Research in Gerontological Nursing

State of the Science Commentary 

The Use of Health Information Technology to Improve Care and Outcomes for Older Adults

Kathryn H. Bowles, PhD, FAAN, FACMI; Patricia Dykes, PhD, FAAN, FACMI; George Demiris, PhD, FACMI

Abstract

Using health information technology (HIT) to improve care and outcomes for older adults is a growing program of research, propelled by recent transformative policies such as the Health Information Technology for Economic and Clinical Health (HITECH) Act (Blumenthal, 2010; Institute of Medicine [IOM], 2011) and the IOM report (2010) titled The Future of Nursing: Leading Change, Advancing Health. Both documents call for the implementation of electronic health records (EHRs) and HIT solutions to improve the safety, quality, and efficiency of care. Several nurse scientists are at the forefront of advancing this work, particularly using EHRs, decision support, and telehealth. The current commentary highlights examples of recent research (i.e., from 2010 to 2014) led by nurse scientists using HIT to improve patient safety and the quality and efficiency of patient care. Future opportunities for gerontological nurse scientists interested in blending the care of older adults and HIT will be discussed, and strategies to increase health care providers’ and nurses’ capacity to engage in such innovative research will be suggested.

Abstract

Using health information technology (HIT) to improve care and outcomes for older adults is a growing program of research, propelled by recent transformative policies such as the Health Information Technology for Economic and Clinical Health (HITECH) Act (Blumenthal, 2010; Institute of Medicine [IOM], 2011) and the IOM report (2010) titled The Future of Nursing: Leading Change, Advancing Health. Both documents call for the implementation of electronic health records (EHRs) and HIT solutions to improve the safety, quality, and efficiency of care. Several nurse scientists are at the forefront of advancing this work, particularly using EHRs, decision support, and telehealth. The current commentary highlights examples of recent research (i.e., from 2010 to 2014) led by nurse scientists using HIT to improve patient safety and the quality and efficiency of patient care. Future opportunities for gerontological nurse scientists interested in blending the care of older adults and HIT will be discussed, and strategies to increase health care providers’ and nurses’ capacity to engage in such innovative research will be suggested.

Using health information technology (HIT) to improve care and outcomes for older adults is a growing program of research, propelled by recent transformative policies such as the Health Information Technology for Economic and Clinical Health (HITECH) Act (Blumenthal, 2010; Institute of Medicine [IOM], 2011) and the IOM report (2010) titled The Future of Nursing: Leading Change, Advancing Health. Both documents call for the implementation of electronic health records (EHRs) and HIT solutions to improve the safety, quality, and efficiency of care. Several nurse scientists are at the forefront of advancing this work, particularly using EHRs, decision support, and telehealth. The current commentary highlights examples of recent research (i.e., from 2010 to 2014) led by nurse scientists using HIT to improve patient safety and the quality and efficiency of patient care. Future opportunities for gerontological nurse scientists interested in blending the care of older adults and HIT will be discussed, and strategies to increase health care providers’ and nurses’ capacity to engage in such innovative research will be suggested.

Using The EHR to Improve Outcomes for Older Adults

Recent incentives provided by the HITECH Act have resulted in rapid growth in the development and implementation of the EHR. Nurse-led studies are beginning to demonstrate that effective use of the EHR can improve outcomes of relevance, such as reductions in pressure ulcers and falls, among older adults. Dowding, Turley, and Garrido (2012) evaluated the impact of an integrated EHR in 29 Kaiser Permanente hospitals on process and outcome indicators for patient falls and hospital-acquired pressure ulcers. They found that the EHR system was associated with improved documentation of both fall and pressure ulcer risk assessments and statistically significant improvements for pressure ulcer risk assessment documentation. They also demonstrated that improved documentation using the EHR was associated with a 13% decrease in hospital-acquired pressure ulcer rates. Patient fall rates remained unchanged after EHR implementation. The authors reported variation in these outcomes across hospitals and care regions. They noted that in addition to EHR implementation, organizational factors, such as collaboration, teamwork, and supportive leadership, are needed to achieve sustained improvements in quality and safety outcomes. These findings highlight a role for gerontological nurses, as they can promote improvements in nursing-sensitive measures, such as patient falls and hospital-acquired pressure ulcer rates, by modeling adoption and use of the EHR and leading quality improvement efforts that engage both senior leadership and frontline nursing staff (McFadden, Stock, & Gowen, 2014; Rosen et al., 2010). Leading geriatric care improvement programs within a health care organization, such as Nurses Improving Care for Healthsystem Elders, is an example of how gerontological nurses can partner with nursing leadership and frontline staff to improve the care of older adults. This type of program, coupled with an integrated EHR that captures data in a structured, coded format and provides clinical decision support, can ensure that all older adults receive evidence-based, personalized care and that nursing documentation is reused to build evidence for future practice.

Gerontological nurse experts can efficiently influence important outcomes and standardize the way health care professionals assess and treat older adults by providing input on which evidence-based assessment and decision-support tools are embedded in the EHR. For example, in a study in long-term care, the number of malnourished residents significantly decreased after embedding evidence-based assessment tools into the EHR that prompted nutritional and pressure ulcer risk assessments and documentation (Fossum, Alexander, Ehnfors, & Ehrenberg, 2011). Using such tools prompts caregivers to assess these important parameters, and, over time, the data generated during standardized assessments and documentation will enable research and knowledge generation using large datasets across settings and time. The IOM called for a learning health system where health care professionals use EHR data to apply what is known about a patient to generate or apply knowledge resulting in evidence-based, personalized care in the form of decision support (Friedman, Wong, & Blumenthal, 2010). An integrated EHR with structured, coded data capture provides the data infrastructure for the learning health care system that will transform the way gerontological nurses generate and apply knowledge. Data recorded at the individual patient level during an encounter can be used to personalize care for that patient and can be simultaneously applied to spur discovery and innovation for future care delivery for older adults (Greene et al., 2009). Gerontological nurses play an important role in guiding the development of the learning health system.

Providing Decision Support Interventions

Using the EHR as a tool to achieve a learning health system affords the opportunity to build decision support within the workflow of nurses caring for older adults. Decision support can take the form of alerts, reminders, or algorithms that guide evidence-based care. Bowles, Hanlon, Holland, Potashnik, and Topaz (2014) implemented the expert discharge decision support system (D2S2) within the hospital nursing admission assessment to identify older adults in need of post-acute care, such as skilled home care or skilled nursing facility care. Based on how patients answer a series of questions, an algorithm generates a daily report sent to discharge planners alerting them of patients at risk for poor discharge outcomes and therefore in need of a post-acute referral. Use of the D2S2 achieved a 26% relative reduction in both 30- and 60-day readmissions (Bowles et al., 2014). Study findings suggest that using decision support to identify at-risk patients early and arranging appropriate follow-up care is associated with improved post-acute care outcomes.

Symptom management during cancer treatment is another complex care challenge for many older adults and their caregivers. A nurse-led team created a computable algorithm that adapts research evidence for use in a clinical decision support system, providing individualized symptom management recommendations to clinicians at the point of care (Cooley et al., 2013). This complex challenge required mixed methods that involved two large clinical sites, multiple panels of experts, a seven-step process, and 2 years to complete. These rigorously developed algorithms are available for testing.

HIT can also provide decision support for sensitive topics, such as advanced care planning. Hickman, Lipson, Pinto, and Pignatiello (2014) created a multimedia decision support intervention that delivers education about advanced directives to patients recovering from critical illness. Brought to the bedside via laptop computer, this intervention increased the intent to sign an advanced directive by 25 times, compared with the commonly used advanced directive educational brochure, “Put It in Writing: Questions and Answers on Advance Directives” (American Hospital Association, 2012).

Clinical decision support in the EHR can also facilitate guideline adherence. Beeckman et al. (2013) evaluated whether a decision support system for pressure ulcer prevention improves guideline adherence with pressure ulcer prevention recommendations in a nursing home setting. They found that nurses who used the EHR system with the pressure ulcer prevention decision support were more likely to provide guideline-based pressure ulcer prevention interventions than nurses in the control group who received a paper copy of the practice guidelines.

The successful work of Dykes et al. (2010) illustrates the value of integrating fall risk assessment and clinical decision support into the EHR. Based on qualitative research with professional and paraprofessional providers (Dykes, Carroll, Hurley, Benoit, & Middleton, 2009), patients, and families (Carroll, Dykes, & Hurley, 2010), researchers learned that communication problems were a barrier to falls prevention. Nurses routinely conduct fall risk assessment on hospitalized patients, but the degree to which the results of that assessment and the associated plan are communicated to other care team members, the patient, and family members was variable. In a randomized control trial of more than 10,000 patients, they found that by using HIT to integrate fall risk assessment and clinical decision support for tailored fall prevention plans into the workflow (Carroll, Dykes, & Hurley 2012), older patients were more likely to have personalized fall prevention plans and were less likely to fall during an acute hospitalization (Dykes et al., 2010).

Remote Monitoring of Older Adults

Telehealth, defined as the use of video and biometric devices to monitor and provide care at a distance, is a rapidly growing intervention studied by nurses. The body of literature in the domain of telehealth specifically for older adults has been growing in more recent years, and numerous studies highlight the leading role of nursing in designing, implementing, and evaluating such systems. Published reports range from pilot feasibility studies to large, multisite, randomized clinical trials. One such recent trial was conducted by Takahashi et al. (2010); it examined telemonitoring in older adults with multiple chronic conditions (i.e., Tele-ERA-Elder Risk Assessment) as a tool to reduce hospitalizations and emergency department visits when compared with usual care. The telehealth device used was a commercially available one that has video monitoring, thus allowing real-time, face-to-face interaction with the provider team. Peripheral devices were attached to measure blood pressure and pulse, oxygen saturation, glucose level, and weight. The older adult study patients found home telemonitoring to be acceptable, providing a sense of safety in their homes (Pecina et al., 2011). However, home tele-monitoring among older adults with multiple comorbidities did not significantly improve self-perception of mental well-being and may worsen self-perception of physical health. Although a report on the effectiveness for reducing hospitalizations has not been published yet, findings from this trial have already highlighted the role of an RN as the individual who oversees all processes and evaluates any changes in patient status, as assessed by videoconferencing and telemonitoring.

A nurse-led study examining the effectiveness of a home-based individual telehealth intervention for stroke caregivers was conducted in South Korea (Kim et al., 2012). This study used a quasiexperimental design with a repeated-measures analysis to explore whether caregiver burden would be lower for families that received a telecare intervention in addition to standard care, when compared to the control group. Seventy-three patients from five hospitals participated in the study. A statistically significant decrease of family caregiver burden occurred in the experimental group, and the intervention was found to be cost effective.

A study by Emme et al. (2014) explored the role of home telehealth in facilitating self-efficacy in patients with chronic obstructive pulmonary disease (COPD). It was conducted within a larger initiative called the Virtual Hospital (Emme et al., 2014), which included patients admitted to the emergency department due to COPD exacerbation. Within 24 hours after admission, participants were randomly assigned to receive standard treatment using telehealth equipment with an integrated organizational support in their own home or standard treatment in the hospital. The results of the study suggest that no difference may exist between self-efficacy in COPD patients undergoing virtual admission compared with conventional hospital admission.

Keeping-Burke et al. (2013) conducted a randomized clinical trial to determine whether coronary artery bypass graft surgery patients and their caregivers who received telehealth follow up had greater improvements in anxiety levels from presurgery to 3 weeks after discharge than those who received standard care. No group differences were noted in changes in patient anxiety and depressive symptoms; however, patients in the telehealth group had fewer physician contacts. Furthermore, caregivers in the telehealth group experienced a greater decrease in depressive symptoms than those in the standard care group, and female caregivers in the telehealth group experienced greater decreases in anxiety than those in standard care.

A single-center, randomized controlled clinical trial conducted by Wakefield et al. (2012) compared two remote telehealth monitoring intensity levels (i.e., low and high) and usual care in patients with type 2 diabetes and hypertension being treated in primary care. No significant differences were found across the groups in self-efficacy, adherence, or patient perceptions of the intervention mode. The study indicated that home telehealth can enhance detection of key clinical symptoms that occur between regular physician visits; however, it called for further investigation of the mechanism of the effect of the telehealth intervention.

In the studies described previously, patients and/or their family members have to operate specific hardware and software applications as part of the telehealth intervention. Having to operate telehealth hardware and software raises the question of feasibility for older adults who may live alone and be frail, inexperienced with technology, or experiencing cognitive or functional limitations. As technology advances, opportunities arise to use systems that do not require a user to operate them; instead, the systems enable passive and ongoing monitoring of older adults’ well-being. An extensive program of research led by Rantz et al. (2012) conducted in senior housing facilities demonstrates the power of telehealth to predict adverse events and support older adults to age in place. In these studies, sensor networks (i.e., stove temperature, bed, chair, motion, and Microsoft Kinect® sensors) were deployed to assess behavioral and physiological patterns over time and identify abnormalities or emergencies. Findings, so far, suggest that the sensor data can serve as tools for early illness detection.

Other initiatives underway explore the concept of a smart home, or a residential setting with technology embedded in the residential infrastructure to enable passive monitoring of residents with the goal of assessing overall patterns of activity, quality of life, and well-being. As part of the Home-Based Environmental and Assisted Living Technologies for Healthy Elders initiative in the School of Nursing at the University of Washington, researchers have installed various sensor technologies in apartments of older adults who live in retirement communities in Seattle. The sensor technologies include motion sensors to detect how individuals move inside the home, infrastructure mediated sensing (i.e., an electricity sensor that can detect electricity consumption by electricity source), and a water sensor that detects water consumption by each water source. These features allow the detection of activities, such as meal preparation or bathroom visits, with a level of granularity that motion sensors alone cannot provide. Advanced data analysis and pattern recognition techniques allow not only the detection of activities, but also potential changes over time, for example, if data indicate a more sedentary behavior over time or an irregular pattern of activities calling for timely interventions to prevent an adverse event (Reeder, Chung, et al., 2013). Current findings indicate that older adults accept these technologies if they see a purpose, and perceived usefulness outweighs privacy concerns (Chung et al., 2014). Case studies showcase the potential of technology to identify health-related trends. However, the concept of smart homes is still an emerging one, and large longitudinal studies and clinical trials that will examine the effectiveness of such technologies and their impact on clinical or other outcomes are lacking (Reeder, Meyer, et al., 2013)

What Is In The Nursing Research Pipeline?

A search of the National Institutes of Health’s REPORTER database provided information about what nurse-led HIT studies, funded by the National Institute of Nursing, are in the pipeline. Results of several innovative studies that address the needs of and improve outcomes for patients with Alzheimer’s disease and their caregivers are forthcoming. At least four studies address dementia: two are RO1s, one is an R21, and one is an R15. RO1NR014737 (K. Williams, Principal Investigator [PI]) will test the effects of technology that connect dementia caregivers to experts for guidance in managing disruptive behaviors and supporting care at home. Experts will analyze video-recordings of the triggers, features, and precursors of the disruptive behaviors and provide prevention and management advice to caregivers. The second RO1NR011042 (D. Fick, PI) proposes to use the EHR to deliver an Early Nurse Detection of Delirium Superimposed on Dementia intervention. The EHR will provide decision support through standardized delirium assessment and management screens. The R21NR 013471 (D. Mahoney, PI) will develop an innovative bureau dresser retrofitted with sensors and an iPad® that offers visual cues and verbal prompting to help individuals with dementia dress. The team hopes to advance the technology from prototype proof-of-concept to ready it for large-scale intervention trials. Finally, the R21NR013569 (R. Hickman, PI) uses gaming technology to create an interactive, avatar-based, tailored electronic program that will engage and prepare family members for the role of surrogate decision maker when caring for individuals with impaired judgment.

Beyond the study of dementia, the value of large data-set analysis is evident to meet the aims of RO1NR010822 (E. Larson, PI). In this study, investigators are using data within a clinical data warehouse to conduct three comparative effectiveness studies about hospital-acquired infections and various contributing or preventive factors. The study will also produce policies and procedures regarding the future use of these large datasets to make them more widely available for future research. An RO3NR012802 (T. Kim, PI) takes advantage of EHR data documented during the longitudinal care of older adults as they transitioned across multiple care settings, including their homes. The focus of the study is care coordination, and the aims are to identify interventions used in care coordination, relationships among patient characteristics, and care coordination interventions and outcomes.

These exciting and innovative examples provide a snapshot of new knowledge for the future and provide excellent examples of the learning health system and the use of HIT to improve care for older adults.

How Gerontological Nurses Can Get Involved

The HIT research completed to date provides a foundation for evidence-based nursing care of older adults and a learning health system. Gerontological nurses can contribute to the learning health system in several ways. First, nurses can adopt standardized, evidence-based risk assessments in practice and work with their information technology departments or vendors to ensure these assessments, corresponding interventions, and patient outcomes are represented in a structured, coded fashion in the EHR. Linking evidence-based interventions to assessment data in the EHR will ensure that all patients receive evidence-based care during each encounter. In addition, submission of risk assessment and outcome data to a national nursing outcomes database, such as the National Database for Nursing Quality Indicators, the Collaborative Alliance for Nursing Outcomes, the Veterans Administration Nursing Outcomes Database, or Military Nursing Outcomes Database, provides a means to contribute the types of data needed for local quality benchmarking, while contributing to a learning health system that will improve the care of older adults nationally.

Challenges and New Directions

As noted throughout the current commentary, nurses are leading research related to the use of EHRs, clinical decision support, and telehealth. Many of these efforts have resulted in improved care and interventions for older adults. However, this work is not without challenges. One challenge of EHR research is often the inability to conduct randomized clinical trials. Most EHR studies are quasiexperimental because the EHR is delivered to all patients, thus negating the ability to have a simultaneous control group. When considering the quality of EHR research, nurses must note whether confounding factors were considered and adequate controls were instituted to compensate for the lack of randomization. In addition, many of these studies have multiple components. For example, in telehealth studies, the type of equipment used, the number of times a patient uses the equipment, or the quality of team communication could all affect the study outcomes, making it difficult to know which component is responsible for the impact. For decision support, it is important to monitor (a) the fidelity of the intervention to understand the amount of exposure to the advice and (b) any other interventions occurring simultaneously that could affect the outcomes. In addition, it is important to recognize that these interventions are “decision support”; that is, they are not one size fits all, and health care providers must never lose sight of individual patient needs and instances in which the decision support is not applicable.

To advance the science of HIT research, the authors of the current commentary suggest more research to:

  • understand how nurses use HIT systems in practice, the factors associated with adoption, and the effect of EHR systems on nursing practice;
  • identify the organizational factors that lead to improved quality and safety outcomes after implementation of an EHR;
  • determine how patient-reported data can be captured and used to provide clinical decision support that is aligned with patient preferences; and
  • develop HIT interventions that will facilitate the engagement of older adults in their recovery plans within hospital, home care, and long-term care settings and in maximizing self-management, wellness, and independence as they age at home.

Finally, nurse scientists must expand the settings in which HIT research occurs. A recent systematic review of nursing informatics studies revealed that 42.5% occurred in acute care, whereas only 3.75% occurred in home care or long-term care, respectively (Carrington & Tiase, 2013). Given the concentration of older adults served in home care and long-term care, these areas of practice are prime sources for knowledge generation through future studies.

References

  • American Hospital Association. (2012). Put it in writing: Questions and answers on advance directives. Retrieved from www.aha.org/content/13/putitinwriting.pdf
  • Beeckman, D., Clays, E., Van Hecke, A., Vanderwee, K., Schoonhoven, L. & Verhaeghe, S. (2013). A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: A two-armed randomized controlled trial. International Journal of Nursing Studies, 50, 475–486. doi:10.1016/j.ijnurstu.2012.09.007 [CrossRef]
  • Blumenthal, D. (2010). Launching HITECH. New England Journal of Medicine, 362, 382–385. doi:10.1056/NEJMp0912825 [CrossRef]
  • Bowles, K.H., Hanlon, A., Holland, D., Potashnik, S. & Topaz, M. (2014). Impact of discharge planning decision support on time to readmission among older adult medical patients. Professional Case Management, 19, 29–38. doi:10.1097/01.PCAMA.0000438971.79801.7a [CrossRef]
  • Carrington, J.M. & Tiase, V.L. (2013). Nursing informatics year in review. Nursing Administration Quarterly, 37, 136–143. doi:10.1097/NAQ.0b013e3182869deb [CrossRef]
  • Carroll, D.L., Dykes, P.C. & Hurley, A.C. (2010). Patients’ perspectives of falling while in an acute care hospital and suggestions for prevention. Applied Nursing Research, 23, 238–241. doi:10.1016/j.apnr.2008.10.003 [CrossRef]
  • Carroll, D.L., Dykes, P.C. & Hurley, A.C. (2012). An electronic fall prevention toolkit: Effect on documentation quality. Nursing Research, 61, 309–313. doi:10.1097/NNR.0b013e31825569de [CrossRef]
  • de Chung, J., Reeder, B., Lazar, A., Joe, J., Demiris, G. & Thompson, H.J. (2014). Exploring an informed decision-making framework using in-home sensors: Older adults’ perceptions. Informatics in Primary Care, 21, 73–77. doi:10.14236/jhi.v21i2.53 [CrossRef]
  • Cooley, M.E., Lobach, D.F., Johns, E., Halpenny, B., Saunders, T.-A., Del Fiol, G. & Abrahm, J.L. (2013). Creating computable algorithms for symptom management in an outpatient thoracic oncology setting. Journal of Pain and Symptom Management, 46, 911–924. doi:10.1016/j.jpainsymman.2013.01.016 [CrossRef]
  • Dowding, D.W., Turley, M. & Garrido, T. (2012). The impact of an electronic health record on nurse sensitive patient outcomes: An interrupted time series analysis. Journal of the American Medical Informatics Association, 19, 615–620. doi:10.1136/amiajnl-2011-000504 [CrossRef]
  • Dykes, P.C., Carroll, D.L., Hurley, A.C., Benoit, A. & Middleton, B. (2009). Why do patients in acute care hospitals fall? Can falls be prevented?Journal of Nursing Administration, 39, 299–304. doi:10.1097/NNA.0b013e3181a7788a [CrossRef]
  • Dykes, P.C., Carroll, D.L., Hurley, A., Lipsitz, S., Benoit, A., Chang, F. & Middleton, B. (2010). Fall prevention in acute care hospitals: A randomized trial. Journal of the American Medical Association, 304, 1912–1918. doi:10.1001/jama.2010.1567 [CrossRef]
  • Emme, C., Mortensen, E.L., Rydahl-Hansen, S., Østergaard, B., Svarre Jakobsen, A., Schou, L. & Phanareth, K. (2014). The impact of virtual admission on self-efficacy in patients with chronic obstructive pulmonary disease—A randomised clinical trial. Journal of Clinical Nursing. Online advanced release. doi:10.1111/jocn.12553 [CrossRef].
  • Fossum, M., Alexander, G.L., Ehnfors, M. & Ehrenberg, A. (2011). Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly. International Journal of Medical Informatics, 80, 607–617. doi:10.1016/j.ijmedinf.2011.06.009 [CrossRef]
  • Friedman, C.P., Wong, A.K. & Blumenthal, D. (2010). Achieving a nationwide learning health system. Science Translational Medicine, 2(57), 1–3. Retrieved from https://www.networkforphl.org/_asset/3pcp7d/AchievingNationwideLHS.pdf doi:10.1126/scitranslmed.3001456 [CrossRef]
  • Greene, S.K., Shi, P., Dutta-Linn, M.M., Shoup, J.A., Hinrichsen, V.L., Ray, P. & Yih, W.K. (2009). Accuracy of data on influenza vaccination status at four Vaccine Safety Datalink sites. American Journal of Preventive Medicine, 37, 552–555. doi:10.1016/j.amepre.2009.08.022 [CrossRef]
  • Hickman, R.L. Jr. , Lipson, A.R., Pinto, M.D. & Pignatiello, G. (2014). Multimedia decision support intervention: A promising approach to enhance the intention to complete an advance directive among hospitalized adults. Journal of the American Association of Nurse Practitioners, 26, 187–193. doi:10.1002/2327-6924.12051 [CrossRef]
  • Institute of Medicine. (2010). The future of nursing: Leading change, advancing health. Washington, DC: National Academies Press. Retrieved from http://books.nap.edu/openbook.php?record_id=12956&page=R1
  • Institute of Medicine. (2011). Digital infrastructure for the learning health system: The foundation for continuous improvement in health and health care—Workshop series summary. Washington, DC: National Academies Press.
  • Keeping-Burke, L., Purden, M., Frasure-Smith, N., Cossette, S., Mc-Carthy, F. & Amsel, R. (2013). Bridging the transition from hospital to home: Effects of the VITAL telehealth program on recovery for CABG surgery patients and their caregivers. Research in Nursing & Health, 36, 540–553. doi:10.1002/nur.21571 [CrossRef]
  • Kim, S.S., Kim, E.J., Cheon, J.Y., Chung, S.K., Moon, S. & Moon, K.H. (2012). The effectiveness of home-based individual tele-care intervention for stroke caregivers in South Korea. International Nursing Review, 59, 369–375. doi:10.1111/j.1466-7657.2011.00967.x [CrossRef]
  • McFadden, K.L., Stock, G.N. & Gowen, C.R. III. (2014). Leadership, safety climate, and continuous quality improvement: Impact on process quality and patient safety. Health Care Management Review, 40(1). doi:10.1097/HMR.0000000000000006 [CrossRef]
  • Pecina, J.L., Vickers, K.S., Finnie, D.M., Hathaway, J.C., Hanson, G.J. & Takahashi, P.Y. (2011). Telemonitoring increases patient awareness of health and prompts health-related action: Initial evaluation of the TELE-ERA study. Telemedicine Journal and E-Health, 17, 461–466. doi:10.1089/tmj.2010.0213 [CrossRef]
  • Rantz, M.J., Skubic, M., Koopman, R.J., Alexander, G.L., Phillips, L., Musterman, K. & Miller, S.J. (2012). Automated technology to speed recognition of signs of illness in older adults. Journal of Gerontological Nursing, 38(4), 18–23. doi:10.3928/00989134-20120307-01 [CrossRef]
  • Reeder, B., Chung, J., Lazar, A., Joe, J., Demiris, G. & Thompson, H.J. (2013). Testing a theory-based mobility monitoring protocol using in-home sensors: A feasibility study. Research in Gerontological Nursing, 6, 253–263. doi:10.3928/19404921-20130729-02 [CrossRef]
  • Reeder, B., Meyer, E., Lazar, A., Chaudhuri, S., Thompson, H.J. & Demiris, G. (2013). Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. International Journal of Medical Informatics, 82, 565–579. doi:10.1016/j.ijmedinf.2013.03.007 [CrossRef]
  • Rosen, A.K., Singer, S., Zhao, S., Shokeen, P., Meterko, M. & Gaba, D. (2010). Hospital safety climate and safety outcomes: Is there a relationship in the VA?Medical Care Research Review, 67, 590–608. doi:10.1177/1077558709356703 [CrossRef]
  • Takahashi, P.Y., Hanson, G.J., Pecina, J.L., Stroebel, R.J., Chaudhry, R., Shah, N.D. & Naessens, J.M. (2010). A randomized controlled trial of telemonitoring in older adults with multiple chronic conditions: The tele-ERA study. BMC Health Services Research, 10, 255. doi:10.1186/1472-6963-10-255 [CrossRef]
  • Wakefield, B.J., Holman, J.E., Ray, A., Scherubel, M., Adams, M.R., Hills, S.L. & Rosenthal, G.E. (2012). Outcomes of a home telehealth intervention for patients with diabetes and hypertension. Telemedicine Journal and E-Health, 18, 575–579. doi:10.1089/tmj.2011.0237 [CrossRef]
Authors

Dr. Bowles is van Ameringen Professor in Nursing Excellence and Director of the Center for Integrative Science in Aging, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania; Dr. Dykes is Senior Nurse Scientist, Director of the Center for Patient Safety Research and Practice, Director of the Center for Nursing Excellence, Brigham and Women’s Hospital, Boston, Massachusetts; and Dr. Demiris is Alumni Endowed Professor in Nursing, Professor in Biomedical and Health Informatics, School of Medicine, and Director, Clinical Informatics and Patient Centered Technologies, University of Washington, Seattle, Washington. Dr. Demiris is also Graduate Program Director, Biomedical and Health Informatics, University of Washington, Seattle, Washington.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Kathryn H. Bowles, PhD, FAAN, FACMI, van Ameringen Professor in Nursing Excellence and Director of the Center for Integrative Science in Aging, University of Pennsylvania School of Nursing, Claire M. Fagin Hall, Room 340, 418 Curie Boulevard, Philadelphia, PA 19104; e-mail: bowles@nursing.upenn.edu.

10.3928/19404921-20121222-01

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