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Data Collection Methods that aid Population Health Success

Apr 20, 2021
Data Collection Methods in Health Sector

Data Collection Methods

With more emphasis on value-based care than ever, medical professionals are under tremendous pressure to improve health outcomes. Collecting medical data from different sources is a critical component of the industry, particularly when that data needs to be continually monitored and analyzed to provide the best care.

Data collection methods is the first step in the population health initiative. Healthcare data is spread across a broad spectrum; from claims to electronic health records to input from the Internet of Things, healthcare organizations have a whole lot of data on their hands.

For a successful population health initiative, organizations need to have the full patient picture available to make the best healthcare decisions. This prominent care initiative involves categorizing patients based on certain characteristics – gender, age, and chronic conditions, to name a few. Later, this data of the individuals involved is monitored and analyzed to improve their health status.

What is PHM and how it works?

Until recently, healthcare organizations were satisfied by having access to patient’s clinical data collection methods – the care patients were receiving within their system. But things have changed now. A broad range of data sources is required to understand the care patients receive across the continuum, making it critical for health systems to strategically invest in data infrastructure.

Population Health Management (PHM) can be defined as the collection of patient data across multiple sources, its analysis into a single patient record, deliver care targeted to the individual needs, and steps to improve both clinical and financial outcomes. These real-time insights will help clinicians & administrators to address care gaps within the patient population.

PHM usually begins with collecting clinical data about patients and then sorting it into categories based on their medical history and risk. Providers must focus their initial data-gathering efforts on the following types of information if they wish to succeed with population health management.

1. Claims Data

Claims include a breadth of information from across multiple healthcare organizations, like patient demographics, diagnosis codes, and service dates, which allow providers to get a better picture of patients’ history, their concerns, and treatment cost. Not just this, providers can track the preventive services that patients have had in the past like flu vaccines. This data provides opportunities for healthcare systems to improve patient health and satisfaction.

However, claims have their limitations. Generally, this data is months or years old, limiting its value for proactive population health monitoring. A lot of important data is also missed – important clinical details and the process of care.

2. Patient-Generated Health Data (PGHD)

As the interest in personalized healthcare rises, more and more patients have started using wearables and remote monitoring devices, to share information with care providers in real-time through a secure portal. PGHD includes an individual’s medical history, current symptoms, laboratory test results, information about their lifestyle and is very helpful in improving patient engagement with their care plans.

Making use of these unstructured data collection methods can be difficult. Judicious use of this data can assist care providers in managing chronic care needs and accordingly develop new treatment plans.

Healthcare systems are seeking help from IT companies to integrate PGHD monitoring and alerting into their software products. And as the focus is increasing more on value-based care, patient patient-reported outcomes are becoming crucial information for providers who are seeking ways to provide the best possible long-term results.  

3. Electronic Health Record (EHR) Data

EHRs contain details about the process of care and volunteer patient concerns that may not have resulted in diagnoses. They also include vital signs, medications, allergies, imaging reports, lab results, and other documents as static PDF files, which cannot be analyzed without additional processing. Having access to EHRs, providers can answer a wealth of population health management questions.

EHRs have their downsides, however. Data analytics automation in these systems are often captured in unstructured notes that can be incomplete, difficult to extract and analyze.

With these data collection methods, healthcare organizations should consider investing in comprehensive PHM platforms that can generate actionable insights. Doing so will allow providers to have a better understanding of the patient population and follow necessary approaches to improve their health status.

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