Electronic Medical Records Quotes

Nick Dawson, a leader of the Society of Participatory Medicine, uses Evernote as his electronic medical record, pulling in data from sensors and sharing with providers or family members.64

Distraction leaches the authenticity out of our communications. When we are not emotionally present, we are gliding over the surface of our interactions and we never tangle in the depths where the nuances of our skills are tested and refined. A medical professor describes the easy familiarity with which her digital-native resident students master medical electronic records—but is troubled by the fact that they enter data with their eyes focused on their digital devices, not on the patient in the room with them. Preoccupation with technology acts as a screen between the student and the patient’s real emotion, real fear, and real concern. It may also prevent these residents from noticing physical symptoms that the patient fails to mention. The easy busyness of medical record entry is a way to sidestep the more challenging dynamics of human connection. But experienced physicians know that interpersonal skills are essential to mastering the art and science of medical diagnosis.

Medical records at many hospitals had been kept in manila folders rather than electronically, so health care providers typically had little information about what medications displaced patients with life-threatening ailments like cancer or heart disease were taking,

The ultimate goal of gathering big data in electronic medical records (EMR) ¬managed by professionals, and personal health records (PHR) updated by patients is creating smart alerts in natural language. That is, the system would understand the actual meaning of words and expressions in the records, thereby making it simpler to intervene in a patient’s affairs when needed.

Marc Goodman is a cyber crime specialist with an impressive résumé. He has worked with the Los Angeles Police Department, Interpol, NATO, and the State Department. He is the chief cyber criminologist at the Cybercrime Research Institute, founder of the Future Crime Institute, and now head of the policy, law, and ethics track at SU. When breaking down this threat, Goodman sees four main categories of concern. The first issue is personal. “In many nations,” he says, “humanity is fully dependent on the Internet. Attacks against banks could destroy all records. Someone’s life savings could vanish in an instant. Hacking into hospitals could cost hundreds of lives if blood types were changed. And there are already 60,000 implantable medical devices connected to the Internet. As the integration of biology and information technology proceeds, pacemakers, cochlear implants, diabetic pumps, and so on, will all become the target of cyber attacks.” Equally alarming are threats against physical infrastructures that are now hooked up to the net and vulnerable to hackers (as was recently demonstrated with Iran’s Stuxnet incident), among them bridges, tunnels, air traffic control, and energy pipelines. We are heavily dependent on these systems, but Goodman feels that the technology being employed to manage them is no longer up to date, and the entire network is riddled with security threats. Robots are the next issue. In the not-too-distant future, these machines will be both commonplace and connected to the Internet. They will have superior strength and speed and may even be armed (as is the case with today’s military robots). But their Internet connection makes them vulnerable to attack, and very few security procedures have been implemented to prevent such incidents. Goodman’s last area of concern is that technology is constantly coming between us and reality. “We believe what the computer tells us,” says Goodman. “We read our email through computer screens; we speak to friends and family on Facebook; doctors administer medicines based upon what a computer tells them the medical lab results are; traffic tickets are issued based upon what cameras tell us a license plate says; we pay for items at stores based upon a total provided by a computer; we elect governments as a result of electronic voting systems. But the problem with all this intermediated life is that it can be spoofed. It’s really easy to falsify what is seen on our computer screens. The more we disconnect from the physical and drive toward the digital, the more we lose the ability to tell the real from the fake. Ultimately, bad actors (whether criminals, terrorists, or rogue governments) will have the ability to exploit this trust.

Emergency department physicians spent 44 percent of their time entering data into electronic medical records, clicking up to 4,000 times during a 10-hour shift. —Becker’s Health IT & CIO Review magazine, October 11, 2013

Instead of giving every patient the same medication at the same dose, what if your doctor could actually peer into your genome and choose the medication and dose that was right for you? For many years, electronic medical record systems have been able to look up your existing medications when your doctor prescribes a new one to check for possibly dangerous interactions between these and the new drug. We started to think that these systems could be co-opted to also look up your genome. If we were going to properly explore the entirety of a patient’s genome from a medical perspective, we certainly needed an approach to drug therapy. What we needed was a database of pharmacogenomic variants. Well, as luck would have it, there was Russ Altman.

intensive care units (ICUs) are physiologically fragile and unstable, generally have life-threatening conditions, and require close monitoring and rapid therapeutic interventions. They are connected to an array of equipment and monitors, and are carefully attended by the clinical staff. Staggering amounts of data are collected daily on each patient in an ICU: multi-channel waveform data sampled hundreds of times each second, vital sign time series updated each second or minute, alarms and alerts, lab results, imaging results, records of medication and fluid administration, staff notes and more.

It was also clear that although petabytes of data are captured daily during care delivery in the country’s ICUs, most of these data were not being used to generate evidence or to discover new knowledge. The challenge, therefore, was to employ existing technology to collect, archive and organize finely detailed ICU data, resulting in a research resource of enormous potential to create new clinical knowledge, new decision support tools, and new ICU technology. We proposed to develop and make public a “substantial and representative” database gathered from complex medical and surgical ICU patients.

Fiscal Numbers (the latter uniquely identifies a particular hospitalization for patients who might have been admitted multiple times), which allowed us to merge information from many different hospital sources. The data were finally organized into a comprehensive relational database. More information on database merger, in particular, how database integrity was ensured, is available at the MIMIC-II web site [1]. The database user guide is also online [2]. An additional task was to convert the patient waveform data from Philips’ proprietary format into an open-source format. With assistance from the medical equipment vendor, the waveforms, trends, and alarms were translated into WFDB, an open data format that is used for publicly available databases on the National Institutes of Health-sponsored PhysioNet web site [3]. All data that were integrated into the MIMIC-II database were de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access to MIMIC-II. Deletion of protected health information from structured data sources was straightforward (e.g., database fields that provide the patient name, date of birth, etc.). We also removed protected health information from the discharge summaries, diagnostic reports, and the approximately 700,000 free-text nursing and respiratory notes in MIMIC-II using an automated algorithm that has been shown to have superior performance in comparison to clinicians in detecting protected health information [4]. This algorithm accommodates the broad spectrum of writing styles in our data set, including personal variations in syntax, abbreviations, and spelling. We have posted the algorithm in open-source form as a general tool to be used by others for de-identification of free-text notes [5].

records: selection bias, confounding, and missing data. These are explored in greater depth in other chapters of this text. Selection bias, or the failure of the population of study to represent the generalizable population, can occur if all the patients, including controls, are already seeking medical

and Collaboration One of the greatest challenges of working with medical data is gaining knowledge of

aware of the source of error and can repeat the measurement then ignore the known incorrect outlier value when planning care. However, clinicians cannot remove the erroneous measurement from the medical record

Clinicians are often aware of the source of error and can repeat the measurement then ignore the known incorrect outlier value when planning care. However, clinicians cannot remove the erroneous measurement from the medical record