PatientsChartingCourse

nomena predict and are mirrored by changes in healthcare delivery—such as translational and clinical research that emerges as a result of either new classes of data with relevance for process control and/or scientific discovery or the new presentation of data that existed previously but were not available at the time and location of decision making. Three real-world examples serve to illustrate this premise.

The first is an instance of the effect of real-time presentation of data already captured in an inpatient environment, but not previously available to support quality control of a complex, team-based healthcare operation. Effective outcomes for many diseases require a multistep process, and the completion of any single step is necessary but not sufficient to achieve the desired outcome. For example, it is recommended that patients on ventilator support in intensive care units undergo a set of preventive measures to reduce risk of ventilator-associated pneumonia (VAP) (Coffin et al., 2008). These measures include prophylaxis against deep venous thrombosis, measures to reduce stress ulcers, sedation “vacations” to assess readiness to extubate, elevation of the head of the bed, mouth care, and hypopharyngeal suctioning. At Vanderbilt University Medical Center, electronic clinical decision support systems in use since 1994 provide alerts and reminders to clinicians about recommended best practices (Starmer et al., 2000). Each alert is triggered by an event monitor, a computer program that continuously reviews available data on an individual patient. The program generates a system message if a specific set of findings, such as a new laboratory value (e.g., falling serum potassium in the setting of digoxin administration) appears in the clinical data system.

Computerized alerts were developed for the care measures needed to prevent VAP, and although compliance was high for most individual measures, the end-to-end consistency of all measures being administered for all patients was below 30 percent. A new set of displays of existing data, called “dashboards,” was created, showing all members of the care team which measures were due, done, and overdue (using simple green-yellow- red color coding, as shown in Figure 5-1). Also instituted was a set of web-based, real-time management reports of consistency of care showing “improvement opportunities” across all patients by time and location (see Figure 5-2). These changes were associated with a dramatic and sustained improvement in compliance with all measures and a reduction in the annual incidence of VAP per 1,000 patient days from 15.2 to 9.3 (Zaydfudim et al., 2009). Information technology alone was not responsible for this sustained improvement—which also required people and process—but it was a key enabling element.

A second example of new data sources catalyzing change is in the realm of personalized medicine—care tailored to known individual variability (particularly genetic variability) rather than being based on statistical

(100,000+ users)

FIGURE 5-3  Patient portal home page inviting participation in a research study regarding medication effects. The invitation is keyed on pharmacy records showing a recent new drug prescription.

of data they handle—real-time process measures in an intensive care unit, DNA variation associated with conditions recorded in EHRs, and patientobserved serendipitous drug effects—each conveys the power of new types of data to inform both research and care.

WEB 2.0 AND PATIENT ENGAGEMENT

Kamal Jethwani, M.D., M.P.H., and Joseph C. Kvedar, M.D. Harvard Medical School and Partners HealthCare

“Connected health” is a term used to describe a model for healthcare delivery that uses technology to provide health care remotely. Technology is used to deliver patient care outside the hospital or doctor’s office, thus em-

in Boston, follows these principles to deliver care for a range of high-risk patients with chronic conditions. Participants in its programs are patients and providers in Partners’ integrated delivery network throughout eastern Massachusetts. The Center has reported high rates of acceptance of its programs by patients and significant clinical improvement with a variety of patient groups, such as heart failure patients, hypertensive patients, and diabetics (Center for Connected Health).

Some forward-thinking employers in the area are also looking at such programs to help employees better manage their health. A recent example is EMC, a large data storage company that used SmartBeat, a hypertension self-management program developed by the Center for Connected Health. Such employee-based programs use characteristics that are unique to the work environment, such as competition, teamwork, and reward schemes, to motivate employees to adopt healthier behaviors.

Center for Connected Health: Overview

The Center currently offers three active programs for patients across the Partners network of hospitals: the Connected Cardiac Care Program (CCCP) for patients with chronic heart failure, Diabetes Connect for patients with type 2 diabetes mellitus, and Hypertension Connect for patients with hypertension (Table 5-1). The programs are designed to maximize opportunities for patient self-management of chronic diseases. The traditional model of care at the doctor’s office is overly episodic and minimizes the opportunity for patients to be active participants in their own care. Connected health programs change this model, bringing care directly to patients while their care provider coaches them through the process of care. The programs follow a similar structure, based on the following four cornerstones.

Accurate Physiologic and Behavioral Data

The connected health programs harness physiologic and behavioral data using technologies that obtain these data objectively, such as wireless scales and blood pressure cuffs, smart glucometers, and pulse oximeters. These objectively derived data are often more accurate and reliable than hand-entered data and are important in gaining both patient and provider engagement.

Data-Specific Feedback

Using the data transmitted to the Center’s servers, various levels of feedback are provided to users. This feedback ranges from a graphical

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TABLE 5-1  Connected Health Programs Designed to Support Patient Self-Management of Chronic Disease

display of users’ data points over time, to assessment of their progress, to automatic alerts when their readings are out of a predetermined range.

Coaching

Data generated by users serve as the basis for context-specific feedback in the form of coaching. Coaching can be of two types: automatic and provider driven. Most connected health programs currently rely on coaching delivered by nurses who are working at the primary care level. These educators contact patients on a regular basis, provide feedback on their progress, and guide them to help improve control of their condition. The Center also has successfully demonstrated the use of automatic coaching by a “virtual coach.”

Interface with Physicians

The connected health approach places special emphasis on the presentation of data to clinicians. The main design principle is finding meaningful trends and exceptions and presenting them to clinicians in a concise and actionable format.

Connected Health Patients

Connected health programs use primarily the Internet to collect data, engage patients, and provide feedback on their success. Despite recent survey data from the Pew Internet Project indicating that only 62 percent of adults living with at least one chronic illness have access to the Internet, compared with 81 percent of adults with no chronic diseases (Pew Internet and American Life Project, 2010), most programs at the Center have seen almost 100 percent acceptance by users. The current challenge for connected health is to create programs that employ advanced technology in a way that is simple to use and even simpler to understand and access— hence the Center’s use of technology that connects to the Internet through simple telephone lines or Internet modems. Evidence also indicates that almost 70 percent of people with two or more chronic illnesses use mobile phones (as opposed to the 52 percent that use the Internet), underscoring the importance of mobile phones in reaching this population.

Mobile phones have proven to be an inexpensive, effective, and culturally acceptable means of keeping patients engaged with connected health programs. Mobile text messaging has been used in various programs as a tool to improve engagement. One such example is a sunscreen adherence study conducted by the Center, which demonstrated that participants who received daily text message reminders had significantly higher adherence rates (56.1 percent versus 30.0 percent, p <0.001) (Armstrong et al., 2009).

User Engagement

One commonly raised concern with connected health programs is whether patients will really use them. All programs of the Center for Connected Health are currently practice based; hence the decision regarding how active patients should be is made by the practice based on how sick the patients are, what their perceived comfort with technology is, and how far away from their goals they are.

An important observation in the Center’s ongoing program evaluation has been that patients who are not active in a program within the first 30 days of starting on the program almost never become active again. Patients who are active in the first month but not in the second likewise never become active again. Conversely, patients who remain active for the first 2 months have a 90 percent likelihood of remaining above the activity threshold throughout the program.

Another important determinant of success has been practice engagement, defined according to the number of times practitioners from a practice log on to the web portal to check their patients’ activity. Preliminary

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data show that practices whose providers log on more frequently have more patients who are active and have greater clinical success.

Connected Cardiac Care Program

The CCCP is currently in its third year of operation, making it possible to study the program’s value in managing heart failure patients. Of about 3,000 eligible discharges from Partners each year, about 1,000 are enrolled in intensive monitoring using the CCCP. The program employs a weight scale, blood pressure cuff, and pulse oximeter as sensors, which upload data through a hub device given to the patient on enrollment. Three to four nurses oversee readings for 250 patients on any given day and reach out to the patients for software-flagged exceptions. Thus care can be focused on the patient, with data being uploaded every day and continuous telemonitoring­ being performed by the nurses. The involvement of the cardiologist is thus tailored to provide specialized care for those who need it, exactly when it is needed. Finally, the doctor is able to collaborate with patients on how their data relate to their clinical condition and affect their clinical outcomes and quality of life. As a result of this program, readmission rates have been reduced by almost half (from 0.92 annualized ­readmissions per person per year to 0.48) among program enrollees. Patients have reported higher awareness of how better to manage and control their clinical parameters and a greater sense of control over their disease condition.

Diabetes Connect: An Illustration of Lessons Learned

Diabetes Connect was started in February 2009 with two practices in the primary care network at Partners HealthCare that vary significantly in administration, activity, and success in the program. At the time of this analysis, the program had enrolled 75 patients divided almost equally between the two practices who were similar in age, gender, and pre-enrollment blood glucose level.

Practice 1 has a diabetes management center with dedicated staff (two registered nurses, certified diabetes educators, and a nutritionist) that supported the team of physicians. Practice 2 receives diabetes patient referrals from an endocrinologist and primary care providers in the community and is managed by one registered nurse. In the first 10 months of the program, practice 1 providers consistently logged on more often to the website (59 times/month versus 8 times/month by practice 2, p <0.001).

The overall drop in HbA1c, the main clinical outcome measure, varied by practice (–1.8 points for practice 1 and –0.9 points for practice 2, p <0.05). One explanation for this difference is how engaged the providers in charge of the application were as measured by the number of times they