Healthcare Data Analytics

Healthcare Data Analytics

Bill Hersh MD

After reviewing this presentation, viewers should be able to:

Discuss the difference between descriptive, predictive and prescriptive analytics

Describe the characteristics of “Big Data”

Enumerate the necessary skills for a worker in the data analytics field

List the limitations of healthcare data analytics

Discuss the critical role electronic health records play in healthcare data analytics

Learning Objectives

One of the promises of the growing clinical data in electronic health record (EHR) systems is secondary use (or re-use) of the data for other purposes, such as quality improvement and clinical research

Interest in healthcare data has grown exponentially due to EHR incentives after the HITECH Act and the addition of genomic information that will eventually be integrated with EHRs

Introduction

The term analytics is achieving wide use both in and out of healthcare. A leader in the field defines analytics as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions”

IBM defines analytics as “the systematic use of data and related business insights developed through applied analytical disciplines to drive fact-based decision making for planning, management, measurement and learning

Introduction

Descriptive – standard types of reporting that describe current situations and problems (how many uninsured patients do we have with type 2 diabetes?)

Predictive – simulation and modeling techniques that identify trends and portend outcomes of actions taken (can we predict who will be readmitted for heart failure in the next 30 days?)

Prescriptive – optimizing clinical, financial, and other outcomes (of those patients identified as high risk for readmission for heart failure is it more cost effective to case manage in the hospital or at home?)

Different Types of Analytics Increasing functionality and value

Machine learning is the area of computer science that aims to build systems and algorithms that learn from data

Data mining is defined as the processing and modeling of large amounts of data to discover previously unknown patterns or relationships

Text mining, a sub-area, applies data mining techniques to mostly unstructured textual data

Analytics Concepts

Provenance, which is where the data originated and how trustworthy it is for large-scale processing and analysis

Business intelligence, which in healthcare refers to the “processes and technologies used to obtain timely, valuable insights into business and clinical data”

Learning health system, where data can be used for continuous learning to allow the healthcare system to better carry out disease surveillance and response, targeting of healthcare services, improving decision-making, managing misinformation, reducing harm, avoiding costly errors, and advancing clinical research

Analytics Concepts

Another related term is big data, which describes large and ever-increasing volumes of data that adhere to the following attributes:

Volume – ever-increasing amounts

Velocity – quickly generated

Variety – many different types

Veracity – from trustable sources

While big data is considered a buzz word by some, we are having to deal with terabytes and petabytes of information today. With the addition of genomics big data will escalate

Big Data

Healthcare organizations are generating an ever-increasing amount of data. In all healthcare organizations, clinical data takes a variety of forms, from structured (e.g., images, lab results, etc.) to unstructured (e.g., textual notes including clinical narratives, reports, and other types of documents)

For example, it was estimated by Kaiser-Permanente in 2013 that its current data store for its 9+ million members exceeds 30 petabytes (petabyte = 1024 terabytes) of data

Big Data

Another example is CancerLinQ that will provide a comprehensive system for clinicians and researchers consisting of EHR data collection, application of clinical decision support, data mining and visualization, and quality feedback

Lastly, IBM’s Watson is now focusing on healthcare, specifically Oncology so that massive amounts of cancer information/research can be analyzed and applied to individual patient decision making

Big Data

The Analytics Big Data Pipeline According to Kumar et al

One begins with multiple data sources, that are extracted and cleansed and normalized

Statistical processing prepares the data for output

Finally, the data helps generate descriptive, predictive and prescriptive analytics

Accountable care organizations (ACOs) provide incentives to deliver high-quality care in cost-efficient ways that will require a robust IT architecture, health information exchange (HIE) plus analytics. This approach would be used to predict and quickly act on excess costs

As one pundit put it: ACOs = HIE + Analytics

Big Data Big Data will Drive ACOs

Data generated in the routine care of patients may be limited in its use for analytical purposes. For example, data may be inaccurate or incomplete. It may be transformed in ways that undermine its meaning (e.g., coding for billing priorities)

It may exhibit the well-known statistical phenomenon of censoring, i.e., the first instance of disease in record may not be when it was first manifested (left censoring) or the data source may not cover a sufficiently long time interval (right censoring)

Challenges to Data Analytics

Data may also incompletely adhere to well-known standards, which makes combining it from different sources more difficult

Clinical data mostly allows observational and not experimental studies, thus raising issues of cause-and-effect of findings discovered

Research questions asked of the data tend to be driven by what can be answered, as opposed to prospective hypotheses

Challenges to Data Analytics

Data are not always as objective as one might like, and “bigger” is not necessarily better

There are ethical concerns over how the data of individuals is used, the means by which it is collected, and the possible divide between those who have access to data and those who do not

Who owns the data and who can use it?

Challenges to Data Analytics

There is an emerging base of research that demonstrates how data from operational clinical systems can be used to identify critical situations or patients whose costs are outliers

There is less research, however, demonstrating how this data can be put to use to actually improve clinical outcomes or reduce costs. Studies using EHR data for clinical prediction have been proliferating

Research and Application of Analytics

One common area of focus has been the use of data analytics to identify patients at risk for hospital readmission within 30 days of discharge. The importance of this factor comes from the US Centers for Medicare and Medicaid Services (CMS) Readmissions Reduction Program that penalizes hospitals for excessive numbers of readmissions

This has led to research using EHR data to predict hospital readmissions. Thus far, the results are mixed and several examples of trials are included in the textbook chapter

Research and Application of Analytics

Research and Application of Analytics Scenarios for EHR Data Analysis

Predicting 30-day risk of readmission and death among HIV-infected inpatients

Identification of children with asthma

Risk-adjusting hospital mortality rates

Detecting postoperative complications

Measuring processes of care

Determining five-year life expectancy

Detecting potential delays in cancer diagnosis

Identifying patients with cirrhosis at high risk for readmission

Predicting out of intensive care unit cardiopulmonary arrest or death

Identifying patients who might be eligible for participation in clinical studies

Determining eligibility for clinical trials

Identifying patients with diabetes and the earliest date of diagnosis

Predicting diagnosis in new patients

Research and Application of Analytics Identifying Patients for Research Using EHR Data

Virtual Data Warehouse (VDW) Project was able to demonstrate a link between childhood obesity and hyperglycemia in pregnancy

United Kingdom General Practice Research Database (UKGPRD), a repository of longitudinal records of general practitioners, was able to demonstrate the ability to replicate the findings of the Women’s Health Initiative and RCTs of other cardiovascular diseases

Research and Application of Analytics Use EHR Data to Replicate Randomized Controlled Trials

Other data repositories have helped to predict a variety of cancers, risk for venous thromboembolism (blood clots) and even rare medical disorders

Note the info box in the next slide that discusses data analytics by the Veterans Health Administration (VHA)

Research and Application of Analytics Use EHR Data to Replicate Randomized Controlled Trials

Case Study: Veterans Health Administration (VHA) The VHA is a large healthcare system with a long track record of EHR use (VistA). In 2013, the VHA had 30 million unique electronic patient records with 2 billion clinical notes (100,000 notes added daily). They also have had a corporate data warehouse (CDW) of structured data which allows them to analyze clinical and administrative data for patients at risk of hospital admission (from falls, coronary disease, PTSD, etc.). Analytics are run once weekly on all primary care patients looking for “at risk” patients who would likely require more coordinated care using care managers, home health and telehealth. In 2012, VHA researchers reported in the American Journal of Cardiology on the use of predictive analytics on heart failure patients. Specifically, using six categories of risk factors derived from the EHR they could successfully predict which patients were at risk of hospitalization and death. According to Dr. Stephen Fihn, Director of Analytics and Business Intelligence for the VHA, the VHA is embarking on a 24-month pilot project to expand the use of healthcare data analytics. They will use natural language processing and machine learning to analyze patient records to aid in diagnosis, identify dangerous drug-drug interactions and optimally design treatment strategies.
Research and Application of Analytics Using Genomic Information and EHRs

Researchers have carried out genome-wide association studies (GWAS) that associate specific findings from the EHR (the “phenotype”) with the growing amount of genomic and related data (the “genotype”) in the Electronic Medical Records and Genomics (eMERGE) Network

eMERGE has demonstrated the ability to identify genomic variants associated with atrioventricular conduction abnormalities, red blood cell traits, white blood cell count abnormalities, and thyroid disorders

More recent work has “inverted” the paradigm to carry out phenome-wide association studies (PheWAS) that associated multiple phenotypes with varying genotypes

Genome-wide and phenome-wide association studies are also discussed in the chapter on bioinformatics

Research and Application of Analytics Using Genomic Information and EHRs

There has been little focus on the human experts who will carry out analytics, to say nothing of those who will support their efforts in building systems to capture data, put it into usable form, and apply the results of analysis

Where will these workers come from and what will be the education of those who work in this emerging area, that some call data science?

We do know that data analytics experts are in high demand

Role of Informaticians in Analytics

From basic biomedical scientists to clinicians and public health workers, those who are researchers and practitioners are drowning in data, needing tools and techniques to allow its use in meaningful and actionable ways

Dr. Hersh believes that a strong background in Health Informatics or Biomedical Informatics is the best preparation for the healthcare data analytics field

Role of Informaticians in Analytics

Data science is more than statistics or computer science applied in a specific subject domain. It requires an understanding of data, its varying types, and how to manipulate and leverage it

The field requires skills in machine learning, a strong foundation in statistics (especially Bayesian), computer science (representation and manipulation of data), and knowledge of correlation and causation (modeling)

Role of Informaticians in Analytics

A report by McKinsey consulting states that there will soon be a need in the US for 140,000-190,000 individuals who have “deep analytical talent” and an additional 1.5 million “data-savvy managers needed to take full advantage of big data”

An analysis by SAS estimated that by 2018, there will be over 6400 organizations that will hire 100 or more analytics staff

Another report found that data scientists currently comprise less than 1% of all big data positions, with more common job roles consisting of developers (42% of advertised positions), architects (10%), analysts (8%) and administrators (6%)

The Need for Data Analytics Experts

The technical skills most commonly required for big data positions as a whole were NoSQL, Oracle, Java and SQL

PriceWaterhouseCoopers noted that healthcare organizations need to acquire talent in systems and data integration, data statistics and analytics, technology and architecture support, and clinical informatics

Business knowledge is also useful

The Need for Data Analytics Experts

Programming – especially with data-oriented tools, such as SQL and statistical programming languages

Statistics – working knowledge to apply tools and techniques

Domain knowledge – depending on one’s area of work, bioscience or health care

Communication – being able to understand needs of people and organizations and articulate results back to them

The Need for Data Analytics Experts What Skill Sets Should Universities Train For?

Healthcare data has proliferated greatly, in large part due to the accelerated adoption of EHRs

Analytic platforms will examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems

Analytics is necessary to transform data to information and knowledge

Accountable care organizations and other new models of healthcare delivery will rely heavily on analytics to analyze financial and clinical data

There is a great demand for skilled data analysts in healthcare; expertise in informatics will be important for such individuals

Conclusions

Welcome to originalessaywriters.com, our friendly and experienced essay writers are available 24/7 to complete all your assignments. We offer high-quality academic essays written from scratch to guarantee top grades to all students. All our papers are 100% plagiarism-free and come with a plagiarism report, upon request

Tell Us “Write My Essay for Me” and Relax! You will get an original essay well before your submission deadline.