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The Future of Health Involves Human-Agent Collectives (Part 2 of 2)

Posted on February 3, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site ( and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article looked at the basic idea of devices and computer systems that can deal with loosely connected actors, human and mechanical. This part takes it further into current experiments in health care.

Devices Must Adapt to Collaborators’ Needs

To be a useful agent, a computer system must understand the context in which it is operating. Take pulse oximetry–the measurement of oxygen in the blood. It’s an easy procedure to perform, and is used in hospitals to tell whether a sick patient, such as one with lung problems, is in danger. The same technology can also be used by fitness buffs to tell whether they’re getting a good workout.

These are obviously very different goals–and the device used for pulse oximetry will also be used in different ways. In a risk monitoring situation, samples may be taken less often than during a healthy fitness workout. At the minimum, a device should be configurable so that it gives the timing and accuracy needed in a particular setting. It should also be easy to turn a device on and off if it is needed for a limited time period, such as a workout.

Diego Alonso, a researcher at MD PnP, points to analgesia (the administration of pain killers) in the hospital as an example of competing needs that must be reconciled by a supervisor, human or machine. So long as the patient is stable, the pain killer should be administered. But if a monitor notices a drop in the patient’s vital signs, the painkiller’s dose must be reduced.

A popular standard for exchanging data among devices is the Data Distribution Service (DDS). The standard is rich and complex, typical of those produced by the Object Management Group. But among its virtues are an ability to specify how often you want data from a particular device. OpenICE uses DDS, among many other systems.

In short, the frequency and accuracy of data collection should be configurable. As patterns of human behavior are better understood, devices may become even more responsive to the contexts in which they are needed.

Even before the current move to standards, Capsule Tech managed to get devices to talk to EHRs through the grueling effort of interpreting the inputs and outputs of each system and crafting protocols to make them work together.

Started in 1997, the company has recently expanded from merely sharing data to developing useful tools based on data, such as alerts and a modest amount of analytics. Some of these tools demonstrate a kind of adaptability reminiscent of a human-agent collective.

For instance, alerts are crucial in any hospital environment, but notorious for crying wolf–90% can be false. In addition to sending data to the EHR, Capsule’s SmartLinx’s Medical Device Information System sends near-real-time alarm data to its Alarm Management System. This helps hospitals manage their alarms, in line with the Joint Commission’s National Patient Safety Goals.

SmartLinx does not suppress any information, but when reporting it through the Alarm Management System to the clinician’s mobile device, includes some context to help the clinician decide whether the alert needs a response. Some context involves basics such as who, where, when, and which device was activated. Other context can consist of physiological data such as the patient’s heart rate and how long the alarm has been sounding.

Additionally, to provide actionable, timely information that aids in human decision making, Capsule has built an early warning scoring system application that uses vital sign information to calculate an immediate general health status score for patients and to identify those likely to deteriorate. The application also guides the care team through appropriate actions. This may be the beginning of an intelligent, integrated health system.

Computer Systems Must Be Sensitive to Bad Input and Failure

An unfortunate tenet of human-agent collectives is that agents can’t be trusted. The most basic example is system failure. If you don’t hear from a device, does that mean the patient is fine or that the device’s battery has run out of power? DDS offers a handshake or heartbeat, the common way for distributed computing systems to determine whether part of the system has gone bust.

Provenance is another requirement for collaborative environments. This means recording when a measurement was taken, and what person or device was responsible. There must also be ways to protect against data that arrives late or is assigned the wrong timestamp. When data is entered by humans, errors can be assumed as a matter of course, even in something as simple as spelling the name of a medication manufactured by your company.

More subtle is input from inexact devices, and worse still is the potential for malicious manipulation. I heard of instances where people who got rewards by their employers for reporting exercise put their fitness devices on their dogs. Using analytics, a health care system should be able to tell that a series of sudden 20-mile-per-hour rushes interrupted by inactivity are not a human activity.

Ethical and Technical Considerations

Lots of issues come up as simple human-computer interaction evolves into collaboration among agents. I’ve already mentioned error detection and provenance. Other issues include flexibility in computers taking or relinquishing control (agile teaming), legal responsibility, providing each agent with the right incentives, considering when to engage the user’s attention (instead of taking action behind the scenes), and offering the proper interface to do so. Connected health is a deep concept offering a lot to explore, and technologies will get better as we understand more of it.

Laying the Best Foundation for Medication Reconciliation

Posted on September 6, 2013 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

The following is a guest blog post by Brian Levy, MD, Senior Vice President and Chief Medical Officer for Health Language.
Levy Low Res
Effective medication reconciliation across the continuum of care is a critical element to eliminating medication errors and adverse drug events (ADEs). It is a focal point of such national initiatives as Meaningful Use (MU) and the Joint Commission’s National Patient Safety Goals and will also be crucial to ensuring performance metrics are met under Value-Based Purchasing and the Hospital Readmissions Reduction Program.

Simply put, one of the primary end-goals of current industry movements is to eliminate the revolving door effect in healthcare where patients are readmitted soon after discharge due to ADEs or lack of good information across the continuum. A growing body of research points to enhanced medication reconciliation as an effective way for hospitals to reduce readmission rates to meet this objective.

A 2012 study published in the Joint Commission Journal on Quality and Patient Safety found that accurate preadmission medication lists—acquired as part of medication reconciliation strategies— reduced ADEs both in the hospital and following discharge. Another paper published in the November 2012 edition of Pharmacotherapy also points to the critical role ADEs play in readmission rates and how ineffective care transitions, especially as they relate to medication management, exacerbate the situation.

The logistics of medication reconciliation has historically been an uphill battle for many clinicians. Without an electronic method for capturing information, the scene usually comes down to a Q&A session where physicians, nurses or other clinicians rely on patients to give them an accurate medication list. When a patient is unaware of the name of a medication, it usually results in a protracted delay in patient care while phone calls are made and consults conducted to accurately identify medications and avoid the potential for error.

EHRs provide the first step to correcting this inefficient way of gathering information. And while these systems are great repositories of patient information, the difficulty for medication reconciliation has been a lack of standards—specifically the lack of a standardized medical vocabulary. A number of proprietary medical terminologies exist within the industry, and without a standard for information exchange, the risk is that one drug could be identified by a number of different terminology codes depending on the proprietary system used.

Clinicians need an effective method for exchanging patient medication information between disparate systems in a standardized format that can be translated accurately by various healthcare organizations, providers and departments involved in patient care. MU is addressing this issue on one level through the introduction of RxNorm, a normalized naming system produced by the National Library of Medicine for generic and branded drugs and a tool that supports semantic interoperability between drug terminologies and pharmacy knowledge base systems.

RxNorm is a critical first step to ensuring the feasibility of building and accessing an accurate medication summary, thus minimizing the possibility of duplicate therapies, drug allergies and drug interactions. By adopting this standard, healthcare organizations and providers will begin receiving RxNorm codes in important CCD summary of care documents and HL7 messages. This standard will complement the use of the Systematized Nomenclature Of Medicine Clinical Terms (SNOMED CT®), a widely-used clinical terminology set also required under MU for the creation of problem lists.

While RxNorm provides efficient and accurate capture of medication information from external systems, healthcare organizations and providers will still require a method of converting codes from RxNorm to internal systems and visa-versa. This step ensures that internal medicine systems and drug information and interactions databases like Medi-Span, First Databank, Micromedex and Multum can also reconcile important patient medication information.

To address the full picture of data normalization, healthcare providers can leverage a healthcare terminology management solution to ensure automated mapping of patient medication data received from disparate sources to standardized terminologies. This process de-duplicates data, creating a normalized code across all clinical systems used internally, minimizing the potential for error.

This approach also provides an effective way for leveraging a comprehensive, longitudinal patient record, which is a primary goal of the health IT movement to enhance patient care. A foundation of standardized codes enables healthcare organizations to more fully develop advanced clinical decision support functions, where alerts can be received immediately for clinical activity impacting individual patients or within populations of patients.

As the healthcare’s industry move toward higher-quality care and more efficient care delivery continues to mature, the use of standardized medical terminologies will be paramount to effective clinical information exchange. While some initiatives like RxNorm and SNOMED CT are addressing this need for standardization, healthcare organizations can further advance data normalization strategies by leveraging the efficiencies and advantages of healthcare terminology management solutions.

New Patient Safety Standards Proposed For EHR Certification

Posted on July 11, 2011 I Written By

Katherine Rourke is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Here’s a proposal that could make Meaningful Use standards and vendor certification programs more valuable. Authors writing for the Journal of the American Medical Association have suggested that the Joint Commission’s National Patient Safety Goals for 2011 be included in EHR certification and MU criteria.

Here’s how the JAMA authors suggest linking EHR standards with the NPSG list:

* Patient identification:  EHRs can and should make patient identification more reliable, in part by including patient photos. EHRs should also require caregivers to re-enter patient initials if patients seem to have similar names, the comment suggests.

* Physician notification:  EHRs should not only ping physicians when a patient has abnormal test results, but also require doctors to respond by a given deadline, according to the article.

* Improving medication safety:  As long as they don’t warp clinical workflow and create additional risk of error, EHRs should support bar code med administration and clinical decision support, the JAMA authors say.

* Infection control:  EHRs should track patients with dangerous infections, and also offer checklists which can improve clinicians’ compliance with IC protocols, according to the proposal.

* Medication reconciliation:  One of the most obvious ways the NPSGs, Meaningful Use and EHRs can work together is to support appropriate med reconciliation, particularly by improving interoperability between med lists across organizations and varied EHRs, the writers suggest.

* Suicide risk:  Here’s an intriguing idea. The authors argue that EHRs should include a checklist to assess risk for patient self-harm, as well as notifying clinicians for patients who should be screened for depression.

As an analyst, rather than clinician, I don’t have any direct comments on the list of safety proposals. But I must say that from my perspective, this approach seems smart, practical and even better, focused.  Adding specific patient safety goals to EHR standards — rather than debating over broad safety issues — looks like a great idea.  Am I missing something here, or do you share my enthusiasm?