There are additional challenges that are specific to healthcare. Healthcare data are bound by strict privacy rules govern by federal regulations, most notably Health Insurance Portability and Accountability Act (HIPAA) (Hirsch & Deixler, 2014). The rules are complex, and the penalties are high for violations. The federal government is concerned about privacy violations with the development of digital records and, in January 2013, expanded privacy coverage in the omnibus rule amending HIPPA, which was the first ruling following the HITECH Act (Clark & Bilimoria, 2013). These worries are not unfounded given the recent hacker attacks on insurers (Anthem, Excellus) and providers (UCLA, Community Health) exposing almost 100 million patients
…show more content…
The federal certification of EHR systems involves three timed stages; the majority of hospitals and providers have attested to Stage 1. However, the attestations for Stage 2 are lagging with only 40% of hospitals and 10% of providers attesting, highlighting the difficulty of the requirements (Miliard, 2015).
In addition, there is a large portion of data being captured outside the EHR (Ward, Marsolo, & Froehle, 2014). This includes laboratory information management systems, diagnostic imaging systems, human resource systems, and accounting systems. True application of healthcare analytics will require integrating each of these platforms into a single data warehouse (Groenfeldt, 2012). The EHR may capture the various supplies used on a patient, but the accounting system knows the cost of each …show more content…
As just described, the current application of healthcare analytics attacks the first two issues, but not necessarily the third goal of population health management. Going forward, this will be the next focus for healthcare analytics, which incorporates biomedical research with the analytics of healthcare data from hospitals and providers. The additional element to be tapped and incorporated is the enormous amount of wearable sensor data that both healthy and sick people wear to monitor their current condition (Simpao, Ahumada, Galvez, & Rehman, 2014). The Cleveland Clinic again this year highlighted the importance of healthcare analytics of sensor data by including “frictionless remote monitoring” as one of the top 10 medical innovations for 2016 (Cleveland Clinic, 2015).
Three initiatives include Smart Health and Wellbeing, Learning Health Systems, and IBM Watson Health. All three envision incorporating wearable data into healthcare analytics. The goals of each lead to personalized medicine, which is defined as specific treatments and recommendations tailored to the particular needs of each patient (Computing Research
The federal certification of EHR systems involves three timed stages; the majority of hospitals and providers have attested to Stage 1. However, the attestations for Stage 2 are lagging with only 40% of hospitals and 10% of providers attesting, highlighting the difficulty of the requirements (Miliard, 2015).
In addition, there is a large portion of data being captured outside the EHR (Ward, Marsolo, & Froehle, 2014). This includes laboratory information management systems, diagnostic imaging systems, human resource systems, and accounting systems. True application of healthcare analytics will require integrating each of these platforms into a single data warehouse (Groenfeldt, 2012). The EHR may capture the various supplies used on a patient, but the accounting system knows the cost of each …show more content…
As just described, the current application of healthcare analytics attacks the first two issues, but not necessarily the third goal of population health management. Going forward, this will be the next focus for healthcare analytics, which incorporates biomedical research with the analytics of healthcare data from hospitals and providers. The additional element to be tapped and incorporated is the enormous amount of wearable sensor data that both healthy and sick people wear to monitor their current condition (Simpao, Ahumada, Galvez, & Rehman, 2014). The Cleveland Clinic again this year highlighted the importance of healthcare analytics of sensor data by including “frictionless remote monitoring” as one of the top 10 medical innovations for 2016 (Cleveland Clinic, 2015).
Three initiatives include Smart Health and Wellbeing, Learning Health Systems, and IBM Watson Health. All three envision incorporating wearable data into healthcare analytics. The goals of each lead to personalized medicine, which is defined as specific treatments and recommendations tailored to the particular needs of each patient (Computing Research