Johns Hopkins Research Finds that Data Mining of Health Records useful to Reduce Physician and Treatment Mistakes
Johns Hopkins Research Finds that Data Mining of Health Records useful to
Reduce Physician and Treatment Mistakes
Diagnostic errors are one of the biggest patient safety issues we face in healthcare
and these very often lead to medical errors. It is often caused by a diagnosis that is
missed, wrong or delayed, as detected by some later definitive test or finding. These
costly errors can result in delay or failure to treat a condition, or to provide
treatment for a condition that doesn’t actually exist. On evaluating 25 years of U.S.
malpractice claim payouts, Johns Hopkins University researchers found that
diagnostic errors are the reason for death or permanent damage for around 160,000
patients every year. These errors are also the leading reason for malpractice claims
that are paid to physicians.
The notable thing is that diagnostic errors are more easily preventable than any
other medical mistakes. Automation is a practical solution that can address this
problem. Computers can be used to check medical records and identify possible
errors, and also to prompt doctors to follow up on risky test results. Helpful online
services that can assist doctors with diagnoses and tests/devices that can help them
identify conditions/illnesses more accurately are other solutions. Doctors are being
made aware of the risk involved in holding on to one diagnosis and not looking
further. They need to keep an open mind in cases that appear confusing with
conflicting evidence. The new healthcare law that lays emphasis on coordinated care
is expected to improve diagnosis while also ensuring that patients consult specialists
when they are required to do so.
Effort is on to develop techniques that can identify and measure diagnostic errors.
Data mining from electronic records can help identify information such as lab results
that may have escaped notice.
Data mining is one of the powerful techniques available for accurate disease
diagnosis. When a large amount of medical data is available, more powerful data
analysis tools can be used to mine useful information. For example, researchers are
employing statistical and data mining tools to assist healthcare providers diagnose
heart disease accurately.
Data mining techniques are also employed for the prevention of diseases such as
cancer, stroke, cardiac arrest, and diabetes. It helps in the prevention of hospital
errors, in early detection and prevention of diseases, and in the detection of
fraudulent insurance claims.
Data mining techniques for diagnosis varies with disease. For example, the most
frequently used techniques for the diagnosis of heart disease are naïve bayes,
decision tree, and neural network. Kernel density, automatically defined groups,
bagging algorithm, and support vector machine are some other techniques used in
heart disease diagnosis.
Studies reveal that diagnostic errors occur usually due to problems in ordering
diagnostic tests, history taking, examination, and referrals. About 14% of immediate
deaths, 19% of serious permanent damage, and 16% of serious harm are some of
the end results. Such a situation can be prevented using data mining which includes
the following steps:
Problem Definition (Identifying goals)
Data Exploration (Analyzing Quality of Data)
Data Preparation (Cleaning Data)
Modeling (Applying data mining algorithm)
Evaluation and Deployment (Extracting Information)
Although applying data mining in disease diagnosis is beneficial, not much research
has been conducted in this area to identify treatment plans. Anyway, researchers
claim that incorporating these techniques can improve the efficiency of the physician
or practitioner. As data mining consumes a lot of time and effort, whether to identify
missed information or to make an accurate diagnosis, it is better to outsource this
job to a reliable company with expertise in data mining, rather than setting up a
team in-house. Relying on professional data mining services will help to complete the
task accurately and within quick turnaround time. Avoiding mistakes in the
evaluation of health records could reduce diagnostic errors and thereby mortality
rate to a considerable extent.