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ORIGINAL RESEARCH

Pay-for-performance policy and data-driven decision making within nursing homes: a qualitative study Kathleen Abrahamson,1 Edward Miech,2 Heather Wood Davila,3 Christine Mueller,3 Valerie Cooke,4 Greg Arling1

1

School of Nursing, Purdue University, West Lafayette, Indiana, USA 2 Indiana University School of Medicine, Indianapolis, Indiana, USA 3 School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA 4 Minnesota Department of Human Services, Minneapolis, Minnesota, USA Correspondence to Dr Kathleen Abrahamson, School of Nursing, Purdue University, 502 N. University Street, West Lafayette, Indiana 47907, USA; [email protected] Received 2 July 2014 Revised 16 February 2015 Accepted 19 February 2015 Published Online First 6 March 2015

To cite: Abrahamson K, Miech E, Davila HW, et al. BMJ Qual Saf 2015;24: 311–317.

ABSTRACT Introduction Health systems globally and within the USA have introduced nursing home pay-forperformance (P4P) programmes in response to the need for improved nursing home quality. Central to the challenge of administering effective P4P is the availability of accurate, timely and clinically appropriate data for decision making. We aimed to explore ways in which data were collected, thought about and used as a result of participation in a P4P programme. Methods Semistructured interviews were conducted with 232 nursing home employees from within 70 nursing homes that participated in P4P-sponsored quality improvement (QI) projects. Interview data were analysed to identify themes surrounding collecting, thinking about and using data for QI decision making. Results The term ‘data’ appeared 247 times in the interviews, and over 92% of these instances (228/247) were spontaneous references by nursing home staff. Overall, 34% of respondents (79/232) referred directly to ‘data’ in their interviews. Nursing home leadership more frequently discussed data use than direct care staff. Emergent themes included using data to identify a QI problem, gathering data in new ways at the local level, and measuring outcomes in response to P4P participation. Alterations in data use as a result of policy change were theoretically consistent with the revised version of the Promoting Action on Research Implementation in Health Services framework, which posits that successful implementation is a function of evidence, context and facilitation. Conclusions Providing a reimbursement context that facilitates the collection and use of reliable local evidence may be an important consideration to others contemplating the adaptation of P4P policies.

INTRODUCTION Nursing home quality is a persistent healthcare concern. The gap between

evidence and practice remains particularly wide within many long-term care settings, and implementation of innovative quality improvement (QI) practices within nursing homes is challenging.1 2 Nursing homes are traditionally hierarchical organisations, where staff have distinct role boundaries that are often unyielding and impervious to change. Nursing homes struggle to maintain minimum staffing levels, and direct-care staff have relatively little formal education.3 Staff turnover is high, complicating training and consistent evaluation of implementation processes, and a long history of a punitive regulatory culture has limited opportunities for nursing home providers to take organisational risks and innovate in the area of quality.4 Measuring and defining nursing home quality presents unique challenges, given the intersection between medical and social care found in most nursing facilities.5 Although evidence is available to support strategies to improve nursing home care, QI implementation remains a significant nursing home challenge.1 In response to the need for improved nursing home quality, health systems globally and within the USA have introduced nursing home pay-for-performance (P4P) programmes. P4P programmes seek to enhance healthcare value by paying for care services based upon quality outcomes.6 Evidence on the effectiveness of nursing home P4P programmes is mixed.7–9 Programmes vary considerably, and the ability of P4P to cost-effectively improve quality is limited by the complexity of accurately selecting and measuring quality outcomes, implementing programmes in a manner that accounts for existing system inequalities, decreasing unintended effects such as decreased

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Original research attention to aspects of care not attached to financial incentive and provision of a financial incentive that is proportionate to the effort undertaken to improve care quality.6 7 10 11 Central to the challenge of administering effective P4P is the availability of accurate, timely and clinically appropriate data for decision making.10 As part of a larger evaluation of nursing home P4P policy in the state of Minnesota, we closely examined the process and experience of implementing QI within the nursing home context. We found quantitative evidence that clinical quality had been improved in relation to participation in the P4P initiative, and qualitative evidence that capacity for QI increased among participating nursing home staff members.12–14 Further examination of emergent qualitative themes allowed us to explore the implementation processes that surrounded P4P success. Notable was the frequency with which nursing home staff members described a changed relationship with ‘data’ as part of their QI journey. Two research questions, highlighted in this manuscript, emerged from the qualitative interview data: 1. How did the definition of data and ways in which data was collected, thought about and used change as a result of participation in the P4P programme? 2. How did staff perception of data and data use vary by organisational role within the nursing home?

BACKGROUND The Minnesota Performance-based Incentive Payment Program

The state of Minnesota established the Performancebased Incentive Payment Program (PIPP) in 2006. Through PIPP, nursing homes receive financial incentive to develop, implement and evaluate a QI project based upon the local needs and context of their nursing facility. The Minnesota Department of Human Services (DHS) requests that nursing homes create a QI project, develop an implementation plan and carry out the project over 1–3 years. Projects go through a competitive review process and, for approved projects, nursing home providers negotiate the measures on which their outcomes will be evaluated with DHS programme administrators. The PIPP has a unique QI strategy that differs from a traditional regulatory approach that is often adversarial and focused on avoidance of poor care rather than promotion of good quality care, and a traditional P4P approach that ties financial incentives to better performance on quality metrics but does little directly to build provider capabilities for QI. The PIPP combines financial incentives for better care with an emphasis on QI skill development and capacity building. The design of PIPP is consistent with implementation frameworks such as the Promoting Action on Research Implementation in Health Services (PARIHS) framework, which highlights the importance of acknowledging local context and facilitating change through 312

support for projects developed at the grass-roots, point of care level.15 16 Nursing homes participating in PIPP receive a negotiated per diem increase in Medicaid reimbursement of up to 5% of the base operating rate during the implementation of their QI project. If negotiated outcomes from the QI project are not met, the nursing home is required to return a portion (up to 20%) of the incentive funds over time. Between 2007 and 2012 the PIPP funded 127 QI projects involving 60% (n=222) of Minnesota nursing facilities. Projects are developed by nursing home leadership and based upon facility needs and resources, and funded PIPP projects addressed a range of quality domains. The majority of projects have targeted clinical quality concerns such as falls and pain. Other projects were aimed at psychosocial care, care transitions, personcentered care, and use of technology.17 Because projects addressed a wide range of areas based on diverse facility needs, selected performance measures linked to financial incentive varied as well. Importantly, improvements have been demonstrated through implementation of a variety of PIPP projects within a wide range of nursing home contexts.12 Generation and use of credible and clinically appropriate information for decision making has been demonstrated to promote change within healthcare organisations.18 Development of accurate and nimble systems for staff to access data-based feedback and monitor change between departments and disciplines is a potential barrier to QI implementation within nursing homes, and is supportive of evidence-based practice decisions.19 Data gathered at the local level may have greater impact on nursing home staff and administration than quality reporting data gathered through more formal mechanisms,18 and federal and state data may be reported too infrequently or nonspecifically to meet the daily decision-making needs of staff. In order to monitor their own progress towards goals and make mid-project adjustments, PIPP-funded nursing home providers were encouraged to develop methods of tracking data at the facility level in ways that went beyond federally mandated data collection. We conducted an in-depth qualitative analysis to explore the potential relationship between the PIPP design and staff perception of changes in their use of data; the possible influence of PIPP participation on data-driven decision making; and how this influence may differ by organisational role. The results of that analysis are described in this manuscript. METHODS Design and sample

An emergent qualitative approach was used to explore the experiences of nursing home staff members. In-depth semistructured interviews were conducted with 232 nursing home employees of 70 nursing homes participating in 13 PIPP-funded QI projects. Abrahamson K, et al. BMJ Qual Saf 2015;24:311–317. doi:10.1136/bmjqs-2014-003362

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Original research PIPP funds single facility and multifacility projects, so some projects included multiple facilities. For each of the 13 selected projects, a site visit was carried out at one to three of a project’s participating facilities. During each site visit, 5–10 staff interviews were completed. If a project involved more than three facilities, telephone interviews were done with the project leader from each of the remaining facilities. One hundred and seventy-eight of the interviews were obtained during day-long facility site visits; 54 interviews were conducted over the telephone. In an intentional sampling framework, we sought to gather the perspectives of nursing home employees from a wide variety of organisational roles. The interviewees included project leaders, directors of nursing, nursing assistants, activity department personnel, quality specialists, therapists, exercise technicians, staff nurses and administrators. Data collection

The interview was semistructured with open-ended responses to probes. Interview questions addressed issues such a respondent’s role in project development and implementation, challenges and facilitators to implementation, perceived project successes, workgroup relationships, advice seeking and mentorship, interdisciplinary input, changes to the QI process and mechanisms of communication. The average interview length was 30 min. Interviews were recorded with respondent permission, and Institutional Review Board (IRB) approval was obtained prior to the initiation of data collection. The audiotaped interviews were professionally transcribed and then de-identified prior to data analysis to remove any information identifying specific respondents, facilities or organisations.

student) analysts simultaneously examined a portion of the interview data to create preliminary coding categories. Text was coded at the micro level, allowing for codes to emerge from short statements or phases when they comprised a complete unit of thought. Lists of emergent codes were then compared among researchers and discussed to reduce discrepancies within the coding scheme. Each investigator then recoded a portion of the data based on the agreedupon coding categories and discussed the findings to maximise intercoder reliability. Investigators then independently coded a selected sample of interview text data and coding categories were further discussed and refined. Once a consensus of coding taxonomy was established, investigators then coded the text independently with the understanding that codes may require additional discussion and amendment based upon interviewee responses. Individual text fragments were coded into multiple categories when the interviewee response indicated multiple emergent themes. Interview questions addressed broad themes related to organisational change and did not specifically focus on changes in the use of data or evidence as a result of PIPP participation. Initial, broad coding categories included topics such as implementation challenges, facilitators, communication patterns, leadership, project-related changes in the QI process and impact on care quality. Through open, inductive coding the research team realised that respondents perceived that PIPP had influenced their practice in terms of datadriven decision making. Changes in the QI process and use of evidence, data and internal reports were emergent subthemes from within the broader analysis. These themes were then explored further in response to the above-stated research questions.

Qualitative analysis

Interview data were analysed using a thematic narrative analysis framework and inductive category development.20 21 Categories or themes were derived from the data during analysis. The NVivo software program, developed by QSR International, was used to manage data analysis. Categories or themes were derived empirically from the data during analysis. Initially, three (two co-investigators and a doctoral Table 1

RESULTS The term ‘data’ appeared 247 times in the interviews, and over 92% of these instances (228/247) were spontaneous references by nursing home staff. Overall, 34% of respondents (79/232) referred directly to ‘data’ in their interviews. There was considerable variation in these numbers by respondent role/position, as displayed below in table 1.

Variation by position in direct references to ‘data’

Project leader Director of nursing Administrator Staff nurse, rehab or social worker CNA CNA, Certified Nursing

Number of respondents

Number of individuals making direct references to ‘Data’

Percentage of individuals making direct references to ‘Data’ (%)

Total number of direct references to ‘Data’

86 16 15 82

51 8 6 14

59.3 50 40 17

152 21 22 33

0

0

33 Assistant.

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Original research Themes related to data use were identified 32 times within the 15 interviews conducted with nursing home administrators, 29 times within the 16 interviews conducted with directors of nursing and 179 times within the 86 interviews conducted with project leaders. Conversely, data use related themes emerged only 9 times within the 33 interviews conducted with nursing assistants. The two most frequent data related themes were the use of data to identify the area of quality need for project development (99 coded instances) and the selection and monitoring of outcome measures (94 coded instances). The frequency of emergent data-related theme by interview respondent role is presented in table 2. Reanalysis of interview responses specific to data use provided deeper insight into the influence of QI implementation within the context of P4P on data use within PIPP participating nursing homes. The following insights related to data-driven decision making emerged.

Data and identifying a QI problem

Nursing home staff used multiple complementary data sources ( publicly available federal and statespecific quality indicators as well as locally gathered nursing home data) to identify a quality problem on which to focus. The importance of using data to identify the targeted area of QI focus surfaced in almost half (48%) of interviews with administrators or directors of nursing. Nursing home leadership noted that the potential to obtain PIPP funding facilitated the development of new ways to gather different types of data at the facility level, and thus facilitated data collection and analysis. Additionally, respondents spoke of using quality data (eg, fall rates, pain measures) to communicate with stakeholders, such as physicians and family members, regarding the importance of QI.

Data gathering at the local level

Because state quality indicators and resident quality of life data are reported infrequently (quarterly or annually), respondents developed new ways to track progress towards outcome goals at the local nursing home level, such as tracking trends based upon time of day and unit, collecting detailed data to examine the root cause of events, and more closely documenting level of resident satisfaction with care processes. This was a change in focus and at times a challenge for many nursing home employees, who were now spending significant effort collecting and analysing data because of concern over meeting performance policy outcome goals and the need to monitor progress. Managers spend a great deal of time doing follow-up and doing data collection…finding out what the

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strengths are or the areas of needed improvement continue to be… Administrator and Director of Education #1 I have tons of data that I collect and report on from our project, like this is every month……...just lots of data. Project Leader #1 We have more data than we used to because we’re keeping track… so we have more data to work with Project Leader #2

Data were used to motivate staff towards increased participation and continued progress towards performance goals. When project performance outcomes were on track, nursing home leaders noted the effectiveness of communicating these objective, positive trends with care staff in order to motivate them towards continued success. Conversely, data were helpful to counsel staff members who were performing poorly. Respondents noted that looking closely at trends in the data and communicating them with staff encouraged buy-in and facilitated project success at the nursing home level. Because the outcomes are motivating them to continue Director of Nursing #1 We’ve been able to see the changes because of the documentation that we do Case Manager #1 Measuring outcomes in response to policy requirements

PIPP facilities were required to report data that corresponded with the negotiated outcomes linked to the financial incentives to the DHS. Respondents noted the importance of giving in-depth attention to the selection of outcome measures. For example, a project aimed at the reduction in pain noted the challenges of using a pain measure that suffered from ceiling effects and did not clearly demonstrate what staff perceived to be notable decreases in chronic pain among longterm residents. Additionally, project leaders at times reflected that it was difficult to predict which outcome measure would best reflect the changes they perceived were happening as a result of their efforts. For example, some projects resulted in an unpredicted change in areas such as resident behaviour, and required nursing home leadership to alter their data gathering focus to reflect clinical observations. Respondent discussion surrounding outcome measures emerged 80 times within the 86 interviews conducted with PIPP project leaders. Moreover, 27% of the project leaders that were interviewed discussed changes in their facility QI processes related to use and knowledge of quality indicators since participating in the PIPP process. Abrahamson K, et al. BMJ Qual Saf 2015;24:311–317. doi:10.1136/bmjqs-2014-003362

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Original research Table 2

Frequency of emergent data-related theme by interview respondent role

Data related theme

Administrator interviews (n=15)

Project based upon need 9 identified in the data Themes addressing the selection 5 and measurement of outcomes Positive results evident through 6 examination of data Data reports generated as a result 6 of PIPP Increased collection of data 4 Pressure to achieve outcome 2 numbers Total emergent responses by role 32 PIPP, Performance-based Incentive Payment Program.

Director of nursing interviews (n=16)

Project leader interviews (n=86)

Other interviews (Staff nurse, rehab or social worker) (n=82)

Nursing assistant interviews (n=33)

Total emergent responses by theme

12

33

38

7

99

5

80

4

0

94

5

27

19

2

59

1

19

1

0

27

3 3

13 7

5 4

0 0

25 16

29

179

71

9

Respondents spoke of pride in achieving improvement in their outcome measures and wanting to succeed to maintain the positive reputation of their facility. Positive outcomes, such as reduced hospital readmission rates, were also used to strengthen relationships with potential referral sources, such as area hospitals. The salience of outcome measure selection encouraged providers to identify new sources of data and use existing sources of data in new ways. Providers advised others to think about data collection and outcomes measurement early in project planning, and to be open to a variety of data sources for evaluation: Ensuring how that project is going to be measured would be my first step. Find something different…see if there is a different way that (the project focus) can be measured. Director of Nursing #2

An additional challenge for PIPP participants was finding data sources to measure the often subtle, but important changes that were taking place within their facilities. Multidimensional domains such as quality of life are difficult to measure,13 and nursing home leaders expressed concern that what they perceived to be improvements were not reflected in the outcomes they reported to DHS. Staff reported feeling that resident quality of life and staff satisfaction were improved by some projects in ways that were difficult to measure using available quantitative data: …one to one interactions with the resident showed some small improvements….So, it was hard to be measured on just the (minimum data set; MDS) data. Sometimes I wanted to provide some qualitative information instead of just quantitative. Project Leader #3 Abrahamson K, et al. BMJ Qual Saf 2015;24:311–317. doi:10.1136/bmjqs-2014-003362

Respondents noted a change towards thinking about the root cause of quality problems and application of research findings to the quality issues that they are facing within their nursing homes. There was a perception that the use of data to monitor quality was motivated by PIPP requirements. … (we track) our (facility) critical success factors, and we do it every month… we’ve actually added this because we started the PIPP Director of Nursing #3 …the importance of the data-gathering aspect of it, and again it all comes back to PIPP being the driving force behind us doing that Project Leader #4

DISCUSSION The purpose of this analysis was to understand the influence of a P4P policy on the gathering and use of data to drive QI decisions from the vantage point of nursing home personnel. P4P policies such as PIPP link payment to outcomes and, as such, the gathering of accurate QI data becomes an important policy component. In order to achieve PIPP funding, QI projects must have quantifiable quality measures that serve as targets for meeting project objectives. Although a federal system of nursing home quality measures is available, complications arise when linking such measures to provider payment. Nursing home quality indicators or other nursing home report card data (http:// www.medicare.gov) are a valuable resource for assessing facility performance; however they may not be reported frequently enough or in sufficient detail for QI purposes, and they may not target the outcomes of interest for a particular provider.10 22–24 Consistent with implementation frameworks such as PARIHS, which posits that successful implementation is a function of evidence, context and facilitation,25 26

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Original research the PIPP design purposefully facilitates the gathering and use of data within the local nursing home context. The PIPP funded QI project must address a facility-level quality weakness as evidenced by data, and use validated outcomes measures. Respondents perceived that this led them to identify, analyse, use and think about data in new ways that supported the successful implementation of local QI projects. Our respondents noted feeling challenged and at times frustrated by the difficult nature of measuring QI change and selecting measures that captured the outcomes most influenced by the intervention. Mandated regular reporting on quality outcomes reinforced attention to the ongoing or continuous nature of QI. Also consistent with the PARIHS framework,25 26 PIPP-funded QI efforts were facilitated by a state policy context that promoted and rewarded the use of multiple forms of data to make QI decisions. The Minnesota PIPP is unique in that it fosters and depends upon collaborative relationships between the state and nursing home providers to facilitate quality goals. Our respondents discussed the challenges of managing data without strong analytical experience. Throughout the application and implementation process, state programme leaders provided technical assistance and ongoing support to providers, who often needed new skills related to data interpretation, tracking and analysis in order to succeed. Providing a reimbursement context that facilitates the collection and use of reliable local evidence may be an important consideration to others contemplating the adaptation of P4P policies. QI efforts share common measurement challenges: defining quality criteria, establishing accurate data collection systems, and accounting for inherent differences between individuals and settings. When dollars are linked to quality measures, the risks and challenges of datadriven decision making may be amplified.10 11 Through the PIPP funding process, our respondents perceived that the state facilitated data-driven decision making through: providing a competitive application process that required providers to identify a QI problem that was driven by local data and develop a related evidence-based intervention; providing financial motivation to closely examine outcomes through the P4P per diem rate incentive; requiring frequent monitoring of validated outcome measures; and making technical support available when needed. The grass roots nature of QI project development acknowledged provider knowledge about local areas of quality weakness, availability of outcomes data, and organisational complexity while providing the state the ability to negotiate a measurement standard and hold providers financially accountable. We found that data and research evidence played an important role in QI project implementation. Nursing home staff discussed using data to identify problems, track progress, motivate employees and increase the 316

marketability of the organisation. The state policy encouraged nursing home staff to evaluate their progress continually through data gathered at the facility level. While a growing body of work has explored the PARIHS framework,16 26 few studies, if any, have considered its application to nursing home care. There are a number of limitations to this current research. First, the qualitative design of this study allowed us to examine the influence of PIPP from the vantage point of nursing home staff, but does not allow for findings that are clearly generalisable beyond our sampling context. Second, observational data to provide an objective assessment of data use in QI decision making among respondents is not available, which limits what can be known surrounding the data-use behaviour of PIPP participants. Despite the use of P4P programmes in a wide variety of settings and health systems, this study took place in a single State and only within nursing home settings. However, we contend that the perspective of nursing home staff generates important insight into the P4P experience.

CONCLUSION Our findings indicated that participants in PIPPfunded QI projects perceived a change in the rate and manner in which they gathered, used and considered data in their QI decisions, and that they often attributed that change to a state administered P4P programme. A number of studies have examined the effectiveness of P4P programmes.6 27 28 Additionally, there is research addressing the aggregate effects of state policies on overall nursing home quality.29–31 This current analysis contributes to the literature surrounding P4P, particularly within nursing home settings, by addressing the impact of state P4P policy on professional caregiver perspectives surrounding data and decision making at the facility level. Contributors Substantial contribution to the conception and design of this work: All authors. Acquisition, analysis, interpretation of data: KA, EM, HWD, CM, GA. Drafting and critical revision of manuscript: All authors. Approval of final version: All authors. Agreement to be accountable for all aspects of the work: All authors. Funding This project was supported by the Agency for Healthcare Research and Quality (grant number R18HS018464). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Competing interests None. Ethics approval University of Minnesota and Indiana University. Provenance and peer review Not commissioned; externally peer reviewed.

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Pay-for-performance policy and data-driven decision making within nursing homes: a qualitative study Kathleen Abrahamson, Edward Miech, Heather Wood Davila, Christine Mueller, Valerie Cooke and Greg Arling BMJ Qual Saf 2015 24: 311-317 originally published online March 6, 2015

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Pay-for-performance policy and data-driven decision making within nursing homes: a qualitative study.

Health systems globally and within the USA have introduced nursing home pay-for-performance (P4P) programmes in response to the need for improved nurs...
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