Crisis Intervention
When someone has suffered a trauma, a therapist or qualified professional can support a healthier processing of an extreme situation. Helping someone after a crisis occurs helps them to gain support and a clear perspective when it is needed most.
Now that you’re familiar with the different types of interventions available today, let’s look at:
The Role of Data Analytics in Improving Outcomes of Interventions
The secret to successful population health management lies with identifying the right high-risk and rising-risk patients. To reliably select the patients most likely to benefit from interventions, health systems need data from across the continuum of care.
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Analytics-driven care management tools can help healthcare providers identify the right patients for their programs and deliver optimal care in key ways:
1) Identification of Rising Risk Patients
Patient stratification technology leverages data to help healthcare organizations locate those patients that haven’t been identified as high risk, but who could become high-priority, high-cost patients.
With data from Electronic Medical Records (EMR), it has now become fairly easy to identify patients with chronic conditions by how frequently they visit their healthcare provider or the Emergency Department (ED), or those that might have an acute condition based on hospitalization records.
Patient stratification also uses risk models to help providers decide which patients are at an increased risk of becoming ill in the future. With this understanding of rising risk, the physician in-charge can then determine where to strategically focus efforts. The organization can also customize algorithms to target specific patients as their population health strategy develops.
2) Identification of Patients for Palliative or Hospice Care
Healthcare providers can leverage data analytics to develop a strategy wherein one can identify multiple admissions, or certain diagnoses data, including terminal diagnosis or multiple admissions for an underlying condition, to determine when patients may need palliative or hospice care.
Primary care physicians must work collaboratively with palliative and hospice agencies to underline the level of care needed, especially for patients with terminal diagnoses and severe symptoms who require more intensive palliative management.
Care planning tools can then be used to customize hospice assessments and can also include palliative assessments (again, customizable as per the patient’s requirement).
3) Identification of Patients with Underlying Chronic Conditions
Data gathered through analytics can also help healthcare providers in identifying patients with chronic conditions, such as chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), diabetes, etc.
Providers can then intervene and ultimately reduce medical costs while improving care for this population.
More than two-thirds of all healthcare costs are for treating chronic diseases. The National Council on Aging estimates that close to 95% of healthcare costs for older Americans can be attributed to chronic diseases.
Therefore, analytics can play a significant role in ameliorating outcomes for this particular category of patients.
4) Boosting Patient Engagement
Patients with chronic diseases must engage in management strategies and adhere to treatment plans in order to maintain their health and keep care costs low. Self-management interventions powered by analytics can allow patients to independently keep track of their health and actively engage in their own care.
To make sure patients are actively participating in chronic disease management plans, organizations can use patient data to develop predictive risk scores and design individualized treatment strategies.
“We tend to identify quite a few people who have different chronic conditions or other issues that call for enhanced management. When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyle, such as nutrition or exercise,” Lillian Dittrick, Fellow of the Society of Actuaries told Health IT Analytics in a recent interview.
Patients with mental illnesses can also benefit from tailored treatment plans, and organizations can use existing data to design individualized care strategies.
Here are a few supporting case studies that will help you better comprehend the benefits that come alongside the adoption of data analytics in interventions:
Real Life Application of Analytics in Healthcare Interventions
Case Study 1: Keralty Organization, Columbia
Keralty organization, based out of Columbia, is formed by a group of health services and insurance companies that together develop an integral health model. The purpose of this model is to produce health and well-being solutions for people across age groups.
With a robust data analytic component, the organization was able to classify opportunities for healthcare development and to refine the way in which care is both coordinated and delivered throughout its network of smaller medical facilities and hospitals.
The results depicted remarkable cost savings in the mere span of 6 months.
The organization now utilizes this platform to generate meaningful, timely, and actionable insights to improve the quality of care for patients and drive change in the way care is delivered. The organization uses the data for prioritization of the care coordination activities, risk-stratification of the network’s population, and prevention activity’s interventions.
Risk stratification was completed for all patients, enabling care managers to identify individuals at various risk levels for high-cost utilization and unnecessary services, enhancing patient experience as well as health outcomes. The analytical component also facilitates access to specialty care and community-based services, and reduces unnecessary visits.
Other benefits include 3% increase in the detection of high-risk patients with primary care, 20% increase in the number of patients with ongoing care managed, and 10% percent reduction in emergency department utilization per member among care managed patients.
Case Study 2: Leveraging Analytics to Develop an Intervention Prediction Model
Data analytics have also been applied to rare cardiovascular diseases (CVD). One study recently published on Pubmed developed an intervention prediction model for Hypoplastic Left Heart Syndrome (HLHS).
HLHS is a rare form of fatal heart disease in infants, which often requires surgery. Post-surgical evaluation is critical as patient conditions can shift in a very short span of time.
Indicators of wellness of the patients are not easily measurable, but inferences can be made based on measurable physiological parameters including heart rhythm, pulse, systemic blood pressure, urine output, common atrial filling pressure, physical exam, and systemic and mixed venous oxygen saturations.
To help healthcare providers in decision-making, the researchers developed a prediction model by identifying the correlation between physiological parameters and interventions. They collected 19,134 records of 17 patients in Pediatric Intensive Care Units (PICU). Each record contained different physiological parameters measured by devices and noted by nurses.
For each record, a wellness score was calculated and assigned by domain experts. After classifying the data using a set algorithm, decision rules were extracted for each wellness score to help in developing intervention plans.
A new measure for feature selection—Combined Classification Quality (CCQ)—was developed by considering the effect of variations in a feature value and distinct outcome each feature value leads to.
Authors showed that higher value of CCQ leads to higher classification accuracy which is not always true for commonly used measure classification quality (CQ). For example, two features with a CQ value of 1 leads to very different classification accuracy—35.5% and 75%. Same two features had CCQ value 0.25 and 0.40, features with 0.40 CCQ produced 75% classification accuracy.
Data analytics is continuing to emerge as a promising solution that can push the boundaries of healthcare delivery and how it can be used to improve patient outcomes.
Right from artificial intelligence to machine learning to natural language processing, the true future of healthcare data analytics is in wielding this technology for greater impact.
This is just a taste of what’s in store for healthcare interventions. With technology advancing at a rapid clip, new discoveries that can create even greater impact are inevitable.