Achieving Meaningful Use with Decision Support Research

Achieving Meaningful Use in Research with Information Technology Column

by Dr. Kathryn Bowles, Senior Editor

Associate Professor University of Pennsylvania


Bowles , K. (February, 2011). Achieving Meaningful Use with Decision Support Research. Achieving Meaningful Use in Research with Information Technology Column. Online Journal of Nursing Informatics (OJNI),15 (1). Available at http://ojni.org/issues/?p=347.


Achieving Meaningful Use in Research with Information TechnologyThe Health Information Technology Act of 2009 (HITECH) provides incentives for electronic health record (EHR) adoption and the meaningful use criteria direct how information technology will eventually improve the quality of patient care (U.S. Department of Health and Human Services, 2010; Weinstock, 2010). There are several meaningful use criteria published in stage one. They include capturing health information electronically in a structured format, using that information to track key clinical conditions and communicating the information for care coordination, implementing decision support tools for disease and medication management, engaging patients and their families, and reporting clinical quality measures and public health information (U.S. Department of Health and Human Services, 2010). The focus of this manuscript is the meaningful criteria to implement decision support tools and an example of such.

Stage 1 Clinical Decision Support Objective

At this time, the meaningful use stage 1 objective for decision support requires implementation of only one clinical decision support rule relevant to specialty or high clinical priority (U.S. Department of Health and Human Services, 2010). The certification criterion calls for ability within the EHR to implement automated electronic clinical decision support rules (in addition to drug-drug and drug-allergy contraindication checking) using data elements from the problem list, medication list, demographics, and laboratory test results. The clinicians should be notified automatically and electronically in real-time with care suggestions based upon clinical decision support rules. The purpose of this article is to illustrate an example from our discharge planning decision support research that meets this meaningful use criterion.

Identification of the Problem

Several years ago when analyzing data from a randomized clinical trial of high risk older adults,(Naylor et al., 2004) we discovered that the majority of the control group patients  got no post acute care (Bowles, Naylor, & Foust, 2002). Given that the enrollment criteria assured that they were high risk for poor outcomes we were shocked by this finding. Using case studies we investigated how and why patients with needs were discharged without post acute referrals for services such as home care. Clinicians shared several reasons for suboptimal decisions when making discharge plans (Bowles, Foust, & Naylor, 2003) leading our team to develop an expert discharge referral decision support tool funded by the National Institute of Nursing Research (RO1-007674).

The Discharge Decision Support Solution

The discharge decision support system (D2S2) was developed using 355 case studies assembled from the records of hospitalized older adults (Bowles et al., 2009). A multidisciplinary team of experts in discharge planning and the care of the elderly reviewed the cases and recommended a referral or not and identified the characteristics of the case that supported their decisions. Overall, 26 patient and clinical characteristics were considered and logistic regression analysis produced a model of six factors associated with the need for a post acute referral. The model was validated with a second sample and had an area under the curve >0.80 indicating it had adequate sensitivity (ability to correctly identify who needs a referral) and specificity (ability to correctly identify who does not need a referral) (Bowles et al., 2009). Further validation of the model examined outcomes at 12 weeks after discharge showing experts indeed identified patients who went on to have poor post discharge outcomes such as readmission (Bowles et al., 2008).

Current Decision Support Testing

Subsequent work with the models have simplified their administration to improve feasibility for the clinical setting by applying points to each item and creating a version for patients able to self report (cognitively intact) or via proxy (non-verbal or cognitively impaired). The tools are currently being tested in two academic medical centers in Pennsylvania and New York funded by several sources (Edna G. Kynett Foundation, the Leonard David Institute, Frank Morgan Jones Fund, and the NewCourtland Center for Transitions and Health).

The D2S2 is administered as part of the nursing admission assessment and repeated every 8 days if the patient is still hospitalized. Based on the answers to questions about mobility, self rated health, age, numbers of co-morbid conditions, length of stay, and depression points accumulate toward a pre-determined cut-off score to alert the clinical team about patients who should be considered for post acute referral. For patients not able to self report, caregiver availability and income enter the model.

Achieving Meaningful Use

The D2S2 serves as an excellent example of how to use clinical decision support to achieve meaningful use. The goal of this program of research is to improve clinical decision making so that patients in need get the right care at the right time. The improvements in quality of care are measured by decreased 30 and 60 day readmission rates and decreases in unmet problems and needs after discharge. Patients for whom the tool is used are compared to those without the decision support. The study is ongoing and results are expected the summer of 2011. So far, the tool identifies 13-25% more patients for referral than clinicians and of those referred by the decision support but not by the clinicians (because of refusal or clinician decision) the readmission rate is 27% by 60 days.

Embedding Decision Support in the HER

Ideally decision support runs behind the scenes and is not intrusive to the user. For example, when embedded in the EHR, the D2S2 is calculated automatically as the nurse enters the answers to the clinical questions. In addition, some answers such as age might be pulled from other parts of the medical record and not need to be repeated. Based on the pre-determined cut-off score an alert is sent to the discharge planning team stating this is a patient who should be considered for post acute referral. The discharge planners are documenting when they agree or disagree with the advice for further evaluation by our team.

Conclusion and Future Directions

The D2S2 is a simple and accurate tool to support a common and important decision in patient care.  Our team submitted a proposal to the NINR for widespread testing of this tool and comparative effectiveness testing of the D2S2 with the Early Screen for Discharge Planning (Holland, Harris, Leibson, Pankratz, & Krichbaum, 2006); another decision support tool that identifies patients for comprehensive discharge planning. Thirty four hospitals of various types and sizes representing 15 states signed on to participate. In addition, our team was recently funded by NINR (RO1-007674) with a competing renewal of this work to expand its development and testing to a generalized sample of all hospitalized older adults and to predict the yes/no decision and the site of referral such as home care, skilled nursing facility, rehabilitation, or inpatient hospice. There is great momentum and interest in decision support applications. There are few decision support applications for nurses or developed by nurses. This is a rich and timely opportunity for nursing decision support research.


Bowles, K. H., Foust, J. B., & Naylor, M. D. (2003). Hospital discharge referral decision making: A multidisciplinary perspective. Applied Nursing Research, 16(3), 134-143.

Bowles, K. H., Holmes, J. H., Ratcliffe, S. J., Liberatore, M., Nydick, R., & Naylor, M. D. (2009). Factors identified by experts to support decision making for post acute referral. Nursing Research, 58(2), 115-122.

Bowles, K. H., Naylor, M. D., & Foust, J. B. (2002). Patient characteristics at hospital discharge and a comparison of home care referral decisions. Journal of the American Geriatrics Society, 50(2), 336-342.

Bowles, K. H., Ratcliffe, S. J., Holmes, J. H., Liberatore, M., Nydick, R., & Naylor, M. D. (2008). Post-acute referral decisions made by multidisciplinary experts compared to hospital clinicians and the patients’ 12-week outcomes. Medical Care, 46(2), 158-166.

Holland, D. E., Harris, M. R., Leibson, C. L., Pankratz, V. S., & Krichbaum, K. E. (2006). Development and validation of a screen for specialized discharge planning services. Nursing Research, 55(1), 62-71.

Naylor, M. D., Brooten, D. A., Campbell, R. L., Maislin, G., McCauley, K. M., & Schwartz, J. S. (2004). Transitional care of older adults hospitalized with heart failure: A randomized, controlled trial. Journal of the American Geriatrics Society, 52(5), 675-684.

U.S. Department of Health and Human Services. (2010). Medicare and medicaid programs: Electronic record incentive program. Federal Register, 75(144), 44314-44588.

Weinstock, M. (2010). For hospitals and meaningful use, context is everything. Hospitals & Health Networks, 84(8), 20-21.


Dr. Bowles holds a BSN from Edinboro University of Pennsylvania, an MSN from Villanova, and a PhD from the University of Pennsylvania. In addition to her position at the Penn School of Nursing, she is the Beatrice Renfield Visiting Scholar for the Visiting Nurse Service of New York, a Senior Fellow in the Leonard Davis Institute, a faculty member in the Ackoff Center for Advancement of Systems Approaches, and Director of the Health Informatics Minor. Dr. Bowles leads a program of research in the use of information technology to improve healthcare for elders and support healthcare provider’s decision-making regarding hospital discharge referrals for elders.

Dr. Bowles’ program of research examines decision making supported by information technology to improve care for older adults. Her ongoing study, funded by the National Institute of Nursing Research, focuses on decision-making and the development of decision support for hospital discharge referral decisions. Other research areas include telehealth technology, quality of life among frail elders, intervention research to close the health care racial divide, and the use of large databases in home care to support clinical decision-making.

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