How Big Data Works with In-home Healthcare to Improve Outcomes

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At-Home healthcare is rapidly becoming one of the fastest growing models in the healthcare industry today. The patient population served by remote healthcare is enormous.

The ultimate goal of this model is to aid patients lead their lives with greater independence and help them remain at home, therefore avoiding hospitalization or re-hospitalization, especially for patients that are elderly or have underlying chronic conditions.

In 1960, less than 1 million Americans were 65 years or older. By 2000, this number further increased to 4.2 million and it is estimated that by 2034, close to 77.0 million Americans will be 65 years and older, as compared to 76.5 million under the age of 18.

With the increase in demand of home healthcare systems for an aging population that now makes up a huge percentage, it is extremely crucial to identify aspects that affect patient health and safety. This is where big data comes into the picture.

With the help of big data technology, healthcare providers can develop highly precise diagnoses and treatments. This would lead to higher quality care at lower costs and better outcomes.

In this piece, we will be looking at a few ways big data helps with in-home healthcare. 


Home healthcare providers can leverage big data to conduct regular surveillance of patients so that emergency situations can be identified and averted even before they start emerging on the surface. In fact, right now, too much focus of the healthcare industry is on prevention than cure. 

According to Dr. Jeffrey Brenner of Camden, New Jersey, big data could also be used to analyze numerous patient problems and provide effective solutions.

Dr. Brenner’s organization, Camden Coalition of Healthcare Providers, identifies patients who are frequent users of hospital facilities with the help of the data collected from such sources as doctor consultations, ambulance calls, and patient records.

Experts at the organization then furnish solutions that could prevent emergency situations from occurring.

On a more individual basis, with the help of big data and predictive analytics, healthcare providers that offer in-home services can easily track patients who are unable to manage a chronic illness properly.

An article published in the Health Affairs magazine suggests that healthcare providers could extract health-related information from the smartphones of patients to identify whether the patient has been suffering from a disease. 

This could be done based on the individual’s browsing habits, online behavior and other relevant data.

Once that has been done, the healthcare provider needs to be able to conclude whether the patient is approaching an emergency situation and needs to quickly find out the steps that need to be taken to prevent such a thing from happening by providing prompt aid.

Pattern Identification

Almost every illness has a certain pattern. For instance; flu, hay fever, or asthma usually occur due to changes in weather conditions, extreme temperatures or dust and other such allergens.

People who suffer from asthma or some form of allergy are more likely to fall sick during these periods. 

Given the surge in the amount of health data generated by healthcare organizations on a regular basis, home healthcare providers can retrieve data about patients who have specific illnesses and identify triggers as well as patterns that lead to the onset of the illness. 

Wayne Parslow, in his article on Healthcare Network, reports that Britain’s National Health Service (NHS) is considering the use of remote patient monitoring (RPM) devices in conjunction with big data to predict patient behaviors and patterns to ameliorate their health. 

Predictive intelligence has huge potential for the NHS. Imagine if a doctor could tell a patient that they could add six years to their life expectancy if they altered a behavior or changed a medication in order to reduce their high risk of developing a particular condition – a risk identified through big data,” says Parslow.

In the United States, pattern identification has proved to  be an important milestone in the detection and management of flu. The Centers for Disease Control and Prevention gather data from close to 7,00,00 doctors from all over the country.

This congregated data then helps the public health officials to stay connected with the flu management program, detect the strains that need to be included in the flu vaccines and identify the times when the flu vaccines need to be changed. 

Similar practices can be used for in-home healthcare services, which will then ameliorate overall health outcomes.


Big data can be highly effective at helping healthcare providers prevent hospital readmissions.

The Texas Health Harris Methodist Hospital Hurst-Euless-Bedford conducted an experiment and discovered that patient data could significantly mitigate emergencies and readmissions.

The 213-bed hospital collected and analyzed the medical records of nearly 14,000 patients to develop an algorithm that provided the numeric likelihood of patients that could be readmitted in the future. 

Patients who scored above a certain algorithm were the likeliest to have readmissions in the future. “It takes all these pieces of data from the EHR, and it has an algorithm, and tells us which patient is at higher risk for heart failure”, said Dr Susan Land, the Chief Medical Officer of the hospital.

The hospital also set up an intensive care program that furnished home healthcare services for such patients. The care provided was intensive and it showed results: the hospital was able to reduce readmissions by almost 50% since the scoring system has started.

This is only one among the many examples that demonstrate the significance of big data in preventing emergencies and readmissions. 

What Lies Ahead

The question that lies ahead is- how exactly can home healthcare providers leverage big data to ameliorate care delivery and achieve optimal results in doing so? The answer is pretty simple: providers need to invest a decent amount of resources on technology and training. 

Today, manual data collection is facing severe challenges in the face of the data onslaught. Providers also need to access and analyze all the data they can about a patient before initiating his/her healthcare program.

This helps the provider to customize their services to suit both- the needs and the budget of the patient. 

Big data is set to make some incomprehensible transformations in the field of healthcare in the years to come. Healthcare delivery will rapidly get streamlined with this technology in ways we cant even think of right now. 

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