Drawing from within: Implantable devices yield new types of patient generated data [Video]

Project HealthDesign National Program Office

We are in the final six-month stretch of Project HealthDesign, and we’re trying to share some of our project’s most relevant and dynamic findings with respect to patient-generated data. We learned through our recent grantees’ research that familiar consumer electronic devices, like cell phones and tablets, can help patients capture observations of daily living and personal insights, and allow them to easily share these data with their clinicians. 

There’s another type of patient-generated data out there. We’ve learned from conversations with people whose health lives depend on implantable devices like cardiac defibrillators and insulin pumps, that data derived from these devices  provide important information not only about how the device is performing but also about the patient’s health state.  It is timely to figure out how to make data acquired through such implantable devices accessible and useful to patients as well as clinicians and researchers. 

In this video, Patricia Flatley Brennan offers some compelling thoughts about the need to imagine and harness this novel patient-device data to make sure it serves the ultimate health interest of patients.  Device makers and federal bodies who regulate these devices should pay particular attention. While watching this video, keep these questions in mind:  How can we ensure that patient-device data are 1) understandable to and accessible by both patients and clinicians; 2) completely secure from disruption and tampering, and 3) regulated carefully and fittingly? 

Project HealthDesign wants your input on this important matter. If you have thoughts, suggestions, ideas about all types of patient-generated data, post your comments below or send them to [email protected]

 

ONC Webinar on Automating Blue Button Data Download

Libby Dowdall, Communications Coordinator, Project HealthDesign National Program Office

Don’t miss ONC's upcoming webinar about developing standards and specifications for automating Blue Button data download. The webinar will be held Aug. 15 from 4-5 p.m. EST. Register for the webinar.

The Blue Button idea began when the Department of Veterans Affairs (VA) added a blue button to their patient portal (My HealtheVet) to allow users to download their health data. Since then, the idea has expanded and many health care provider groups and other organizations have signed on to allow their own patients to download health data via the Blue Button. This webinar is a first step toward automating the Blue Button  by developing standards and specifications that allow would allow users to not only download their health information, but also to automate the sending of that data from their health care providers to their personal health records (PHRs), email accounts, or other data repositories.

This event is a response to feedback from ONC’s recent Patient Access Summit, which was held in June. Learn more.

The Challenges of Liberating and Aggregating Data for Optimal Patient-Provider Collaboration and Outcomes

Dan Bernstein, M.Arch., Chronology.MD Technical Partner, Medic8 Manager

Before a team of clinicians can develop and deploy a good treatment plan, they need the best available information in order to correctly and quickly diagnose a patient’s condition and status. 

In the past, many people saw the same doctor for most of their lives.  At the time, paper records were the best option we had; that’s not the case anymore.  Now, patients move and travel, see multitudes of health care professionals throughout their lives, and use several pharmacies and labs. Today, although electronic health records can make patient health data portable, information liquidity is not happening.

Almost every use case in health care should have the patient at its center. But most of the diagrams I’ve seen that illustrate how information flows in health care show patients as a byproduct, as an afterthought, or don’t even document their existence.

Today’s EHRs are not leveraging the power of intelligent data management. In fact, they are used in much the same way as paper records have been used traditionally.

This reminds me of my time as an architect. Even though most firms had begun using what were then considered state-of-the-art CAD systems, I soon realized that moving to digitized systems hadn’t magically allowed them to work smarter (e.g., they hadn’t created symbol libraries of common building elements or building systems broken out into individual files for re-use and global updates). That was 20 years ago. Ad while architecture firms and CAD systems have evolved, health care and EHR systems seem to have been dragging their feet.

Organizations need to jump feet first into the patient-centered digital realm. Today, supporting both paper and digital systems is inefficient. 

It’s a travesty that in a modern society patient health records are stashed away and only made available to the patient or a physician at another hospital through a process that hasn’t changed since the early part of the last century.  Here is what I experienced in my recent interaction with University of Chicago Hospital when trying to get my own health records:

• The hospital mails me blank forms to document my approval to release my records. These forms are not available to be faxed or emailed or on their website for download.
• There are charges for me to get a copy of my records.
• Even though the University of Chicago Hospital uses an Epic EHR system, I can’t access my own information via an online patient portal.
• I have records at three different hospitals that all use Epic EHR systems, but it's not easy to share my health information between the systems.

It’s a backward system that requires a patient to jump through hoops to request information from his providers, hospitals, labs and pharmacies. Any organization that impedes the appropriate transfer or sharing of patient health information should be penalized.

If the best available information is to become available to both patients and providers at the point and time of care, then it’s going to require lots of changes and all of our support.

Learn more about the Crohnology.MD project.
Read more posts from the Crohnology.MD team.

Evaluation Infrastructure Headaches

Matthew Lee, dwellSense Lead Researcher, Ph.D. Student in Human-Computer Interaction, Carnegie Mellon University

Long-term deployments of technology give researchers an opportunity to understand how technology can be embedded and incorporated into people's lives, particularly since observations of daily living (ODLs) naturally occur in people's everyday lives. However, long-term deployments are not without their hazards. One of these hazards is that the longer the study, the more likely it is that things (both good and bad) will happen to affect the data collection and even the integrity of the ODL data. I'd like to detail a couple of issues that recently complicated our deployment: Wi-Fi coverage and residents moving apartments.

In our evaluation of the dwellSense system, we have deployed a suite of sensors in 12 apartments in a senior high-rise apartment building. For the last seven months, these sensors have captured information about participants’ daily activities such as medication taking, phone use, and meal preparation. To provide us with the connectivity we needed to collect the data from our sensors, we set up a Wi-Fi network that covered the apartments in our study. Initially, this was a challenge because 1) participants lived in different parts of the buildings and 2) the building’s dated architecture  and thick walls reduced Wi-Fi coverage. During the initial deployment, we solved this coverage issue by installing wireless routers that piggy-backed on existing broadband service in two of our participants’ homes. This was enough to cover three floors of the building where many of the study participants lived. All was well for about seven months.

At the beginning of the eighth (and final) month of the evaluation study, we learned that the 90-year-old, historic building was finally going to be renovated. Of our 12 study participants, three had to move apartments. Fortunately for them, they each moved to a beautiful corner apartment with a view of city. Unfortunately for us, the Wi-Fi network and router we had installed in one of their apartments no longer reached any of the other residents. 

The first solution we tried was to install a Wi-Fi repeater to increase the range of our network. This attempt didn't work because the repeater would not connect to the router we set up. The repeater connected well with other routers, but not with our router. This was frustrating. After many hours rebooting and configuring the repeater and router, they just would not get along. Even if we had gotten them to connect, the repeated Wi-Fi signal was weak at best when extended two floors down where we would need it. 

The second solution we tried was to reinstate a Wi-Fi network in the location where it had been before the move. Fortunately, one of our study residents lived in the center of where we needed Wi-Fi coverage. We asked him if we could pay the additional cost to add broadband Internet service to his already extensive (and expensive) cable television service. He agreed, so we ordered the new service for his account, picked up the equipment from Comcast, and installed the cable modem and a new router in his home. All was well—the dwellSense system in his apartment was once again connected to Wi-Fi and uploading data.  However, even though the router was just a few feet away from the original position, the Wi-Fi coverage  did not reach all the study participants that it had once covered. After countless visits to apartments to find that the computers having trouble connecting to a Wi-Fi signal that teetered between "weak" and "no signal," we realized that we needed to boost the signal with the Wi-Fi repeater. Finally, after installing two additional Wi-Fi repeaters, we were able to get everyone back on Wi-Fi for the final month of the study. This took nearly a week and a half of trial and error with debugging.

This Wi-Fi-induced nightmare should be lesson that evaluating systems in the field can be difficult, particularly if the evaluation lasts for a long time. The difficulties are twofold: 1) the unpredictability of people's lives (e.g., people had to move apartments) and 2) debugging IT infrastructure that is mostly invisible (e.g., it's hard to know how far Wi-Fi signals will reach except by trial and error). However, when understanding how technology fits into people’s lives, long-term evaluations and field deployment are still the best way to get real, valid data.

Learn more about the dwellSense project.
Read more posts from the dwellSense team.

The Importance of Holistic Care and the Mother-Baby Dyad

Gillian Hayes, Ph.D., Estrellita Principal Investigator, University of California, Irvine

Beyond the scientific merit, this project has been a learning experience for me, first as a pregnant woman and now as a new mother. I previously wrote about the challenges of recruiting in the NICU when my unborn child was “older” gestationally than the infants we were attempting to enroll in our study. My baby is now 15 weeks old and healthy as can be. We had an easy delivery with only 30 minutes of pushing and were then moved to the Mother-Baby unit; our hospital makes a point of caring for mother and baby together in one room with a shared nurse. Unfortunately, soon after after my son’s birth, I experienced serious complications that led to a blood transfusion and eventual re-admittance to the hospital. As a result, we were split apart as a mother-baby unit and we were treated as individuals when he was only days old.

It was at this point that the importance of treating a mother and her newborn as a unit really hit home for me. If I was sick, he couldn’t eat properly. If he was sick, I worried and it exacerbated my already delicate condition. Every medication I took showed up in his system several hours later.

My very sleep-deprived husband proved his worth as a father in those days. With our families far away, he first had to care for our son by himself while I was hospitalized and then had to care for both of us when I returned home. He used a low-tech (pen and paper) and elaborate (many columns, careful tracking) system for monitoring the intake of my pain and cardiac medications, blood pressure readings, and sleep as well as the baby’s eating and diapering. We eventually transferred these data into Excel and began to search for patterns.

Now that I am healthy, we continue to look for patterns – only now, my husband and I are using our team’s mobile app to record data. Estrellita enables this kind of pattern-searching by letting me examine the baby’s fussiness and my mood (common ODLs for all participants) alongside tables of the foods I eat and naps he takes. It also lets me check my mood and whether I am showing any signs of depression.

The thing I love best about the phone app? I can enter data at night in the dark without having to get out of bed, walk into the bathroom where there is light, and document something on paper (which is what we were doing before we started using Estrellita).

There is, of course, always room for improvement. Future generations of Estrellita should allow for more elaborate visualizations to compare data amongst different ODLs, much like the Chronology.MD concept of stories being told through the data. And, even better would be if more of these data could be collected automatically like in the dwellSense project, because I have also experienced how hard it is to have the time, energy, and brain power to track anything. There is still much work to be done!

Learn more about the Estrellita (formerly FitBaby) project.
Read more posts from the Estrellita (formerly FitBaby) team.

Creative Commons photo by MightyBoyBrian.

BreathEasy by the Numbers

Barbara L. Massoudi, M.P.H., Ph.D., BreathEasy Principal Investigator, RTI International

We're excited to share a quick update now that we're just about at the halfway point in the intervention:

• We've recruited 30 patients who have been actively submitting data through our mobile app for about three months now.


• The patient-generated data is viewed by two types of clinicians participating in the study: family physicians (n=13) and nurses (n=7).


• The nurses review the data at least once a week, but some review more frequently. When they see something that concerns them, the nurses flag the data for additional review by the patient’s physician.


• Twenty-two patients have been in for clinical visits since the intervention phase began, but not necessarily to be seen for their asthma.


• Two patients have received new diagnoses as a result of the intervention.


• Sadly, two of the mobile phones have been stolen to date; one was retrieved and the other is being replaced. We've worked through the appropriate safeguards and reporting processes related to these losses.

Check back next month to learn about the many technical challenges we’ve had that relate to the phones.

Learn more about BreathEasy.
Read more posts from the BreathEasy team.

Project HealthDesign Director Shares Views on Patient Engagement in InformationWeek

Libby Dowdall, Communications Coordinator, Project HealthDesign National Program Office

Head over to InformationWeek today to read “Is 2012 the year of online patients?,” which features insights from Project HealthDesign Director Patricia Flatley Brennan, Ph.D., R.N. In the article, Marianne Kolbasuk McGee explores what 2012 might hold for patient engagement in health care. She also interviewed Missy Krasner, a former Google Health executive. Read an excerpt below, or visit InformationWeek to read more.

“… doctors will have to convince patients to get more involved in their own healthcare--including using their own data to keep better tabs on their illnesses. In fact, payments tied to better patient outcomes--helped by smarter use of patient data-- is a big driver for more healthcare providers to participate in patient engagement initiatives, Brennan said.

Yet, with so much on doctors' plates as it is—"most doctors don't want a tsunami of new data" added to EHR and other systems from their patients' home health monitoring or wellness devices, said Brennan. That's especially true since most of this data doesn't warrant emergency care.

However, when there is an issue that requires immediate attention, Project HealthDesign teams have found that patients' monitoring data can be successfully sent to or shared with other clinicians, such as triage nurses or case managers, who can often help facilitate earlier intervention, or give feedback to patients without overwhelming doctors.”

dwellSense Visualizations

Anind Dey, Ph.D., dwellSense Principal Investigator, Carnegie Mellon University

During an earlier phase of our research, we identified the prospective information needs of our various stakeholders: elders, caregivers, occupational therapists and primary care physicians. In particular, our elders were quite interested in real-time or daily information about their performance of their everyday activities, but didn’t find a lot of value in long-term data or trends. They weren’t opposed to the long-term data, but they really wanted to have the daily information in order to see if they had taken their medication, and to see how well they were doing each day.

We designed the visualization below to run as an application on a Samsung Galaxy 10.1” tablet. The visualization has three horizontal panels, one for each of the ODLs that our sensing systems are tracking: medication taking, making phone calls and coffee-making. The large type size, the colors used and the simple design were purposefully chosen to make it easy for the elders to consume the information.

For medication tracking, we present information about morning and evening pills. In the visualization below, you can see that the subject took their morning medicine on time at 5:07 a.m., but had difficulty determining the right day of the week, having opened the Sunday and Monday doors before correctly opening the Tuesday door. The subject missed the evening pill completely. The two bar charts on the right indicate how timely the subject was in taking his or her medicine and how well the subject performed the task. Because the subject missed his or her evening medicine, the timeliness bar chart is red . The other chart is yellow to indicate his or her difficulty in selecting the correct pillbox door. These bar charts are intended to serve as quick additional abstractions to make it easy to consume the medicine-taking information.

For the phone calls, the visualization indicates the number of calls made and, of those, the number of times the user . The bar chart provides a summary of how well the user performed this task.

For coffee-making, the visualization indicates whether the subject made coffee correctly. In particular, it shows whether the subject performed each of four key coffee-making tasks correctly: getting water, adding the coffee, placing the carafe in the coffee maker and starting the machine. In this case, the subject performed all four tasks correctly, which is what the associated bar chart indicates.

 

We are still going to provide a long-term visualization to some of our subjects to see what value it can provide, despite their seeming lack of interest. This will be a more traditional line graph, with time on the horizontal axis, and the quality of the performance of the task on the vertical axis. Subjects will be able to click on any of the data points to receive the regular daily view for the chosen day. We expect that by providing both kinds of views, we can improve subjects’ awareness of their own performance, and that this will cause them to engage others in discussions (e.g., caregivers, occupational therapists, doctors) about their abilities, with the goal of improving their performance over time.

Learn more about the dwellSense project.
Read more posts from the dwellSense team.

 

Patient Access to Lab Test Reports and the Era of Data-driven Health Care

Patricia Flatley Brennan, Ph.D., R.N., Project HealthDesign National Program Director

As director of Project HealthDesign, a national program of the Robert Wood Johnson Foundation that aims to explore how personal health records (PHRs) and patient-sourced data might improve health and health care, I applaud this proposed rule. Allowing patients direct access to lab test reports is an appropriate and much-needed step toward equipping them to access, understand and act on their own health data. My work with Project HealthDesign has shown me that individuals are ready for an era of data-driven health care. Patients are hungry not only for access to lab test reports and clinical visit data, but also for access to more and different kinds of health data to use in conversations with their clinicians. We’re excited to see how patients, when equipped with their own health data, might share richer insights about their everyday health with clinicians and take charge of their health between clinical visits. That’s why we see this proposed rule as a big step for patients.

However, I also recognize that changing the flow of information is going to change the clinical conversation. Everyone needs to be involved in order to ensure that this process remains efficient and effective. That’s why I urge you to concurrently develop patient education tools that will allow patients to understand and minimally interpret lab test report data. At the same time, we must also work to prepare clinicians to participate in this new era of data-driven health care.

This comment was submitted to HHS in response to a proposed rule that would allow patients direct access to their lab test reports. To read the proposed rule, visit regulations.gov.

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Learn more about the proposed rule.
Read more health policy comments from Project HealthDesign.

Interpreting the Absence of Data

Gillian Hayes, Ph.D., Estrellita Principal Investigator, University of California, Irvine

Anyone doing research about records of any kind knows the challenges of handling missing data. We often hear about epidemiologists who struggle to find patterns in incomplete data sets, clinicians who carefully interview patients and family members to fill in the gaps in a medical record, and so on. This problem becomes particularly acute, however, when you are monitoring the data in real time. In some cases, missing data can mean we need to act in some way, but in other cases, it’s nothing to worry about. Two examples tell this story pretty well: developmental activities and appointments.

The Estrellita application asks parents to track the activities they do to bond with their babies, help them develop, and so on. When parents report doing these activities, we give them encouraging messages like “Great job!” and “Your baby loves it when you sing to him.” They also earn badges on their phones for each activity. In this case, because recording the data is actually part of an intervention to encourage these activities, lack of data requires additional intervention. So, if we get no data for a couple of days, then the application reminds parents to do some of the activities and to record them.

On the other hand, sometimes a lack of data doesn’t indicate a problem at all. The Estrellita application asks parents about their experiences with each clinical appointment a few hours after scheduled appointment times. If they don’t answer in that window, we dismiss the question, because self-report gets more unreliable the longer we wait. One of the mothers in our study wanted to be able to keep all her appointment information together, so she entered old appointments from before the study started. Because the appointments were so long ago, they didn’t trigger the additional questions about how they went. So, when one of our case managers logged in to check on this parent, it looked like she had missed a lot of appointments. The case manager was moments away from intervening when she realized that these appointments all predated the study enrollment date. If that baby had really missed that many appointments, there could have been something wrong.  In this case, however, the missing data were misleading. Luckily, we were able to make a fix for this situation quickly, and in the case of appointment data, we now have three categories: attended, did not attend, and no information.

Getting complete medical records in a setting in which people are trained to create and manage them (like hospitals) is difficult and complex. Doing the same in homes, schools, and other non-clinical settings can be downright impossible. Through our work, we hope to learn a little more about what to do when we have imperfect or missing data, and we are interested to see how the other projects handle this challenge as well.

Learn more about the Estrellita (formerly FitBaby) project.
Read more posts from the Estrellita (formerly FitBaby) team.