The transition from a healthcare system based on fragmented fee-for-service payments to one of an accountable system based on an episode or global payment models has generated considerable enthusiasm. Proponents note that a system based on accountable care can constrain spending growth and improve quality, and in fact, existing evidence suggests cautious optimism.

 HHS has set out a goal to move 50% of payments to these episode or global payment models by 2018.

 Yet, from the perspective of providers, the new environment presents many challenges. This transition involves a change in business models, and as such, it calls for the reexamination of the myriad of systems that drive the provision of care. This issue of 

The American Journal of Accountable Care 

) focuses on some specific examples surrounding this change and the emphasis on use of health information technology (IT).

To highlight a few of those examples from this issue, in their study, Steckler et al specifically examine challenges for physician- led organizations seeking to transform from a fee-for-service system toward one of accountable care. They identify several common issues, including underutilization of IT and few care management tools, but addressing these challenges is complicated by a lack of resources. To combat this, the authors recommend a thorough assessment of existing gaps and resource allocation in order to properly gauge ACO readiness early on in the implementation process.

Alvandi examines the role of electronic health records (EHRs) in a transformed, accountable healthcare system. The potential of EHRs to facilitate information flows to support better care at a lower cost is noted, but it is important to recognize both the multidimensionality of EHR systems as well as the obstacles associated with adoption. Training is crucial, and moreover, acceptance by providers and patients is still a challenge. Managers must recognize that EHRs are not systems to be adopted and then forgotten once in place, but rather, they must evolve as technology, management needs, and capabilities all change.

Oliver and Bacheller focus on the role of care coordination tools for supporting accountability. They identify several important aspects of these tools: first and foremost, tools must be tailored to the population; plans serving Medicaid patients will have different requirements than those serving commercial patients. Also, tools must facilitate use by having a single access point in order to get a view of the entire panel and allow easy application of care guidelines. The facilitation of improved communication is another important aspect of care coordination and health management tools—not only between providers, but between other systems as well, such as is accomplished with the EHR.

These articles, as well as Monsen et al and Wasser et al, emphasize the important role that IT will have in the healthcare system of the future. In many ways, we are at the beginning of the accountable care journey. Success is not guaranteed; IT can help, but is no panacea. Continued work to identify effective IT systems and strategies related to implementation will be needed. At 

, we hope to continue to provide related useful information.

in health care spending and quality 4 years into global payment. N Engl J Med. 

	1. Song Z, Rose S, Safran DG, Landon BE, Day MP, Chernew ME. Changes 

response to Soumerai and Koppel. The Incidental Economist blog. http://theincidentaleconomist.

com/wordpress/mcwilliams-et-al-response-to-soumerai-and-koppel/. 

Published May 5, 2015. Accessed September 8, 2015.

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care-smarter-spending-healthier-people.html. Published January 26, 2015. Accessed 

	3. Burwell SM. Progress towards achieving better care, smarter spending, healthier 

Articles

The American Journal of Accountable Care

Transitioning to a healthcare system based on accountable care presents many challenges, though the potential benefits may be well worth the effort.

Transitioning Our Healthcare System Toward Accountable Care

 The objective was to investigate the viability of projecting demographic information from a large commercial managed care database to the entire US population, and to provide a simple, pertinent weighting scheme.

 Data from the HealthCore Integrated Research Database (HIRD), a repository of enrollee administrative claims from 14 regionally dispersed US health plans, were compared with US Census data. Census-defined regions, gender, and age groups served as demographic standards. To guard against small differences between these large samples appearing statistically significant, an alternative version of goodness-of-fit statistics was used to assess the overall fit of characteristic group variables.

This study compared 14.8 million HIRD enrollees and the 307.7 million individuals from the 2009 US Census. Gender distribution was similar in the groups: females comprised 49.8% (HIRD) and 49.3% (Census). Relative to the US Census, HIRD enrollees were overrepresented in the midwest, underrepresented in the south, and comparable in the northeast and west, with differences of 1% and 0.6%, respectively. HIRD was overrepresented in the 30-to-59 years category and underrepresented in the <5 years and ≥65 years groups; the groups were similar in the 5-to-30 years age group.

 In the absence of data on disease prevalence, treatment patterns, and outcomes, commercial health plan databases may provide a reasonable representation of the national population when appropriately weighted to reflect differential demographic characteristics. The ability to conduct and rely on the results of such projections could be of value to key stakeholders such as healthcare planners, policy makers, and payers.Researchers are keenly interested in ascertaining the impact of disease on society. One of the central elements of this determination is knowledge about the number of individual patients with a disease or condition of interest within a specific region, age group, or gender.

 The exact count or even estimates of patients affected by a given disease may not always be available for a variety of reasons, including the absence of reporting requirements or a lack of organized and maintained disease registries or longitudinal patient databases.

 To obtain an understanding of the size of patient populations that are not well quantified and characterized, often the only workable option is to extrapolate from available data in repositories such as registries and health plan databases (among others).

Disease prevalence can be estimated in subpopulations with accessible data,

 but in extrapolating to the general population, systematic differences in demographic composition must be taken into account. In the United States, it is unlikely that data sets in existing commercial health insurance databases will be representative enough by themselves to present an accurate estimate of the national population.

 As a result, there is considerable interest in census decomposition methodologies or similar approaches that are capable of rendering the data in such nonrepresentative population samples in a form comparable to US Census data.

Cognizant of their role as a vital and reliable source of data on disease prevalence and the size limitations of commercial health plan databases, the objective of this study was to develop a weighting framework for projecting data from commercial databases to a population matching the demographic composition encompassed by the US Census.

This study compared data, demographic structures, and characteristics from a large commercial research database, the HealthCore Integrated Research Database (HIRD), which is notable for its size and geographic breadth, with data from the 2009 US Census. To create a basis for the approximation of counts relative to the US Census data, standard statistical procedures incorporating a suitable alternative to the goodness- of-fit method were used to establish weights for the HIRD. The weighting formulation was then tested with a sample of patients from the northeast region of the United States who were diagnosed with acute coronary syndrome (ACS).

This study utilized a large commercial administrative claims database, the HIRD, which contains a broad spectrum of medical, pharmacy, and laboratory information on more than 46 million enrollees in 14 geographically dispersed managed care plans across the United States. The broad range of service models encompassed by these plans includes health maintenance organizations, point of service, preferred provider organizations, and indemnity plans. The data queried from the HIRD were categorized into geographic regions matching those used by the US Census Bureau.

The US Census Bureau publishes the American Community Survey results every year. The American Community Survey reports population numbers in categories including age, gender, race, and geographic region. No disease prevalence and other types of healthcare utilization information are collected by the American Community Survey. This study was conducted prior to the official release of the 2010 US Census data; as a result, population estimates from the US Census Bureau’s 2009 American Community Survey were used for the total count of individuals residing in the 50 US states.

Researchers had access to limited patient data in this study. Strict measures, in compliance with the 1996 Health Insurance Portability and Accountability Act (HIPAA), were observed to ensure the preservation of patient anonymity and confidentiality throughout. The study did not involve the collection, use, or transmittal of individually identifiable data. It was conducted under the Research Exception provisions of the Privacy Rule, 45 CFR 164.514(e); institutional review board sanction was not indicated.

Health plan members within the HIRD who had at least 1 day of health plan enrollment between January 1, and December 31, 2009, were eligible for inclusion in the study. This interval was selected because it represented the most current US Census Bureau’s American Community Survey data release available at the time of the study. Patients with ACS were selected to perform the projection demonstration. The disease was identified with 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes 410.x1 and 411.1x in the claims database.

Goodness-of-fit statistics was not applicable to match the samples because even small differences would appear to be statistically significant because of the large sample sizes. An alternative interpretation of the fit approach was used to examine the overall fit of the lines—census-defined regions, gender, and age groups&mdash;characterizing the HIRD and the US Census data. Statistical analyses were conducted with SAS version 9.2 (SAS Institute Inc, Cary, North Carolina).

Standard statistical procedures, comprising of an alternative version of goodness-of-fit statistics, were used to establish weights for the HIRD, to facilitate the approximation of counts relative to the US Census data. The linear weighting was computed as the percentage of the overall population divided by the percentage within the HIRD. Weighting schemes enable the projection from smaller known samples to larger populations in which the desired prevalence rate and other target information are not known. By using weights in a linear model along with specific variables, it is possible to make projections to the larger population by employing the relevant attributes of smaller population.

This equation yielded a multiplication factor that was used to compute the weighted number of patients within a geographic region, age group, and gender for a specific disease type or drug classification (

). On the basis of these distributions, weights were calculated to adjust for any differences in gender, geographic region, and age distributions observed between the HIRD population and the US census population estimates.

Study Populations and Demographic Comparison

During the 2009 calendar year, the HIRD included 14.8 million enrollees, and the US Census Bureau’s 2009 American Community Survey data projected in excess of 307.7 million individuals, within an estimated accuracy of 0.1% (margin of error: 0.001) 18 who were used as a base populations in this study. The HIRD population was similar to the US Census estimates in gender distribution, with females comprising 49.8% and 49.3% of their totals, respectively. Relative to the US Census estimates, the HIRD population appeared overrepresented in the midwest and underrepresented in the south. The HIRD population closely matched US Census estimates for the northeast and west regions, differing by only 1% in the northeast and 0.6% in the west (

The age group distributions of the HIRD and US Census populations are shown in 

. The HIRD population had relatively higher representation of age categories between 30 and 59 years; it was underrepresented in the age categories <18 years and ≥65 years relative to the US Census. Although there was close agreement between the 2 populations for ages 5 to 30 years and 55 to 70 years, the overall age group of 18 to 64 years is overrepresented in the HIRD.

Weight Computation Based in the Northeast Region

To demonstrate the weight computation model, weight calculations were applied to the northeast region. In 2009, approximately 0.70% of the US Census population was male, aged 45 to 49 years, and lived in the northeast, while around 0.72% of the HIRD population shared the same geographic region, gender, and age characteristics. Thus, the weight for the male population aged 45 to 49 years living in the northeast during that time period was 0.9690% (

Projection of ACS Patients in the Northeast

 reports the results of weighting the number of HIRD patients with ACS in the northeast region within each age and gender stratum and projecting to the northeast US Census Bureau population. The HIRD had a total of 452 male members from the northeast region, aged 45 to 49 years, who had at least 1 claim with a diagnosis for ACS from January 1 to December 31, 2009. On the basis of the weight for this population group (0.9690), the projection of ACS diagnosis in a representative sample the same size as the HIRD repository (~4.82% of the US population) would be 438 patients (452 × 0.9690) and 9089 in the overall US Census Bureau population (438 ÷ 4.82%). Application of this weighting scheme results in a greater proportion of ACS patients in the ≥65 years age category and a smaller proportion of patients aged 30 to 64 years relative to the original HIRD estimate (Figure 2).

While healthcare data are hardly abundantly available for the entire US population, a considerable volume may be found in veritable data silos such as institutional disease registries and the transactional databases of health plans, among other repositories. For healthcare planners and budget directors, access to plausible population estimates is crucial for decision making. One avenue for population level figures for health budget projections and allocations is to extrapolate from smaller data collections. It is essential to have robust and reliable weighting tools, which are capable of achieving the low margin of error requirements, necessary for such projections.

 Driven by this need, this study developed a simple weighting tool to project heath plan data to estimate prevalence rates at the national level.

Although the HIRD represents a population that is slightly less than one-twentieth (~4.82%) of the US population—as represented by the 2009 US Census Bureau count&mdash;the data in the HIRD are remarkably representative of the entire US population. HIRD data trended in parallel with the US Census data on gender distribution, regional distribution in the northeast and west regions, but as was expected, it was overweight in the 30 to 59 years age category because the repository consists largely of employer-insured working age people.

Reflecting the source of the majority of people represented in the HIRD repository—enrollees of employer sponsored commercial healthcare insurance&mdash;the population aged ≥65 years appears to be relatively underrepresented. Still, the HIRD contains a sizable sample of ≥65-years-old enrollees who may be receiving commercial employer sponsored health benefits, or Medicare advantage, supplement, or Part D benefits. The sample size of this population is substantive enough to allow the application of this weighting methodology to extrapolate the data into the overall US population with statistically acceptable variance.

The weighting methodology developed in this study was tested on the ACS patients from northeast region as an illustration of how the weighting scheme may be applied in practice. While this example specifically addressed ACS patients, it demonstrated how the number of patients in the overall US population for any disease may be estimated from commercially derived healthcare data repositories like the HIRD. This study essentially demonstrated that by using a linear weighting methodology that accounts for differences in geographic regions, age, and gender between an accessible database and the US Census data, it was possible to estimate the prevalence of a number of important healthcare factors. Among areas that may be evaluated using this approach are disease prevalence, healthcare resource utilization, treatment patterns for therapies of interest, and current and potential use of pharmaceutical agents and other treatment modalities.

One of the key objectives of this study was the development of a projection method and a weighting scheme that could be applied to a range of disease conditions and therapeutic categories for which data were available in a repository—such as the HIRD. An important strength of this approach is that it allows for adjustments in the variables or for the updating of estimates of interest with the most current or different data as needed.

Weighed estimations have important planning, resource allocation, and cost management implications for a variety of stakeholders including patients, providers, and payers who have to make decisions based on research results, disease prevalence, treatment availability, and drug utilization, among other factors.

The results of the weighting scheme and ACS projection example discussed in this study must be viewed against some important limitations. This study relied on secondary data from commercial health plans across the United States. These data may have some relevance to similar commercial health plans, but only limited external validity for different patient populations such as the US Medicaid and Medicare programs. In addition, administrative claims lack data on race, ethnicity, and risk factors capable of influencing outcomes. Administrative claims data are prone to over- and underestimations (eg, for patients, disease, medication use, other areas) because of basic assumptions about index events, inability to capture and account for all treatments received by patients, and basic coding and clerical errors. Furthermore, extrapolation was done beyond the point of observable data, contravening a standard requirement of statistical methodology, and likely impacting the robustness of the results. In addition, notable differences existed between the values in the HIRD commercial database and the US Census data. The weights were calculated on the basis of 2009 ACS projections, not official US Census counts.

Consistent with its commercial employment origins and characteristics, the HIRD repository, while representative of US Census data, was overweighting the 30-to-59 years category. The age groups ≥65 years were underrepresented in the HIRD but still accounted for a substantial sample size. While extrapolations beyond observable data have statistical limitations, in the absence of data on disease prevalence and treatment for the US population as a whole, commercial databases could be viable for projecting patient counts within US Census parameters. This could be invaluable to key stakeholders such as healthcare planners, policy makers, and payers.

Bernard B. Tulsi, MSc, provided writing and other editorial support for this manuscript. The authors wish to thank Chaozheng Yang, MS, former research analyst at HealthCore, Inc, for contributions to the study’s design and data analysis.

HealthCore, Inc (TW, BW, OT), Wilmington, DE; AstraZeneca Pharmaceuticals LP (JWY), Wilmington, DE.

Funding for this research project was provided by AstraZeneca Pharmaceuticals LP.

 Drs Wasser and Tunceli and Mr Wu are employees of HealthCore, Inc, a wholly owned research and consulting subsidiary of Anthem, a national health insurance company. Dr YÃ„Â

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7. Mehta P, Antao V, Kaye W, et al. Prevalence of amyotrophic lateral sclerosis - United States, 2010-2011. 

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11. Costa MA, Huang SS, Moore M, Kulldorff M, Finkelstein JA. New approaches to estimating national rates of invasive pneumococcal disease. 

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13. Hanson LA, Zahn EA, Wild SR, Dopfer D, Scott J, Stein C. Estimating global mortality from potentially foodborne diseases: an analysis using vital registration data. 

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This study tests the feasibility of projecting commercial insurance demographic information to the US Census population, and creating the framework for a simple weighting scheme.

Applying Weighting Methodologies to a Commercial Database to Project US Census Demographic Data

To improve the interoperability and exchange of electronic healthcare data, methods are needed to specify healthcare interventions across disciplines and settings. To that end, this study evaluated the applicability for acute care of a multidisciplinary interface terminology commonly used in community settings: the Omaha System.

 Descriptions of acute care nursing from interviews and the literature were mapped to 121 defined Omaha System terms and were validated by clinical and terminology experts.

Mapping results showed that 86% of Omaha System terms were applicable in acute care for medical-surgical and intensive care unit settings. Phrases were also identified that were not represented by an Omaha System term, such as “presence,” “critical thinking,” and “rapid response."

 Further research is needed to evaluate additional phrases identified during the mapping process and to determine the acceptability and usefulness of the Omaha System for nursing documentation in acute care electronic health records. Use of the Omaha System as a multidisciplinary terminology may bridge acute and community care settings and serve as a strategy for improving healthcare information interoperability and exchange in the era of accountable care.The US healthcare system is experiencing rapid changes in business models, reporting requirements, and regulatory oversight.

 Drivers of this change are high costs of healthcare and governmental regulations designed to reduce cost, leverage the benefits of technology, and improve quality.

As evidence of this changes under the Affordable Care Act, government reimbursement becomes linked to healthcare facilities, contingent on quality indicators such as: 1) improved health outcomes, 2) prevention of hospital readmissions, 3) improved patient safety and reduced medical errors, and 4) promotion of wellness and health.

Similarly, the American Recovery and Reinvestment Act of 2009 has driven healthcare delivery to adopt electronic health records (EHRs).

Collectively, these concerns for efficiency and improvement of healthcare delivery and outcomes highlight the need for standards and platforms for documenting healthcare interventions. Within this context, nursing leaders assert the need to more clearly specify and evaluate the impact of nursing care.

 This is essential to promoting data-driven decision making, interoperability across systems, and the exchange of data across clinical settings, healthcare systems, and clinical data repositories for research, such as those funded through US Clinical Translational Science Awards.

Specific to nursing, there has been a growing movement over the last few decades to construct, apply, and evaluate standardized nursing terminologies. There are 4 interface terminologies that have been recognized by the American Nurses Association for documenting nursing interventions: Nursing Interventions Classification, the International Classification for Nursing Practice, the Clinical Care Classification, and the Omaha System.

 Of these, the Omaha System is the only multidisciplinary terminology.

The Omaha System is a standardized interface terminology that exists in the public domain.

It was developed beginning in 1975 with the support of 4 federally funded grants and was first published in 1992 (later revised in 2005). It originated at the Visiting Nurses Association in Omaha, Nebraska, and is widely used in community care EHRs to document patient care.

 The Omaha System is organized around a structured “Problem” list, and is suitable for documentation at the point of care; its use has been explored and evaluated in various community and outpatient settings for multiple disciplines.

 Numerous studies describe use of the Omaha System for clinical decision support, including dissemination of clinical guidelines for evidence-based practice in diverse populations and varied clinical settings.

 Exchange of Omaha System data in continuity of care documents (CCDs) using the consolidated-clinical document architecture has been demonstrated in working CCDs, demonstrating interoperability and exchange across systems.

The ontological structure of the Omaha System is unique among interface terminologies, and its central problem list concept enables relational data collection of assessments, interventions, and outcomes.

The Omaha System consists of 3 relational components: the Problem Classification Scheme, the Intervention Scheme, and the Problem Rating Scale for Outcomes (as shown in 

 The Problem Classification Scheme consists of 42 problems that comprehensively and holistically describe health. Each Problem has a definition and unique signs/symptoms. The Intervention Scheme of the Omaha System is a 3-level hierarchical arrangement of terms that relate to a patient Problem.

 The Problem Rating Scale for Outcomes measures Problem-specific Knowledge, Behavior, and Status using a Likert-type ordinal scale (1 = lowest, 5 = highest). The Omaha System terms and outcome measures may be used on paper or embedded within software with emphasis on avoiding customization of the hierarchy or defined terms in order to maintain the taxonomic structure to ensure rigor and interoperability.

The following description of the Intervention Scheme with a nursing intervention example is provided for demonstration purposes. In the Omaha System, interventions are related to a single Problem concept. The example addresses the “Skin” Problem, which includes signs/symptoms such as a lesion or rash (definition in

, at the first level, a “Category” term specifies the action of the intervention. There are 4 Category terms: 1) Teaching, guidance, and counseling; 2) Treatments and procedures; 3) Case management; and 4) Surveillance (definitions in Table 2). In this example, a nurse might perform “Treatments and procedures” for a wound. At the second level of the Intervention Scheme, 75 defined “Target” terms further specify the nature of the intervention. In this example, the applicable Omaha System Target is “dressing change/wound care” (definition in 

). At the third level of the Intervention Scheme are suggested care description terms. This level is fully customizable, and therefore, the facility protocol or other evidence-based wound care guideline may be referenced as needed at the care description level with the appropriate granularity for clinical guidance. Thus, the intervention in this example consists of 4 linked terms that are data points (Problem [1]-Category [2]-Target [3]-Care description [4]): “Skin-Treatments and procedures-dressing change/wound care-guideline name.” The linguistic syntax of these 4 linked terms/data points may be expressed in sentence form as follows: “I (the nurse) addressed the Skin Problem (1) by performing Treatments and procedures (2)-dressing change/wound care (3), and I used the facility guideline (4).”

In any software that enables Omaha System documentation, these 4 terms may be aggregated and abbreviated for single-click documentation of the intervention’s 4 linked terms/data points that can then be stored or used in predictive algorithms. Numerous studies highlight uses of the Omaha System Intervention Scheme for practice, education, and research.

 Evidence-based Omaha System guidelines have been developed by experts, are available online, and have been incorporated in clinical software for clinical decision support and documentation.

There are few studies evaluating the Omaha System within the acute care setting, despite the trend toward the use of multidisciplinary healthcare teams, the critical need for data interoperability, and the move toward integration of standard terminology in EHRs.

 Two seminal studies about use of the Omaha System in acute care are described in detail below. First, Bowles’ study mapped acute care and discharge planning patient Problems and nurse interventions from narrative notes to Omaha System terms.

Second, Zhang and colleagues mapped common nursing actions in a medical-surgical setting to the Omaha System to form the conceptual basis for a digital time and motion study.

Additionally, there is a recent report that the Alberta Health Services—a large, provincewide health system in western Canada&mdash;is integrating the Omaha System into all care settings including acute care.

These 2 seminal studies of the Omaha System in acute care settings emphasized the importance of using structured and standardized data to understand nursing care for hospitalized patients.

Patients in Bowles’ study had the following diagnoses: angina, percutaneous transluminal coronary angioplasty, myocardial infarction, congestive heart failure, valve replacement, and/or coronary artery bypass graft.

These patients averaged 8 Problems that required nursing attention. Only the most frequent Problems were reported: Circulation, Mental health, Medication regimen, Pain, Neuro-musculo-skeletal function, and Respiration. Bowles included “discharge planning” as an additional Problem-level concept.

 Patients in Zhang’s study were adult surgical patients on a medical-surgical unit of a major university hospital. The most frequent Problems in the observational study were Bowel care, Healthcare supervision, Medication regimen, Personal care, Physical activity, and Skin.

Variation between Problems found in these 2 studies likely reflect the needs of patients in surgical units versus cardiac units. Both studies noted the use of all Omaha System Categories (ie, Teaching, guidance, and counseling; Treatments and procedures; Case management; and Surveillance); as well as numerous Targets.

These studies demonstrate the usefulness of the Omaha System to describe problems and interventions of various populations, including patients in acute care settings.

 However, no studies to date have systematically evaluated the applicability of the Omaha System in medical-surgical and intensive care unit (ICU) settings.

In the era of accountable care, healthcare systems must work together to reach the national mandates of improved health outcomes, prevention of hospital readmissions, improved patient safety and reduced medical errors, and promotion of wellness and health. It is therefore critical to understand and communicate the work of acute care nurses using standards that are interoperable across settings and professions. The purpose of this study is to evaluate the applicability of Omaha System terms for acute care nursing interventions. The objective was to use the Omaha System to specify discrete observable interventions of medical-surgical and ICU nurses in a large urban, faith-based hospital. This practice-partner study was a precursor to a prepost EHR-implementation comparative time-motion study of the impact of a new comprehensive EHR on acute nursing care.

A classification procedure for narrative content was developed based on methods used in previous studies.

 Researchers interviewed 7 registered nurse content experts from the 3 study units in two 1-hour meetings (5 from medical-surgical and 2 from ICU settings) to elucidate discrete nursing activities in 3 acute, adult inpatient settings: 2 medical-surgical units and an ICU.

A convenience sample of registered nurse content experts was recruited by the managers of the selected units based on availability the day of the scheduled interviews. Each participant received a copy of the approved study Institutional Review Board proposal prior to the interview sessions. Participating units were selected by the organization’s chief nursing executive, in collaboration with the nursing leadership team, based on expert knowledge of the units as generally representative of medical-surgical and intensive care.

The structured interviews consisted of 7 questions on the nature of everyday nursing work. The participants were asked to describe important behaviors or activities in their work day. The interview questions were:

	• Describe a typical day: What kinds of things do you do?

	• What are the most important things you do that make a difference for your patients?

	• What are the most important things you do that make a difference to the organization?

	• What nursing acts might be missed by an observer?

	• What are the most important patient needs that might be missed by an observer?

	• What are components of multitask actions?

	• What do you want to be sure we can capture?

The interviews were conducted in two 90-minutes sessions: one with the medical-surgical nurses and the other with the ICU nurses. One researcher was physically present with the nurses and had the primary role of guiding the interview; 2 researchers participated via conference call and were active participants in the conversation, asking clarifying questions and responding to specific participant questions about study purpose and methods. All researchers captured typed transcripts of interview notes.

Notes from the interviews were reviewed and classified according to definitions of Omaha System terms by an Omaha System expert (KM) and reviewed by the research team. Additionally, participants circled or highlighted terms considered relevant to their acute care settings in a comprehensive appendix of defined Omaha System Intervention Scheme Problem, Category, and Target terms.

Two researchers also circled terms that were considered relevant based on their participation in the interview discussion.

Validation of Interview Content Classification

Researchers combined into a spreadsheet the Omaha System terms identified from nurse interviews, included in the instrument developed by Zhang et al, and those reported in Bowles’ study.

 The research team then reviewed the spreadsheet, the interview notes, and all source documents in their entirety. Finally, the team discussed differences and reached consensus on the final mapping.

A brief interview summary is provided as context for the applicability analysis results (

). Selected narrative phrases related to this description are also reported in 

. Nurses from both the medical-surgical and ICU settings described beginning their days with multifaceted information gathering processes, which included a universal emphasis on patient assessment, review of medical orders, and rapid prioritization. Nurses related the importance of reviewing, creating, and sharing the plan of care. Both the plan for the day and the ongoing plan of care were emphasized as important for patient understanding of their treatment and satisfaction with care. Multiple comments described presence and listening as important for rapidly building relationships and establishing trust with the patient and family. Nurses described their alignment with the organization’s mission and core values. They recognized patients and their families as at the center of their care and clearly linked the care experience to patient satisfaction, providing personal insights about the importance of values-driven care.

Interprofessional team-focused care, patient education, and preparation for discharge were threaded throughout responses from both the medical-surgical and ICU nurses. The reliance on team members within their units and the richness of the full interprofessional team’s contribution to the care process and the importance of communication among team members were mentioned multiple times. Patient and family education and anticipatory guidance were described as among the most important things that make a difference for patients.

An applicability analysis of the Omaha System terms in acute care showed that of 121 defined Omaha System Problem, Category, and Target terms, the majority were applicable in acute care in medical-surgical and ICU settings. Forty of 42 Problem terms were applicable (95.2%; Table 1). The most frequent Problems were “Consciousness,” “Circulation,” “Skin,” and “Bowel function.” All Category terms were applicable (100%; Table 2). Most Target terms were applicable (69.3%; Table 3). The most frequent Target terms across sources were “medication administration,” “communication,” “continuity of care,” “dressing change/wound care,” “durable medical equipment,” “environment,” and “signs/symptoms—physical.” Phrases that may represent additional concepts are reported in Table 4. There were 2 Problems and 27 Targets not named in any source (see 

Acute care nurse interviews and the literature described complex nursing interventions provided in medical-surgical and ICU settings. These concepts mapped to Omaha System terms including all but 2 Problem terms, all Category terms, and the majority of Target terms. Phrases that may represent additional concepts were identified. Further research is needed to define and validate these phrases before terminology refinement recommendations can be formulated.

In this study, 95.2% of Omaha System Problem terms were found to be applicable to acute care in medical-surgical and ICU settings. The exceptions were Pregnancy and Postpartum Problems, which would be relevant in a mother—baby or other obstetric unit. Thus, 100% of Omaha System Problems terms would be applicable in acute care settings. It is critical to maintain the ontological structure of the Omaha System to ensure interoperability. Bowles’ study recommended adding “discharge planning” to the Problem list. The research team in this study mapped “discharge planning” to the Healthcare supervision Problem, defined as “Management of the healthcare treatment plan by healthcare providers.”

 Given the emphasis on discharge planning and the national agenda for prevention of hospital readmissions, the term “discharge planning” should be evaluated as a possible Target term in future Omaha System revisions.

This study confirms that 100% of Category terms are applicable in acute care settings, aligning with findings in previous studies.

 This finding may suggest that it is possible to generalize healthcare communication and actions across settings and aligns with the notion of the importance of standards that can enable such communication, including the interoperability and exchange of data.

Finally, this study identified 69.3% of Target terms as applicable in acute care. The fact that there were 27 Targets not named in any source may reflect the broad range of topics addressed in community care settings that may not be relevant in acute care. Some of these Targets, such as “respiratory therapy care,” would be applicable in acute care, but may not have been named by nurses as a nursing-specific intervention. Others, such as “cast care,” may be applicable in specialized units such as orthopedics, and less common in medical-surgical or ICU settings. Further research is needed to evaluate use of these unnamed Targets in acute care.

The percentage of Omaha System terms that are applicable in acute care is a meaningful finding because the Omaha System is a comprehensive, holistic, and finite ontology that describes, classifies, and relates concepts for all of health and healthcare. Its robust information structure is an ecological model that enables theoretical analysis and data capture; in other words, it is a model to conceptually link healthcare information across disciplines and settings. The applicability of acute care nursing interventions within this ontology demonstrates that it is feasible for acute care nurses to meaningfully communicate important healthcare information and to generate useful, interoperable data. This is consistent with Bowles’ discharge planning study and the use of the Omaha System in CCDs.

 Further research is needed to evaluate the acceptability and usefulness of the Omaha System for documentation in acute care EHRs.

Nurse interviews included phrases that did not precisely map to existing Omaha System terms. In general, the majority of these phrases described aspects of nursing actions related to the Intervention Scheme Category terms. For example, “Be present,” “Share compassion,” “Look at the patient holistically,” “Patient satisfaction,” and “Rapport” described aspects of a therapeutic, relationship-based approach to care (Teaching, guidance, and counseling); “Critical thinking—prioritizing and reprioritizing” related to vigilance in monitoring the patient situation (Surveillance); and “Be the advocate” and “Rapid response” related to aspects of managing the patient’s plan of care (Case management). Whether or not to include terms for such approaches to care within nursing documentation is an important question, because researchers examine nursing data to evaluate how nurses make a difference. These approaches to care may describe aspects of the essence of nursing and may apply to any setting in which nursing is practiced. Some may be intangible qualities that are difficult or impossible to describe and quantify, but unless they are documented, it will not be possible to use standardized nursing data sets to evaluate these concepts as contributors to patient outcomes.

Formal adoption of nursing phrases as Problem, Category, or Target terms would require additional review during a future Omaha System revision.

 However, given that the Care description level of the Intervention Scheme is customizable, it could be used to  easily and immediately incorporate such phases within nursing documentation using the Omaha System.

The implications of using the Omaha System to represent acute care nursing are considerable, especially for interoperability and exchange of data across settings. The Omaha System has the potential to be used in any EHR for clinical documentation and to disseminate evidence-based clinical decision support. The study should be repeated in other acute care medical-surgical and ICU settings and should be extended to additional acute care areas, including behavioral health,perinatal care, and pediatric care. Implications for terminology development include evaluating phrases identified from nurse interviews to be considered included in a future revision of the Omaha System.

This study evaluated the applicability of a multidisciplinary interface standard—the Omaha System&mdash;for acute care nursing. Omaha System terms were broadly applicable in acute care nursing and therefore may be of value for measuring patient outcomes and improving healthcare quality before, during, and after hospitalization. Further research is needed to determine if additional phrases identified during the study should be incorporated in a future version of the Omaha System and to evaluate the acceptability and usefulness of the Omaha System in acute care EHRs. Use of the Omaha System as a multidisciplinary terminology may bridge acute and community care settings and serve as a strategy for improving healthcare information interoperability and exchange in the era of accountable care.

University of Minnesota (KAM), Minneapolis, MN; Washington State University College of Nursing (MS), Spokane, WA; Providence Health & Services (RS), Portland, OR; Providence St. Patrick Hospital (ES), Missoula, MT.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (KAM, MS, ES, RS); acquisition of data (RS); analysis and interpretation of data (KAM, ES, RS); drafting of the manuscript (KAM, MS, RS); critical revision of the manuscript for important intellectual content (MS, ES); statistical analysis (KAM, ES); administrative, technical, or logistic support (RS).

Karen A. Monsen, PhD, RN, FAAN, University of Minnesota School of Nursing, 5-150 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455. E-mail: 

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Complex interventions from hospital settings mapped to Omaha System terms commonly used in community care; demonstrating its potential as a tool for interoperability across settings.

Applicability of the Omaha System in Acute Care Nursing for Information Interoperability in the Era of Accountable Care

A few years ago, Gene Lindsey, president of Atrius Health, told the 

, “An ACO is like a unicorn; everyone thinks they know what one is, but no one has ever seen one.” Although the definition of an accountable care organization (ACO) can vary, and some healthcare systems probably use the term quite liberally, it appears we now have vast herds of unicorns roaming the country. Meanwhile, we have seen a surge in tech startups and new business lines from traditional health vendors offering care coordination tools to help ACOs deliver better care. Given the swell of ACOs and the surge in new technologies designed to support them, we should pause to ask ourselves: What are the features we truly need in care coordination tools to deliver on the promise of the Triple Aim? In this article, we will start with a quick overview of this growing market segment and then look at 5 essential features that care coordination tools must have to be helpful to care coordinators and their patients.

First, it is important to briefly share background on the authors, as we are approaching this topic based on our own experience and to settle on a working definition of an ACO. We have collectively served as a registered nurse with oversight of the clinical components of an ACO-like arrangement (the Intensive Outpatient Care Program started by Boeing in St. Louis to better manage the care of their chronically ill employees), an overseer of both a patient-centered medical home with UnitedHealthcare in Arizona and ACO implementations across the state of California with Blue Shield of California, and a former health plan executive with strategy and implementation oversight for the information and technology enablement of ACOs across California.

For the purposes of this article, we will define an ACO as a “care coordination model designed to improve quality of care, increase patient satisfaction, and lower the cost of care by leveraging and connecting the relationships of hospitals, medical groups, and health plans to work together to decrease fragmented care.” Interestingly, for a care system focused on patient experience, patients are rarely aware that they are part of an ACO and the connectivity between the 3 entities is often invisible to them.

Care Coordination Is Key for Many Reasons

From our combined experience, we both know how critically important it is to have good care coordination in any healthcare system or arrangement, including ACOs. Care coordination helps providers to form a complete picture of a patient’s overall heath and it also allows them to be able to better communicate with the patient, their family, and with each other. Care coordination also requires constant prioritization and re-prioritization of patients for effective panel management; it means applying art and science to split attention between patients with immediate needs and those ripe for preventive measures or patients we regard as healthy working adults. In these arrangements, it is essential to have a lead care coordinator. An MD can play this role, but given our own experience and backgrounds, this article will focus on what nurses need from care coordination tools.

The Market for Care Coordination Tools Is Exploding

The marketplace for care coordination tools is growing at an exponential rate: in 2014, venture funding for digital health companies surpassed $4.1 billion—nearly the total of all 3 prior years combined.

A recent online poll from KPMG indicates that a growing number of healthcare leaders think investment in population health management tools will pay off in a big way.

Additionally, the health plans are investing big and earning dividends too—Optum Health recently saw its revenue jump 33%, compared with the prior year’s second quarter.

The wearables market is growing exponentially as well, with sales of mobile wearable devices, including health and fitness devices and smart glasses, expected to reach almost 70 million items by 2017.

Wearables, from fitness trackers to medical-grade devices, are helping to give patients and providers feedback and are thereby playing a growing role in improving care coordination.

Interestingly enough, with all this growth and investment, we also believe we are seeing a sort of “bifurcation” in the market. Some vendors are being very intentional about developing highly specialized tools or services for niche populations, such as an app for teens with diabetes, while others are pursuing a big picture approach, offering comprehensive out-of-the-box tools for complete care coordination.

What Do Care Coordinators Need From the Tools?

With this surge in investment in care coordination tools, what do nurses need in their care coordination toolbox to ensure they can accomplish real care coordination? We suggest focusing on 5 features:

1. Care coordination tools should be tailored to your patient population.

With so many new tools on the market, it is critical to choose ones that match your patient population. Patients’ preferences for how they receive messages about their health (and the content of those messages) is greatly impacted by their lifestyle, age, and socioeconomic status. There is no 1-size-fits-all solution. For example, tools that offer e-mail reminders may be less effective with a Medicaid population compared with those that communicate via text.

Tools need to provide the right mode of delivery (eg, e-mail, phone call, text), as well as the right message, tailored to the audience at the right time. In our experience, because some chronically ill patients in the commercial population don’t necessarily like to think of themselves as “sick” or singled out for intervention, finding tools that encourage use and participation in a more social, “fun” manner may increase buy-in and utilization. Conversely, the more senior population may generally be more receptive to acknowledging their conditions and prefer to focus on communications customized to their specific needs.

2. Care coordination tools should have a single place the care coordinator can visit

 to get the full picture at the panel and patient levels.

Across the country, we have seen some great examples of integrated delivery networks successfully using health information exchange technology to pull together complete records of a patient’s care. The giant organizations like Kaiser and the Veterans Health Administration have figured out these systems quite well. Several states and regional entities are attempting to do the same, but broad adoption is yet to be seen. For the care coordinator in a disjointed system, this can pose a problem. Consider the example of a patient in a preferred provider organization with a chronic heart problem who sees a cardiologist in a different system than his or her primary care physician; it may be very difficult for the care coordinator to get a complete medical history. Although ACOs can start to bridge that gap, the technology and health information exchange needs to keep up. Ensuring the data is easy to locate and does not require multiple log-ons to access will be a critical component of any system seeking to aggregate data. It remains a real challenge—one that care coordination tools should start to address.

3. Care coordination tools must allow for convenient use of clinical pathways and

Several care coordination tools offer clinical decision support in the form of “decision trees” for providers and care coordinators. As an example, the proposed tool will recommend a set series of steps for the patient diagnosed with diabetes. However, we have also seen tools that are, in some ways, too rigid, where for example, the care coordinator does not have enough flexibility to decide which path to choose first among patients with multiple chronic conditions and related psychosocial issues. In our experience, when the nurse and patient can work together to set mutual goals, health behaviors can be changed; ultimately, the nurse needs to be able to use his or her judgment and work with the patient to negotiate a care path that the patient will adhere to. If these clinical pathway tools force the care coordinator to choose a single, very specific path, there may be patient attrition, as some patients might just give up. For some patients, a recommended 30 minutes a day of exercise is just too daunting, for example. This is the part of healthcare that is both an art and a science: care coordinators need flexibility—that artistic license&mdash;to negotiate care paths that will be doable for the patient. The tool can help by giving some general scientific guidance.

4. Care coordination tools need to have strong communication features among providers to facilitate care hand-offs and to involve family/caregivers when appropriate.

Within an ACO, a patient may have interaction with a care coordinator, a primary care doctor, a specialist, a case manager, a pharmacist, a dietician, a social worker, a hospitalist, and a behavioral health clinician. That can mean a lot of hand-offs and a lot of potential for medical errors if the right information is not relayed. Good care coordination tools will help the care team to focus on key events, such as the results of lab tests or a hospital discharge; moreover, they won’t overburden the care coordinator with too many flags or “alerts,” as this can lead to “alert fatigue.” A good tool will also show when a hand-off has been successfully completed so the communication loop can be closed. Family/caregivers need to be included in communication activity upon transitions and hand-offs, particularly in the post discharge scenario, and/or with patients who are challenged in complying with their care plan.

5. Care coordination tools should integrate with other systems—or at least be straightforward about their ability to do so.

So many times in our careers we have had the same conversation with vendors: we ask if the care coordination tool integrates with [fill-in-the-blank]. Often, the short answer is “yes,” but usually there is a much longer conversation needed. In an ideal world, all technology would seamlessly integrate: for example, that care coordination app that delivers mobile health reminders would seamlessly integrate with the electronic medical record (EMR) system. When a tool vendor promises integration, we have learned there are 3 important follow-up questions to ask: first, “Have you done this integration before?” then “With whom?” and “How?” It is also important to assess if the other system (eg, the EMR company in our example) wants integration. It is easy to say you will attempt integration with another system that you know will never integrate with you.

Across the board, we are pleased with the growing national movement toward greater care coordination, as more health systems realize there are better ways to deliver care. New and traditional technology vendors are riding this wave and there are many exciting new tools to choose from. We hope we have provided some basic guidance to care coordinators based on our own experience, though we know there is much still to learn.

This article examines the features a care coordinator should look for in care coordination tools to ensure they meet the needs of patients, the care team, and the care coordinator.

September 2015

Transitioning Our Healthcare System Toward Accountable Care

Applying Weighting Methodologies to a Commercial Database to Project US Census Demographic Data

Applicability of the Omaha System in Acute Care Nursing for Information Interoperability in the Era of Accountable Care

ACOs: What Every Care Coordinator Needs in Their Tool Box

Optimizing the Effect of Electronic Health Records for Healthcare Professionals and Consumers

A Physician-led Accountable Care Organization: From Award to Implementation

Reimagining Health and Care to Foster, Not Force, Accountability