Innova Solutions > Perspectives > Data-driven healthcare > Chronic-disease Management

Chronic disease management is the most expensive, fastest-growing, and most intractable problem facing healthcare providers in every nation on Earth. More than 95 percent of the world’s population suffers from one or more chronic health problems, according to the Global Burden of Disease Study 2013, published this week in The Lancet, as patients live longer with a higher number of significant, expensive, debilitating health problems.

In the United States, the numbers are equally grim. More than nine percent of Americans are living with diabetes – a third of patients may not even know it. In some Southern states, the diabetes rate surpasses 13 percent. In around 20 places in the country, the obesity rate is over 30 percent. Heart disease, cancer, COPD, and hypertension follow in the wake of poor lifestyle choices. Enormous and all-too-familiar stresses on primary care providers, hospitals, emergency rooms, and public health departments, not to mention on the private and public payers that have to shell out trillions of dollars each year to pay the bills.

Healthcare data isn’t just big. It’s broad and deep, but it’s also incredibly messy. Haphazard health IT adoption, non-existent interoperability, a dearth of data standards, differing notions of data governance and quality, and a preoccupation with volume-based fee-for-service reimbursement have made it difficult to extract meaningful insights from the information that exists in walled gardens or proprietary pools, leaving informaticists and data scientists with some steep cliffs to climb.

Savvy healthcare organizations are recognizing that big data analytics may be the life raft they need to survive this stormy financial climate. Quality reporting and patient outcomes become increasingly vital to revenue cycle management and market share. Healthcare big data analytics is helping providers understand everything from how many cases of heart failure are within a provider’s purview to which patients are most satisfied with their chronic disease care.Using big data and clinical analytics to identify high-risk patients is no small thing, but it’s only the start of an impactful chronic disease management program.

Providers must take the next step by reaching out to those patients, managing their care coordination, making community support available, and addressing the underlying social needs or behavioral problems that often lead to the development of chronic disease in the first place.

Personalized medicine

The proliferation of smart devices and exponentially decreasing cost to sequence the human genome along with growth of electronic communication via social media is generating an explosion of health-related data that is specific to a given individual.

This is particularly relevant to the practice of personalized medicine that aims to individualize the diagnosis of a disease and therapy according to the individual patient’s characteristics (e.g., clinical co-morbidities and genetics), as opposed to decisions based on evidences and guidelines derived from population-based studies and clinical trials.

The overarching goal of “personalized medicine” is to create a framework that leverages patient EHRs and OMICS (primarily genomics) data to facilitate clinical decision-making that is predictive, personalized, preventive, and participatory.

In personalized medicine, clinicians can:

  • Validate medical treatments and response to therapies. They can predict the possible side effects and detect adverse events to treatments based on genetic make up for each patient in comparison to other similar patients.
  • Describe better-targeted therapies for individuals. They can determine – apriori – which drug(s) will work better with each patient, instead of adopting an empirically driven approach of trial-and-error.
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Make better decisions on risk prediction and focus on prevention rather than disease management. Collecting genetic information from prenatal testing can be useful to determine possible diseases in the future that can either be avoided or adequately controlled.To study personalized medicine, we need to navigate and integrate clinical information like medical diagnosis, medical images, patient histories) and biological data (e.g., gene, protein sequences, functions, process, and pathways) that have diverse formats and are born from different and heterogeneous sources. However, having a large amount of data does not solve any problem in personalized medicine. We need to summarize or abstract data in a meaningful way to translate data to information, knowledge, and wisdom. We still need to investigate to effectively represent a large amount of data for making use of them in decision-making. To handle a large amount of data, tools such as Hadoop systems can help us to speed up querying, data processing.

Three Critical Ways a Precision Medicine Portfolio Drives More Personalized Care –

Three Critical Ways a Precision Medicine Portfolio Drives More Personalized Care –

#1: Longitudinal Record of the Patient Journey
#2: Access to Real-World Evidence
#3: More Effective Research Study Design and Patient Identification for Clinical Trial Recruitment

Population health

Population health management has become increasingly important for health systems as the industry shifts its focus towards value-based care. Because health systems have large amounts of data at their disposal, utilizing that data effectively to inform population health management strategies is critical.

Healthcare organizations (HCOs) involved in value-based care are trying to bend the cost curve by aggressively managing high-cost, high-need patients while eliminating waste and inefficiency without sacrificing quality. To achieve these goals of population health management, HCOs need accurate, timely data and robust analytics that show providers where to place their efforts and how to improve their performance.

At the highest level of population health management, HCOs must use analytics to categorize their populations by health risk or disease burden. It enables them to identify the patients who will generate most of the HCO’s health costs in the near term. A robust population health program will automatically identify patient care gaps, using a combination of claims data and clinical data from multiple sources. It will also use automated messaging via phone, email, or text to alert patients with care gaps that they need to make appointments to see their providers.

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Population Health Management Systems have three tasks:

1. Gathering data from multiple sources and transforming this data into a usable format.
2. Applying analytics to themetrics, reports, trends, graphs, work lists.
3. Managing the care for the population – worklists for care managers, alerts and reminders for providers, postcards to patients, reminders to patients on their electronic patient portals.

Barriers to sharing data

The most significant barrier to the holistic analysis of healthcare data has to do with ensuring security, Privacy, and Trust. The consensus view is that patients are happy to share their data if it results in better health outcomes. However, concerns remain about the risk that data misuse can lead to discrimination against people with health issues.The fear of security breaches only compounds the challenge of getting patients to trust the health industry. For instance, in May 2017, there was a Wannacry ransomware attack on NHS in the UK that resulted in many hospitals not able to access their patient records and had to shut down their non-emergency operations.

The healthcare industry pays more than $492 per lost or stolen data on average, which is highest across many verticals. The US has made steps in improving the security of Healthcare data as well as making data readily available for patients. The interoperability HIPAA Final Rule 2020 provides a better platform for all patients to access their health data for the past five years to make better judgments for themselves. It also helps the data analytics firms to run cognitive algorithms and provide deeper insights to all entities of healthcare.

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