E-Patient Connectivity and the Near Term Future
Kvedar JC, Nesbitt T, Kvedar JG, Darkins A. E-patient connectivity and the near term future. J Gen Intern Med. 2011 Nov;26 Suppl 2:636-8. doi: 10.1007/s11606-011-1763-0. PubMed PMID: 21989615.Read More...
The healthcare system is challenged by growth in demand for services that is disproportionate to the volume of service providers. New care models must be created. The revolution in communications and monitoring technologies (connected health) allows for a care model that emphasizes patient self-management and just-in-time provider interventions. Challenges to realizing this vision exist, including maturity of the technology, privacy and security and the ability of providers to customize solutions to maximize patient engagement and behavior change. In addition, provider work-flow and reimbursement must be changed to enable new care models that are focused on patient self-care and just-in-time provider interventions.
Linking Electronic Health Record-Extracted Psychosocial Data in Real-Time to Risk of Readmission for Heart Failure
Watson AJ, O'Rourke J, Jethwani K, Cami A, Stern TA, Kvedar JC, Chueh HC, Zai AH. Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure. Psychosomatics. 2011 Jul-Aug;52(4):319-27. doi: 10.1016/j.psym.2011.02.007. PubMed PMID: 21777714.Read More...
Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care.
We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days.
We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission.
We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source.
Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.
Telemonitoring in Patients with Heart Failure
Everett W, Kvedar JC, Nesbitt TS. Telemonitoring in patients with heart failure. N Engl J Med. 2011 Mar 17;364(11):1079; author reply 1079-80. doi: 10.1056/NEJMc1100395#SA3. PubMed PMID: 21410376.Read More...
To the Editor:
The study by Chaudhry and colleagues showing that telemonitoring did not improve outcomes among patients hospitalized for heart failure has several shortcomings. In other major studies, well-designed home telemonitoring programs that used more advanced forms of technology to support patient education and health care for patients with congestive heart failure have been shown to be successful in reducing unnecessary hospitalizations. These systems require daily, real-time monitoring of physiological data, direct patient feedback and coaching, and a high level of patient−clinician interaction to achieve positive results. The findings of Chaudhry et al. reflect the lack of an effective, comprehensive intervention combined with an intention-to-treat evaluation model that is best reserved for clinical trials that do not involve ongoing provider−patient interaction. In addition, patients who were not using the program at the end of the evaluation period were counted in the results. From our collective experience, the study's negative findings are due to an inadequate intervention and the design of the study itself and should not be taken as a denunciation of telemonitoring systems that enable patients to manage their chronic illnesses effectively.
Implementing a Web-Based Home Monitoring System Within an Academic Health Care Network: Barriers and Facilitators to Innovation Diffusion
Pelletier AC, Jethwani K, Bello H, Kvedar J, Grant RW. Implementing a web-based home monitoring system within an academic health care network: barriers and facilitators to innovation diffusion. J Diabetes Sci Technol. 2011 Jan 1;5(1):32-8. PubMed PMID: 21303622.Read More...
The practice of outpatient type 2 diabetes management is gradually moving from the traditional visit-based, fee-for-service model to a new, health information communication technology (ICT)-supported model that can enable non-visit-based diabetes care. To date, adoption of innovative health ICT tools for diabetes management has been slowed by numerous barriers, such as capital investment costs, lack of reliable reimbursement mechanisms, design defects that have made some systems time-consuming and inefficient to use, and the need to integrate new ICT tools into a system not primarily designed for their use. Effective implementation of innovative diabetes health ICT interventions must address local practice heterogeneity and the interaction of this heterogeneity with clinical care delivery. The Center for Connected Health at Partners Healthcare has implemented a new ICT intervention, Diabetes Connect (DC), a Web-based glucose home monitoring and clinical messaging system. Using the framework of the diffusion of innovation theory, we review the implementation and examine lessons learned as we continue to deploy DC across the health care network.