Acute viral bronchiolitis is the most common cause of hospitalization for children less than 1 year of age.¹ Overuse of ineffective therapies has persisted despite the existence of the evidence-based American Academy of Pediatrics (AAP) clinical practice guideline (CPG), which recommends primarily supportive care.^2-8 Adherence to the AAP CPG recommendations for management of bronchiolitis improved significantly through the AAP’s Bronchiolitis Quality Improvement Project (BQIP), a 12-month, multiinstitutional collaborative of community and free-standing children’s hospitals.⁹ This subsequent study investigates if these improvements were sustained after completion of the formal 12-month project.

Published multiinstitutional bronchiolitis quality improvement (QI) work is limited to 1 study⁵ that describes the results of a single intervention season at academic medical centers. Multiyear bronchiolitis QI projects are limited to single-center studies, and results have been mixed.^5,6,8,10-13 One study¹¹ observed continued improvement in bronchodilator use in subsequent seasons, whereas a second study¹⁰ observed a return to baseline bronchodilator use in the following season. Mittal⁶ observed inconsistent improvements in key bronchiolitis measures during postintervention seasons.

Our specific aim was to assess the sustainability of improvements in bronchiolitis management at participating institutions 1 year following completion of the AAP BQIP collaborative.⁹ Because no studies demonstrate the most effective way to support long-term improvement through a QI collaborative, we hypothesized that the initial collaborative activities, which were designed to build the capacity of local interdisciplinary teams while providing standardized evidence-based care pathways, would lead to performance in the subsequent season at levels similar to or better than those observed during the active phase of the collaborative, without additional project interventions.

Study Design and Setting

This was a follow-up study of the AAP Quality Improvement Innovation Networks project entitled “A Quality Collaborative for Improving Hospital Compliance with the AAP Bronchiolitis Guideline” (BQIP).⁹ The AAP Institutional Review Board approved this project.

Twenty-one multidisciplinary, hospital-based teams participated in the BQIP collaborative and provided monthly data during the January through March bronchiolitis season. Teams submitted 2013 baseline data and 2014 intervention data. Nine sites provided 2015 sustainability data following the completion of the collaborative.

Participants

Hospital encounters with a primary diagnosis of acute viral bronchiolitis were eligible for inclusion among patients from 1 month to 2 years of age. Encounters were excluded for prematurity (<35 weeks gestational age), congenital heart disease, bronchopulmonary dysplasia, genetic, congenital or neuromuscular abnormalities, and pediatric intensive-care admission.

Data Collection

Hospital characteristics were collected, including hospital type (academic, community), bed size, location (urban, rural), hospital distributions of race/ethnicity and public payer, cases of bronchiolitis per year, presence of an electronic medical record and a pediatric respiratory therapist, and self-rated QI knowledge of the multidisciplinary team (very knowledgeable, knowledgeable, and somewhat knowledgeable). A trained member at each site collected data through structured chart review in baseline, intervention, and sustainability bronchiolitis seasons for January, February, and March. Site members reviewed the first 20 charts per month that met the inclusion criteria or all charts if there were fewer than 20 eligible encounters. Sites input data about key quality measures into the AAP’s Quality Improvement Data Aggregator, a web-based data repository.

Intervention

The BQIP project was designed as a virtual collaborative consisting of monthly education webinars about QI methods and bronchiolitis management, opportunities for collaboration via teleconference and e-mail listserv, and individual site-coaching by e-mail or telephone.⁹ A change package was shared with sites that included examples of evidence-based pathways, ordersets, a respiratory scoring tool, communication tools for parents and referring physicians, and slide sets for individual site education efforts. Following completion of the collaborative, written resources remained available to participants, although virtual collaboration ceased and no additional project interventions to promote sustainability were introduced.

Bronchiolitis Process and Outcome Measures

Process measures following admission included the following: severity assessment using a respiratory score, respiratory score use to assess response to bronchodilators, bronchodilator use, bronchodilator doses, steroid doses per patient encounter, chest radiographs per encounter, and presence of an order to transition to intermittent pulse oximetry monitoring. Outcome measures included length of stay and readmissions within 72 hours.

Analysis

Changes among baseline-, intervention-, and sustainability-season data were assessed using generalized linear mixed-effects models with random effect for study sites. Negative binomial models were used for count variables to allow for overdispersion. Length of stay was log-transformed to achieve a normal distribution. We also analyzed each site individually to assess whether sustained improvements were the result of broad sustainability across all sites or whether they represented an aggregation of some sites that continued to improve while other sites actually worsened.

To address any bias introduced by the voluntary and incomplete participation of sites in the sustainability season, we planned a priori to conduct 3 additional analyses. First, we compared the characteristics of sites that did participate in the sustainability season with those that did not participate by using Chi-squared tests for differences in proportions and t tests for differences in means. Second, we determined whether the baseline-season process and outcome measures were different between sites that did and did not participate using descriptive statistics. Third, we assessed whether improvements between the baseline and intervention seasons were different between sites that did and did not participate using a linear mixed-effects model for normally distributed outcomes and generalized linear mixed-effects model with site-specific random effects for nonnormally distributed outcomes. All study outcomes were summarized in terms of model-adjusted means along with the corresponding 95% confidence intervals. All P values are 2-sided, and P < 0.05 was used to define statistical significance. Data analyses were conducted using SAS software (SAS Institute Inc., Cary, North Carolina) version 9.4.

To our knowledge, this is the first report of sustained improvements in care achieved through a multiinstitutional QI collaborative of community and academic hospitals focused on bronchiolitis care. We found that overall sites participating in a national bronchiolitis QI project sustained improvements in key bronchiolitis quality measures for 1 year following the project’s completion. For the aggregate group no measures worsened, and one measure, orders for intermittent pulse oximetry monitoring, continued to increase during the sustainability season. Furthermore, the sustained improvements were primarily the result of consistent sustained performance of each individual site, as opposed to averages wherein some sites worsened while others improved (Appendix 1a). These findings suggest that designing a collaborative approach, which provides an evidence-based best-practice toolkit while building the QI capacity of local interdisciplinary teams, can support performance gains that persist beyond the project’s active phase.

There are a number of possible reasons why improvements were sustained following the collaborative. The BQIP requirement for institutional leadership buy-in may have motivated accountability to local leaders in subsequent bronchiolitis seasons at each site. We suspect that culture change such as flattened hierarchies through multidisciplinary teams,¹⁴ which empowered nurse and respiratory therapy staff, may have facilitated consistent use of tools created locally. The synergy of interdisciplinary teams composed of physician, nurse, and respiratory therapy champions may have created accountability to perpetuate the previous year’s efforts.¹⁵ In addition, the sites adopted elements of the evidence-based toolkit, such as pathways,^16,17 forcing function tools^13,18 and order sets that limited management decision options and bronchodilator use contingent on respiratory scores,^9,19 which may have driven desired behaviors.

Moreover, the 2014 AAP CPG for the management of bronchiolitis,²⁰ released prior to the sustainability bronchiolitis season, may have underscored the key concepts of the collaborative. Similarly, national exposure of best practices for bronchiolitis management, including the 3 widespread Choosing Wisely recommendations related to bronchiolitis,²¹ might have been a compelling reason for sites to maintain their improvement efforts and contribute to secular trends toward decreasing interventions in bronchiolitis management nationally.³ Lastly, the mechanisms developed for local data collection may have created opportunities at each site to conduct ongoing evaluation of performance on key bronchiolitis quality measures through data-driven feedback systems.²² Our study highlights the need for additional research in order to understand why improvements are or are not sustained.

Even with substantial, sustained improvements in this initiative, further reduction in unnecessary care may be possible. Findings from previous studies suggest that even multifaceted QI interventions, including provider education, guidelines and use of respiratory scores, may only modestly reduce bronchodilators, steroids, and chest radiograph use.^8,13 To achieve continued improvements in bronchiolitis care, additional active efforts may be needed to develop new interventions that target root causes for areas of overuse at individual sites.

Future multiinstitutional collaboratives might benefit their participants if they include a focus on helping sites develop skills to ensure that local improvement activities continue after the collaborative phases are completed. Proactively scheduling intermittent check-ins with collaborative members to discuss experiences with both sustainability and ongoing improvement may be valuable and likely needs to be incorporated into the initial collaborative planning.

As these sustainability data represent a subset of 9 of the original 21 BQIP sites, there is concern for potential selection bias related to factors that could have motivated sites to participate in the sustainability season’s data collection and simultaneously influenced their performance. These concerns were mitigated to some extent through 3 specific analyses: finding limited differences in hospital characteristics, baseline performance in key bronchiolitis measures, and performance change from baseline to intervention seasons between sites that did and did not participate in the sustainability season.

Notably, sites that participated in the sustainability phase actually had lower baseline respiratory score use and fewer orders for intermittent pulse oximetry at baseline. Theoretically, if participation in the collaborative highlighted this disparity for these sites, it could have been a motivating factor for their continued participation and sustained performance across these measures. Similarly, sites that recognized their higher baseline performance through participation in the collaborative might have felt less motivation to participate in ongoing data collection during the sustainability season. Whether they might have also sustained, declined, or continued improving is not known. Additionally, the magnitude of improvement in the collaborative period might have also motivated ongoing participation during the sustainability phase. For example, although all sites improved in score use during the collaborative, sites participating in the sustainability season demonstrated significantly more improvement in these measures. Sites with a higher magnitude of improvement in collaborative measures might have more enthusiasm about the project, more commitment to the project activities, or feel a sense of obligation to respond to requests for additional data collection.

This work has several limitations. Selection bias may limit generalizability of the results, as sites that did not participate in the sustainability season may have had different results than those that did participate. It is unknown whether sites that regressed toward their baseline were deterred from participating in the sustainability season. The analyses that we were able to preform, however, suggest that the 2 groups were similar in their characteristics as well as in their baseline and improvement performance.

We have limited knowledge of the local improvement work that sites conducted between the completion of the collaborative and the sustainability season. Site-specific factors may have influenced improvement sustainability. For example, qualitative research with the original group found that team engagement had a quantitative association with better performance, but only for the bronchodilator use measure.²³ Sites were responsible for their own data collection, and despite attempts to centralize and standardize the process, data collection inconsistencies may have occurred. For instance, it is unknown how closely that orders for intermittent pulse oximetry correlate with intermittent use at the bedside. Lastly, the absence of a control group limits examination of the causal relationships of interventions and the influence of secular trends.

Improvements gained during the BQIP collaborative were sustained at 1 year following completion of the collaborative. These findings are encouraging, as national QI collaborative efforts are increasingly common. Our findings suggest that opportunities exist to even further reduce unnecessary care in the management of bronchiolitis. Such opportunities highlight the importance of integrating strategies to both measure sustainability and plan for ongoing independent local activities after completion of the collaborative. Future efforts should focus on supporting local sites to continue individual practice-improvement as they transition from collaborative to independent quality initiatives.

Acknowledgments

The authors thank the 21 hospitals that participated in the BQIP collaborative, and in particular the 9 hospital teams that contributed sustainability data for their ongoing dedication. There was no external funding for this manuscript.

Disclosure

The authors report no financial conflicts of interest.

Acute viral bronchiolitis is the most common cause of hospitalization for children less than 1 year of age.¹ Overuse of ineffective therapies has persisted despite the existence of the evidence-based American Academy of Pediatrics (AAP) clinical practice guideline (CPG), which recommends primarily supportive care.^2-8 Adherence to the AAP CPG recommendations for management of bronchiolitis improved significantly through the AAP’s Bronchiolitis Quality Improvement Project (BQIP), a 12-month, multiinstitutional collaborative of community and free-standing children’s hospitals.⁹ This subsequent study investigates if these improvements were sustained after completion of the formal 12-month project.

Published multiinstitutional bronchiolitis quality improvement (QI) work is limited to 1 study⁵ that describes the results of a single intervention season at academic medical centers. Multiyear bronchiolitis QI projects are limited to single-center studies, and results have been mixed.^5,6,8,10-13 One study¹¹ observed continued improvement in bronchodilator use in subsequent seasons, whereas a second study¹⁰ observed a return to baseline bronchodilator use in the following season. Mittal⁶ observed inconsistent improvements in key bronchiolitis measures during postintervention seasons.

Our specific aim was to assess the sustainability of improvements in bronchiolitis management at participating institutions 1 year following completion of the AAP BQIP collaborative.⁹ Because no studies demonstrate the most effective way to support long-term improvement through a QI collaborative, we hypothesized that the initial collaborative activities, which were designed to build the capacity of local interdisciplinary teams while providing standardized evidence-based care pathways, would lead to performance in the subsequent season at levels similar to or better than those observed during the active phase of the collaborative, without additional project interventions.

Study Design and Setting

This was a follow-up study of the AAP Quality Improvement Innovation Networks project entitled “A Quality Collaborative for Improving Hospital Compliance with the AAP Bronchiolitis Guideline” (BQIP).⁹ The AAP Institutional Review Board approved this project.

Twenty-one multidisciplinary, hospital-based teams participated in the BQIP collaborative and provided monthly data during the January through March bronchiolitis season. Teams submitted 2013 baseline data and 2014 intervention data. Nine sites provided 2015 sustainability data following the completion of the collaborative.

Participants

Hospital encounters with a primary diagnosis of acute viral bronchiolitis were eligible for inclusion among patients from 1 month to 2 years of age. Encounters were excluded for prematurity (<35 weeks gestational age), congenital heart disease, bronchopulmonary dysplasia, genetic, congenital or neuromuscular abnormalities, and pediatric intensive-care admission.

Data Collection

Hospital characteristics were collected, including hospital type (academic, community), bed size, location (urban, rural), hospital distributions of race/ethnicity and public payer, cases of bronchiolitis per year, presence of an electronic medical record and a pediatric respiratory therapist, and self-rated QI knowledge of the multidisciplinary team (very knowledgeable, knowledgeable, and somewhat knowledgeable). A trained member at each site collected data through structured chart review in baseline, intervention, and sustainability bronchiolitis seasons for January, February, and March. Site members reviewed the first 20 charts per month that met the inclusion criteria or all charts if there were fewer than 20 eligible encounters. Sites input data about key quality measures into the AAP’s Quality Improvement Data Aggregator, a web-based data repository.

Intervention

The BQIP project was designed as a virtual collaborative consisting of monthly education webinars about QI methods and bronchiolitis management, opportunities for collaboration via teleconference and e-mail listserv, and individual site-coaching by e-mail or telephone.⁹ A change package was shared with sites that included examples of evidence-based pathways, ordersets, a respiratory scoring tool, communication tools for parents and referring physicians, and slide sets for individual site education efforts. Following completion of the collaborative, written resources remained available to participants, although virtual collaboration ceased and no additional project interventions to promote sustainability were introduced.

Bronchiolitis Process and Outcome Measures

Process measures following admission included the following: severity assessment using a respiratory score, respiratory score use to assess response to bronchodilators, bronchodilator use, bronchodilator doses, steroid doses per patient encounter, chest radiographs per encounter, and presence of an order to transition to intermittent pulse oximetry monitoring. Outcome measures included length of stay and readmissions within 72 hours.

Analysis

Changes among baseline-, intervention-, and sustainability-season data were assessed using generalized linear mixed-effects models with random effect for study sites. Negative binomial models were used for count variables to allow for overdispersion. Length of stay was log-transformed to achieve a normal distribution. We also analyzed each site individually to assess whether sustained improvements were the result of broad sustainability across all sites or whether they represented an aggregation of some sites that continued to improve while other sites actually worsened.

To address any bias introduced by the voluntary and incomplete participation of sites in the sustainability season, we planned a priori to conduct 3 additional analyses. First, we compared the characteristics of sites that did participate in the sustainability season with those that did not participate by using Chi-squared tests for differences in proportions and t tests for differences in means. Second, we determined whether the baseline-season process and outcome measures were different between sites that did and did not participate using descriptive statistics. Third, we assessed whether improvements between the baseline and intervention seasons were different between sites that did and did not participate using a linear mixed-effects model for normally distributed outcomes and generalized linear mixed-effects model with site-specific random effects for nonnormally distributed outcomes. All study outcomes were summarized in terms of model-adjusted means along with the corresponding 95% confidence intervals. All P values are 2-sided, and P < 0.05 was used to define statistical significance. Data analyses were conducted using SAS software (SAS Institute Inc., Cary, North Carolina) version 9.4.

To our knowledge, this is the first report of sustained improvements in care achieved through a multiinstitutional QI collaborative of community and academic hospitals focused on bronchiolitis care. We found that overall sites participating in a national bronchiolitis QI project sustained improvements in key bronchiolitis quality measures for 1 year following the project’s completion. For the aggregate group no measures worsened, and one measure, orders for intermittent pulse oximetry monitoring, continued to increase during the sustainability season. Furthermore, the sustained improvements were primarily the result of consistent sustained performance of each individual site, as opposed to averages wherein some sites worsened while others improved (Appendix 1a). These findings suggest that designing a collaborative approach, which provides an evidence-based best-practice toolkit while building the QI capacity of local interdisciplinary teams, can support performance gains that persist beyond the project’s active phase.

There are a number of possible reasons why improvements were sustained following the collaborative. The BQIP requirement for institutional leadership buy-in may have motivated accountability to local leaders in subsequent bronchiolitis seasons at each site. We suspect that culture change such as flattened hierarchies through multidisciplinary teams,¹⁴ which empowered nurse and respiratory therapy staff, may have facilitated consistent use of tools created locally. The synergy of interdisciplinary teams composed of physician, nurse, and respiratory therapy champions may have created accountability to perpetuate the previous year’s efforts.¹⁵ In addition, the sites adopted elements of the evidence-based toolkit, such as pathways,^16,17 forcing function tools^13,18 and order sets that limited management decision options and bronchodilator use contingent on respiratory scores,^9,19 which may have driven desired behaviors.

Moreover, the 2014 AAP CPG for the management of bronchiolitis,²⁰ released prior to the sustainability bronchiolitis season, may have underscored the key concepts of the collaborative. Similarly, national exposure of best practices for bronchiolitis management, including the 3 widespread Choosing Wisely recommendations related to bronchiolitis,²¹ might have been a compelling reason for sites to maintain their improvement efforts and contribute to secular trends toward decreasing interventions in bronchiolitis management nationally.³ Lastly, the mechanisms developed for local data collection may have created opportunities at each site to conduct ongoing evaluation of performance on key bronchiolitis quality measures through data-driven feedback systems.²² Our study highlights the need for additional research in order to understand why improvements are or are not sustained.

Even with substantial, sustained improvements in this initiative, further reduction in unnecessary care may be possible. Findings from previous studies suggest that even multifaceted QI interventions, including provider education, guidelines and use of respiratory scores, may only modestly reduce bronchodilators, steroids, and chest radiograph use.^8,13 To achieve continued improvements in bronchiolitis care, additional active efforts may be needed to develop new interventions that target root causes for areas of overuse at individual sites.

Future multiinstitutional collaboratives might benefit their participants if they include a focus on helping sites develop skills to ensure that local improvement activities continue after the collaborative phases are completed. Proactively scheduling intermittent check-ins with collaborative members to discuss experiences with both sustainability and ongoing improvement may be valuable and likely needs to be incorporated into the initial collaborative planning.

As these sustainability data represent a subset of 9 of the original 21 BQIP sites, there is concern for potential selection bias related to factors that could have motivated sites to participate in the sustainability season’s data collection and simultaneously influenced their performance. These concerns were mitigated to some extent through 3 specific analyses: finding limited differences in hospital characteristics, baseline performance in key bronchiolitis measures, and performance change from baseline to intervention seasons between sites that did and did not participate in the sustainability season.

Notably, sites that participated in the sustainability phase actually had lower baseline respiratory score use and fewer orders for intermittent pulse oximetry at baseline. Theoretically, if participation in the collaborative highlighted this disparity for these sites, it could have been a motivating factor for their continued participation and sustained performance across these measures. Similarly, sites that recognized their higher baseline performance through participation in the collaborative might have felt less motivation to participate in ongoing data collection during the sustainability season. Whether they might have also sustained, declined, or continued improving is not known. Additionally, the magnitude of improvement in the collaborative period might have also motivated ongoing participation during the sustainability phase. For example, although all sites improved in score use during the collaborative, sites participating in the sustainability season demonstrated significantly more improvement in these measures. Sites with a higher magnitude of improvement in collaborative measures might have more enthusiasm about the project, more commitment to the project activities, or feel a sense of obligation to respond to requests for additional data collection.

This work has several limitations. Selection bias may limit generalizability of the results, as sites that did not participate in the sustainability season may have had different results than those that did participate. It is unknown whether sites that regressed toward their baseline were deterred from participating in the sustainability season. The analyses that we were able to preform, however, suggest that the 2 groups were similar in their characteristics as well as in their baseline and improvement performance.

We have limited knowledge of the local improvement work that sites conducted between the completion of the collaborative and the sustainability season. Site-specific factors may have influenced improvement sustainability. For example, qualitative research with the original group found that team engagement had a quantitative association with better performance, but only for the bronchodilator use measure.²³ Sites were responsible for their own data collection, and despite attempts to centralize and standardize the process, data collection inconsistencies may have occurred. For instance, it is unknown how closely that orders for intermittent pulse oximetry correlate with intermittent use at the bedside. Lastly, the absence of a control group limits examination of the causal relationships of interventions and the influence of secular trends.

Improvements gained during the BQIP collaborative were sustained at 1 year following completion of the collaborative. These findings are encouraging, as national QI collaborative efforts are increasingly common. Our findings suggest that opportunities exist to even further reduce unnecessary care in the management of bronchiolitis. Such opportunities highlight the importance of integrating strategies to both measure sustainability and plan for ongoing independent local activities after completion of the collaborative. Future efforts should focus on supporting local sites to continue individual practice-improvement as they transition from collaborative to independent quality initiatives.

Acknowledgments

The authors thank the 21 hospitals that participated in the BQIP collaborative, and in particular the 9 hospital teams that contributed sustainability data for their ongoing dedication. There was no external funding for this manuscript.

Disclosure

The authors report no financial conflicts of interest.

Acute viral bronchiolitis is the most common cause of hospitalization for children less than 1 year of age.¹ Overuse of ineffective therapies has persisted despite the existence of the evidence-based American Academy of Pediatrics (AAP) clinical practice guideline (CPG), which recommends primarily supportive care.^2-8 Adherence to the AAP CPG recommendations for management of bronchiolitis improved significantly through the AAP’s Bronchiolitis Quality Improvement Project (BQIP), a 12-month, multiinstitutional collaborative of community and free-standing children’s hospitals.⁹ This subsequent study investigates if these improvements were sustained after completion of the formal 12-month project.

Published multiinstitutional bronchiolitis quality improvement (QI) work is limited to 1 study⁵ that describes the results of a single intervention season at academic medical centers. Multiyear bronchiolitis QI projects are limited to single-center studies, and results have been mixed.^5,6,8,10-13 One study¹¹ observed continued improvement in bronchodilator use in subsequent seasons, whereas a second study¹⁰ observed a return to baseline bronchodilator use in the following season. Mittal⁶ observed inconsistent improvements in key bronchiolitis measures during postintervention seasons.

Our specific aim was to assess the sustainability of improvements in bronchiolitis management at participating institutions 1 year following completion of the AAP BQIP collaborative.⁹ Because no studies demonstrate the most effective way to support long-term improvement through a QI collaborative, we hypothesized that the initial collaborative activities, which were designed to build the capacity of local interdisciplinary teams while providing standardized evidence-based care pathways, would lead to performance in the subsequent season at levels similar to or better than those observed during the active phase of the collaborative, without additional project interventions.

Study Design and Setting

This was a follow-up study of the AAP Quality Improvement Innovation Networks project entitled “A Quality Collaborative for Improving Hospital Compliance with the AAP Bronchiolitis Guideline” (BQIP).⁹ The AAP Institutional Review Board approved this project.

Twenty-one multidisciplinary, hospital-based teams participated in the BQIP collaborative and provided monthly data during the January through March bronchiolitis season. Teams submitted 2013 baseline data and 2014 intervention data. Nine sites provided 2015 sustainability data following the completion of the collaborative.

Participants

Hospital encounters with a primary diagnosis of acute viral bronchiolitis were eligible for inclusion among patients from 1 month to 2 years of age. Encounters were excluded for prematurity (<35 weeks gestational age), congenital heart disease, bronchopulmonary dysplasia, genetic, congenital or neuromuscular abnormalities, and pediatric intensive-care admission.

Data Collection

Hospital characteristics were collected, including hospital type (academic, community), bed size, location (urban, rural), hospital distributions of race/ethnicity and public payer, cases of bronchiolitis per year, presence of an electronic medical record and a pediatric respiratory therapist, and self-rated QI knowledge of the multidisciplinary team (very knowledgeable, knowledgeable, and somewhat knowledgeable). A trained member at each site collected data through structured chart review in baseline, intervention, and sustainability bronchiolitis seasons for January, February, and March. Site members reviewed the first 20 charts per month that met the inclusion criteria or all charts if there were fewer than 20 eligible encounters. Sites input data about key quality measures into the AAP’s Quality Improvement Data Aggregator, a web-based data repository.

Intervention

The BQIP project was designed as a virtual collaborative consisting of monthly education webinars about QI methods and bronchiolitis management, opportunities for collaboration via teleconference and e-mail listserv, and individual site-coaching by e-mail or telephone.⁹ A change package was shared with sites that included examples of evidence-based pathways, ordersets, a respiratory scoring tool, communication tools for parents and referring physicians, and slide sets for individual site education efforts. Following completion of the collaborative, written resources remained available to participants, although virtual collaboration ceased and no additional project interventions to promote sustainability were introduced.

Bronchiolitis Process and Outcome Measures

Process measures following admission included the following: severity assessment using a respiratory score, respiratory score use to assess response to bronchodilators, bronchodilator use, bronchodilator doses, steroid doses per patient encounter, chest radiographs per encounter, and presence of an order to transition to intermittent pulse oximetry monitoring. Outcome measures included length of stay and readmissions within 72 hours.

Analysis

Changes among baseline-, intervention-, and sustainability-season data were assessed using generalized linear mixed-effects models with random effect for study sites. Negative binomial models were used for count variables to allow for overdispersion. Length of stay was log-transformed to achieve a normal distribution. We also analyzed each site individually to assess whether sustained improvements were the result of broad sustainability across all sites or whether they represented an aggregation of some sites that continued to improve while other sites actually worsened.

To address any bias introduced by the voluntary and incomplete participation of sites in the sustainability season, we planned a priori to conduct 3 additional analyses. First, we compared the characteristics of sites that did participate in the sustainability season with those that did not participate by using Chi-squared tests for differences in proportions and t tests for differences in means. Second, we determined whether the baseline-season process and outcome measures were different between sites that did and did not participate using descriptive statistics. Third, we assessed whether improvements between the baseline and intervention seasons were different between sites that did and did not participate using a linear mixed-effects model for normally distributed outcomes and generalized linear mixed-effects model with site-specific random effects for nonnormally distributed outcomes. All study outcomes were summarized in terms of model-adjusted means along with the corresponding 95% confidence intervals. All P values are 2-sided, and P < 0.05 was used to define statistical significance. Data analyses were conducted using SAS software (SAS Institute Inc., Cary, North Carolina) version 9.4.

To our knowledge, this is the first report of sustained improvements in care achieved through a multiinstitutional QI collaborative of community and academic hospitals focused on bronchiolitis care. We found that overall sites participating in a national bronchiolitis QI project sustained improvements in key bronchiolitis quality measures for 1 year following the project’s completion. For the aggregate group no measures worsened, and one measure, orders for intermittent pulse oximetry monitoring, continued to increase during the sustainability season. Furthermore, the sustained improvements were primarily the result of consistent sustained performance of each individual site, as opposed to averages wherein some sites worsened while others improved (Appendix 1a). These findings suggest that designing a collaborative approach, which provides an evidence-based best-practice toolkit while building the QI capacity of local interdisciplinary teams, can support performance gains that persist beyond the project’s active phase.

There are a number of possible reasons why improvements were sustained following the collaborative. The BQIP requirement for institutional leadership buy-in may have motivated accountability to local leaders in subsequent bronchiolitis seasons at each site. We suspect that culture change such as flattened hierarchies through multidisciplinary teams,¹⁴ which empowered nurse and respiratory therapy staff, may have facilitated consistent use of tools created locally. The synergy of interdisciplinary teams composed of physician, nurse, and respiratory therapy champions may have created accountability to perpetuate the previous year’s efforts.¹⁵ In addition, the sites adopted elements of the evidence-based toolkit, such as pathways,^16,17 forcing function tools^13,18 and order sets that limited management decision options and bronchodilator use contingent on respiratory scores,^9,19 which may have driven desired behaviors.

Moreover, the 2014 AAP CPG for the management of bronchiolitis,²⁰ released prior to the sustainability bronchiolitis season, may have underscored the key concepts of the collaborative. Similarly, national exposure of best practices for bronchiolitis management, including the 3 widespread Choosing Wisely recommendations related to bronchiolitis,²¹ might have been a compelling reason for sites to maintain their improvement efforts and contribute to secular trends toward decreasing interventions in bronchiolitis management nationally.³ Lastly, the mechanisms developed for local data collection may have created opportunities at each site to conduct ongoing evaluation of performance on key bronchiolitis quality measures through data-driven feedback systems.²² Our study highlights the need for additional research in order to understand why improvements are or are not sustained.

Even with substantial, sustained improvements in this initiative, further reduction in unnecessary care may be possible. Findings from previous studies suggest that even multifaceted QI interventions, including provider education, guidelines and use of respiratory scores, may only modestly reduce bronchodilators, steroids, and chest radiograph use.^8,13 To achieve continued improvements in bronchiolitis care, additional active efforts may be needed to develop new interventions that target root causes for areas of overuse at individual sites.

Future multiinstitutional collaboratives might benefit their participants if they include a focus on helping sites develop skills to ensure that local improvement activities continue after the collaborative phases are completed. Proactively scheduling intermittent check-ins with collaborative members to discuss experiences with both sustainability and ongoing improvement may be valuable and likely needs to be incorporated into the initial collaborative planning.

As these sustainability data represent a subset of 9 of the original 21 BQIP sites, there is concern for potential selection bias related to factors that could have motivated sites to participate in the sustainability season’s data collection and simultaneously influenced their performance. These concerns were mitigated to some extent through 3 specific analyses: finding limited differences in hospital characteristics, baseline performance in key bronchiolitis measures, and performance change from baseline to intervention seasons between sites that did and did not participate in the sustainability season.

Notably, sites that participated in the sustainability phase actually had lower baseline respiratory score use and fewer orders for intermittent pulse oximetry at baseline. Theoretically, if participation in the collaborative highlighted this disparity for these sites, it could have been a motivating factor for their continued participation and sustained performance across these measures. Similarly, sites that recognized their higher baseline performance through participation in the collaborative might have felt less motivation to participate in ongoing data collection during the sustainability season. Whether they might have also sustained, declined, or continued improving is not known. Additionally, the magnitude of improvement in the collaborative period might have also motivated ongoing participation during the sustainability phase. For example, although all sites improved in score use during the collaborative, sites participating in the sustainability season demonstrated significantly more improvement in these measures. Sites with a higher magnitude of improvement in collaborative measures might have more enthusiasm about the project, more commitment to the project activities, or feel a sense of obligation to respond to requests for additional data collection.

This work has several limitations. Selection bias may limit generalizability of the results, as sites that did not participate in the sustainability season may have had different results than those that did participate. It is unknown whether sites that regressed toward their baseline were deterred from participating in the sustainability season. The analyses that we were able to preform, however, suggest that the 2 groups were similar in their characteristics as well as in their baseline and improvement performance.

We have limited knowledge of the local improvement work that sites conducted between the completion of the collaborative and the sustainability season. Site-specific factors may have influenced improvement sustainability. For example, qualitative research with the original group found that team engagement had a quantitative association with better performance, but only for the bronchodilator use measure.²³ Sites were responsible for their own data collection, and despite attempts to centralize and standardize the process, data collection inconsistencies may have occurred. For instance, it is unknown how closely that orders for intermittent pulse oximetry correlate with intermittent use at the bedside. Lastly, the absence of a control group limits examination of the causal relationships of interventions and the influence of secular trends.

Improvements gained during the BQIP collaborative were sustained at 1 year following completion of the collaborative. These findings are encouraging, as national QI collaborative efforts are increasingly common. Our findings suggest that opportunities exist to even further reduce unnecessary care in the management of bronchiolitis. Such opportunities highlight the importance of integrating strategies to both measure sustainability and plan for ongoing independent local activities after completion of the collaborative. Future efforts should focus on supporting local sites to continue individual practice-improvement as they transition from collaborative to independent quality initiatives.

Acknowledgments

The authors thank the 21 hospitals that participated in the BQIP collaborative, and in particular the 9 hospital teams that contributed sustainability data for their ongoing dedication. There was no external funding for this manuscript.

Disclosure

The authors report no financial conflicts of interest.

Health literacy (HL) is the ability of individuals to obtain, process, and understand health information in a way that enables them to make health decisions.¹ Approximately one-third of adults in the United States are considered to have inadequate HL,² and its prevalence is even higher among hospitalized patients.³ Low HL has been associated with higher rates of hospital readmission⁴ and higher mortality.^5,6 Inadequate HL has been identified as a barrier to communication and is associated with poorer outcomes for communication-sensitive behaviors, such as adherence to medications, chronic disease self-efficacy and self-management,^7-10 and understanding hospital discharge instructions.^11,12 It has been largely understood that the association between HL and hospital outcomes has been mediated by these communication challenges.

In this issue of the journal, Boyle et al.¹³ demonstrate that inadequate HL is not only a communication barrier but also an indicator of other social support needs during a transition from the hospital. In particular, the authors found that hospitalized patients with inadequate HL had needs in more social support domains than those with adequate HL. After multivariable adjustment for sociodemographic factors that likely impact social support, such as age and marital status, inadequate HL remained associated specifically with insufficient caregiver support and transportation barriers. These findings suggest that, along with the more direct comprehension barriers previously associated with inadequate HL, the identified social support needs may mediate prior established associations between inadequate HL and poor health outcomes.

The authors concluded that screening for HL along with transitional care needs will allow hospitals to ensure a quality care transition. Indeed, screening for these gaps is the first step in identifying important postdischarge social needs and will be necessary in order to track improvements for at-risk populations. However, screening alone will not likely change outcomes; for this, we will need effective interventions.

In fact, it remains an open question how best to intervene to improve care transitions for patients with social needs and low HL. The recent focus of HL interventions in the literature has been on “universal precautions,” such as the teach-back technique, to ensure patient comprehension of information, and writing patient informational materials at a low literacy level.¹⁴This approach to make all materials and communication accessible to all patients, rather than to tailor HL interventions, has become more prevalent in efforts to address the adverse communication and resultant health impacts of inadequate HL.^15-17

Meanwhile, the focus of care transition interventions has been on transition coaching or case management in the hospital, medication reconciliation prior to discharge, and postdischarge telephone calls from pharmacists or nurses, often utilizing the HL “universal precautions.”^18-20 While these approaches have been impactful to improve discharge preparedness and decrease readmission rates,²¹ they may not adequately address individual social support and social service needs when the patient leaves the hospital.

Recently, the National Academy of Medicine published the Accountable Health Community Screening Tool, designed to screen for the following 5 areas of unmet social need that are known to be impactful for health: housing stability, food insecurity, transportation needs, utility needs, and interpersonal violence.²² This screener is being used as part of the Center for Medicare & Medicaid Services’ Accountable Health Communities Model and is being tested by the Center for Medicare and Medicaid Innovation (CMMI). The goal of the CMMI evaluation is to test whether systematically identifying social service needs and closing the gap between clinical care and community services for patients with the highest levels of need will improve health outcomes.

Screening for HL and social determinants in the hospital will not, in and of itself, improve the quality of care transitions or prevent subsequent readmissions, morbidity, or mortality. However, measurement is the first step toward identifying individuals with the greatest need and can help direct hospitals’ utilization of limited resources, such as transition managers. The CMMI Accountable Health Communities Model evaluation will provide hospital and healthcare systems with best practices for building clinical–social services networks and connecting at-risk patients with high levels of need to appropriate services in the community.

No longer can a patient’s hospital care end with writing prescriptions and scheduling follow-up appointments. For some, using teach-back and low literacy-appropriate discharge materials will be enough; others will require a postdischarge telephone call to review medications and symptoms and ensure follow-up. But for those highest-risk patients, connection to a network of ongoing community social support will be necessary to guide their transition back to health in the community.

Disclosure 

The author reports no conflicts of interest.

Health literacy (HL) is the ability of individuals to obtain, process, and understand health information in a way that enables them to make health decisions.¹ Approximately one-third of adults in the United States are considered to have inadequate HL,² and its prevalence is even higher among hospitalized patients.³ Low HL has been associated with higher rates of hospital readmission⁴ and higher mortality.^5,6 Inadequate HL has been identified as a barrier to communication and is associated with poorer outcomes for communication-sensitive behaviors, such as adherence to medications, chronic disease self-efficacy and self-management,^7-10 and understanding hospital discharge instructions.^11,12 It has been largely understood that the association between HL and hospital outcomes has been mediated by these communication challenges.

In this issue of the journal, Boyle et al.¹³ demonstrate that inadequate HL is not only a communication barrier but also an indicator of other social support needs during a transition from the hospital. In particular, the authors found that hospitalized patients with inadequate HL had needs in more social support domains than those with adequate HL. After multivariable adjustment for sociodemographic factors that likely impact social support, such as age and marital status, inadequate HL remained associated specifically with insufficient caregiver support and transportation barriers. These findings suggest that, along with the more direct comprehension barriers previously associated with inadequate HL, the identified social support needs may mediate prior established associations between inadequate HL and poor health outcomes.

The authors concluded that screening for HL along with transitional care needs will allow hospitals to ensure a quality care transition. Indeed, screening for these gaps is the first step in identifying important postdischarge social needs and will be necessary in order to track improvements for at-risk populations. However, screening alone will not likely change outcomes; for this, we will need effective interventions.

In fact, it remains an open question how best to intervene to improve care transitions for patients with social needs and low HL. The recent focus of HL interventions in the literature has been on “universal precautions,” such as the teach-back technique, to ensure patient comprehension of information, and writing patient informational materials at a low literacy level.¹⁴This approach to make all materials and communication accessible to all patients, rather than to tailor HL interventions, has become more prevalent in efforts to address the adverse communication and resultant health impacts of inadequate HL.^15-17

Meanwhile, the focus of care transition interventions has been on transition coaching or case management in the hospital, medication reconciliation prior to discharge, and postdischarge telephone calls from pharmacists or nurses, often utilizing the HL “universal precautions.”^18-20 While these approaches have been impactful to improve discharge preparedness and decrease readmission rates,²¹ they may not adequately address individual social support and social service needs when the patient leaves the hospital.

Recently, the National Academy of Medicine published the Accountable Health Community Screening Tool, designed to screen for the following 5 areas of unmet social need that are known to be impactful for health: housing stability, food insecurity, transportation needs, utility needs, and interpersonal violence.²² This screener is being used as part of the Center for Medicare & Medicaid Services’ Accountable Health Communities Model and is being tested by the Center for Medicare and Medicaid Innovation (CMMI). The goal of the CMMI evaluation is to test whether systematically identifying social service needs and closing the gap between clinical care and community services for patients with the highest levels of need will improve health outcomes.

Screening for HL and social determinants in the hospital will not, in and of itself, improve the quality of care transitions or prevent subsequent readmissions, morbidity, or mortality. However, measurement is the first step toward identifying individuals with the greatest need and can help direct hospitals’ utilization of limited resources, such as transition managers. The CMMI Accountable Health Communities Model evaluation will provide hospital and healthcare systems with best practices for building clinical–social services networks and connecting at-risk patients with high levels of need to appropriate services in the community.

No longer can a patient’s hospital care end with writing prescriptions and scheduling follow-up appointments. For some, using teach-back and low literacy-appropriate discharge materials will be enough; others will require a postdischarge telephone call to review medications and symptoms and ensure follow-up. But for those highest-risk patients, connection to a network of ongoing community social support will be necessary to guide their transition back to health in the community.

Disclosure 

The author reports no conflicts of interest.

Health literacy (HL) is the ability of individuals to obtain, process, and understand health information in a way that enables them to make health decisions.¹ Approximately one-third of adults in the United States are considered to have inadequate HL,² and its prevalence is even higher among hospitalized patients.³ Low HL has been associated with higher rates of hospital readmission⁴ and higher mortality.^5,6 Inadequate HL has been identified as a barrier to communication and is associated with poorer outcomes for communication-sensitive behaviors, such as adherence to medications, chronic disease self-efficacy and self-management,^7-10 and understanding hospital discharge instructions.^11,12 It has been largely understood that the association between HL and hospital outcomes has been mediated by these communication challenges.

In this issue of the journal, Boyle et al.¹³ demonstrate that inadequate HL is not only a communication barrier but also an indicator of other social support needs during a transition from the hospital. In particular, the authors found that hospitalized patients with inadequate HL had needs in more social support domains than those with adequate HL. After multivariable adjustment for sociodemographic factors that likely impact social support, such as age and marital status, inadequate HL remained associated specifically with insufficient caregiver support and transportation barriers. These findings suggest that, along with the more direct comprehension barriers previously associated with inadequate HL, the identified social support needs may mediate prior established associations between inadequate HL and poor health outcomes.

The authors concluded that screening for HL along with transitional care needs will allow hospitals to ensure a quality care transition. Indeed, screening for these gaps is the first step in identifying important postdischarge social needs and will be necessary in order to track improvements for at-risk populations. However, screening alone will not likely change outcomes; for this, we will need effective interventions.

In fact, it remains an open question how best to intervene to improve care transitions for patients with social needs and low HL. The recent focus of HL interventions in the literature has been on “universal precautions,” such as the teach-back technique, to ensure patient comprehension of information, and writing patient informational materials at a low literacy level.¹⁴This approach to make all materials and communication accessible to all patients, rather than to tailor HL interventions, has become more prevalent in efforts to address the adverse communication and resultant health impacts of inadequate HL.^15-17

Meanwhile, the focus of care transition interventions has been on transition coaching or case management in the hospital, medication reconciliation prior to discharge, and postdischarge telephone calls from pharmacists or nurses, often utilizing the HL “universal precautions.”^18-20 While these approaches have been impactful to improve discharge preparedness and decrease readmission rates,²¹ they may not adequately address individual social support and social service needs when the patient leaves the hospital.

Recently, the National Academy of Medicine published the Accountable Health Community Screening Tool, designed to screen for the following 5 areas of unmet social need that are known to be impactful for health: housing stability, food insecurity, transportation needs, utility needs, and interpersonal violence.²² This screener is being used as part of the Center for Medicare & Medicaid Services’ Accountable Health Communities Model and is being tested by the Center for Medicare and Medicaid Innovation (CMMI). The goal of the CMMI evaluation is to test whether systematically identifying social service needs and closing the gap between clinical care and community services for patients with the highest levels of need will improve health outcomes.

Screening for HL and social determinants in the hospital will not, in and of itself, improve the quality of care transitions or prevent subsequent readmissions, morbidity, or mortality. However, measurement is the first step toward identifying individuals with the greatest need and can help direct hospitals’ utilization of limited resources, such as transition managers. The CMMI Accountable Health Communities Model evaluation will provide hospital and healthcare systems with best practices for building clinical–social services networks and connecting at-risk patients with high levels of need to appropriate services in the community.

No longer can a patient’s hospital care end with writing prescriptions and scheduling follow-up appointments. For some, using teach-back and low literacy-appropriate discharge materials will be enough; others will require a postdischarge telephone call to review medications and symptoms and ensure follow-up. But for those highest-risk patients, connection to a network of ongoing community social support will be necessary to guide their transition back to health in the community.

Disclosure 

The author reports no conflicts of interest.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

When seniors are discharged from the hospital, many will require additional support and therapy to regain their independence and return safely home.^1,2 Most seniors do not understand what their support needs will entail or the differences between therapy choices.³ To complicate the issue, seniors are often incapacitated and unable to make discharge selections for themselves.

Consequently, discharge planners and social workers often explain options to family members and loved ones, who frequently feel overwhelmed.^4,5 While often balancing jobs, loved ones are divided between wanting to stay with the senior in the hospital and driving to area skilled nursing facilities (SNFs) for consideration. Discharges are delayed waiting for families to make visits and choose an SNF. Longer lengths of stay are detrimental to seniors due to the increased risks of infection, functional loss, and cognitive decline.⁶

Although seniors comprised only 12% of the US population in 2003,⁷ they accounted for one-third of all hospitalizations, over 13.2 million hospital stays.⁸ Hospital stays for seniors resulted in hospital charges totaling nearly $329 billion, or 43.6% of national hospital bills in 2003.⁷ Seniors are also high consumers of postacute care services. By 2050, the number of individuals using long-term care services in any setting (eg, at home, assisted living, or SNFs) will be close to 27 million.^9-11 With the knowledge that many seniors will be hospitalized and subsequently require services thereafter, we sought to assist seniors in planning for their hospital discharge needs before they were hospitalized.

Our team developed PlanYourLifespan.org (PYL) to facilitate this planning for postdischarge needs and fill the knowledge gap in understanding postdischarge options. With funding from the Patient Centered Outcomes Research Institute, we aimed to test the effectiveness of PYL on improving knowledge of hospital discharge resources among seniors.

PlanYourLifespan.org

PlanYourLifespan.org (PYL) educates users on the health crises that often occur with age and connects them to posthospital and home-based resources available locally and nationally. PYL is personalized, dynamic, and adaptable in that all the information can be changed per the senior’s wishes or changing health needs.

Content of PYL

Previously, we conducted focus groups with seniors about current and perceived home needs and aging-in-place. Major themes of what advanced life events (ALEs) would impact aging-in-place were identified as follows: hospitalizations, falls, and Alzheimer’s.¹² We organized PYL around these health-related ALEs. Our multidisciplinary team of researchers, seniors, social workers, caregiver agencies, and Area Agencies on Aging representatives then determined what information and resources should be included.

Each section of PYL starts with a video of a senior discussing their real-life personal experiences, with subsections providing interactive information on what seniors can expect, types of resources available to support home needs, and choices to be made. Descriptions of types of settings for therapy, options available, and links to national/local resources (eg, quality indicators for SNFs) are also included. For example, by entering their zip code, users can identify their neighborhood SNFs, closest Area Agency on Aging, and what home caregiver agencies exist in their area.

Users can save their preferences and revisit their choices at any time. To support communication between seniors and their loved ones, a summary of their choices can be printed or e-mailed to relevant parties. For example, a senior uses PYL and can e-mail these choices to family members, which can stimulate a conversation about future posthospital care expectations.

As inadequate health literacy and cognitive impairment are prevalent among seniors, PYL presents information understandable at all levels of health literacy and sensitive to cognitive load.⁹ There is simplified, large-font, no mouse scrolling and audio available for the visually impaired.

Study Design and Randomization

To test PYL, a 2-armed (attention control [AC] and PYL intervention), parallel, randomized controlled trial was conducted. Participants were randomly assigned to 1 of the 2 conditions via a pregenerated central randomization list using equal (1:1) allocation and random permuted block design to ensure relatively equal allocation throughout the study. The AC condition exposed participants to the National Institute on Aging-sponsored website, Go4Life.nia.nih.gov, an educational website on physical activity for seniors. This website has comparable design and layout to PYL but does not include information about advanced planning. The AC condition controlled for the possibility that regular contact with the study team may improve outcomes in participants randomized to the intervention website.

The trial was conducted from October 2014 to September 2015 in Chicago, Illinois; Fort Wayne, Indiana; and Houston, Texas. Inclusion criteria were as follows: age 65 and older, English-speaking, scoring ≥4 questions correctly on the Brief Cognitive Screen,¹⁴ and current self-reported use of a computer or smartphone with internet. Participants were excluded if they had previously participated in the focus groups or beta testing of the PYL website. Community-based patient partners/stakeholders drove subject recruitment in their communities through word of mouth, e-mail bursts, newsletters, and flyers. At the Area Agency on Aging and community centers where services such as food vouchers and case management are provided, participants were recruited on-site and given study information. At the clinical sites, staff referred potential participants. Study materials such as flyers and information sheets were also located in the clinic waiting rooms. The Villages, nonprofit, grassroots, membership organizations that are redefining aging by being a key resource to community members wishing to age in place, heavily relied on electronic recruitment using their regular e-newsletters and e-mail lists to recruit potential participants. Potential subjects were also recruited by distributing flyers at local senior centers and senior housing buildings. Interested seniors contacted research staff who explained the study and assessed their eligibility. If eligible, subjects were scheduled for a face-to-face interview.

At the face-to-face encounter, all study subjects completed a written consent, answered baseline questions, and were randomized to either arm. Next, research staff introduced all study subjects to the website to which they were randomized and provided instructions on its use. Staff were present to assist with questions as needed on navigation but did not assist with decision making for either website. A minimum of 15 minutes and a maximum of 45 minutes was allotted for navigating either website. After navigating their website, participants were administered an immediate in-person posttest survey. One month and 3 months after the face-to-face encounter, research staff contacted all study participants over the phone to complete a follow-up survey. Staff attempted to reach participants up to 3 times by phone. Data were entered into Research Electronic Data Capture survey software.¹⁵ This study was approved by the Northwestern University Institutional Review Board.

Understanding of Posthospital Discharge and Home Services

As part of the larger trial, which included behavioural outcomes that will be reported elsewhere, we sought to explore the effects of PYL on participants’ knowledge and understanding of posthospital discharge and home services (UHS). Participants were asked to respond to 6 questions at baseline, immediate posttest, and at the 1- and 3-month follow-up time points. Knowledge items were developed by the study team in conjunction with the patient/partner stakeholders. UHS scores were calculated as the sum of the 6 questions (each scored 0 if incorrect and 1 if correct) with a possible range of 0-6.

Covariates

Demographic information, self-reported health, importance of religion, and existence of a power of attorney, living will, advanced directive (eg, Physician Orders for Life-Sustaining Treatment) were obtained via self-report. Participants were asked about their general and social self-efficacy using the validated Self-efficacy Scale¹⁶ and their social support using the Lubben Social Network Scale–6.¹⁷ Health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine–Short Form.¹⁸ To measure burden of disease, participant comorbidities were measured using a nonvalidated 9-item dichotomous response condition list, which included some items adapted from the Charlson Comorbidity Index and the Elixhauser Comorbidity Index.

Statistical Analysis

Data analysis included all available data in the intention-to-treat dataset. As UHS was collected at multiple time points up to 3 months postintervention, we employed linear- mixed modeling with random participant effect and fixed arm, time, and time-by-arm interaction terms. The time-by-arm interaction allows for comparison of UHS slopes (or trajectories) across arms. Analyses explored multiple potential covariates, including current utilization of services, physical function, comorbidities, social support, health literacy, self-efficacy, and sociodemographics. Those covariates found to have a significant association (P < 0.05) with outcome were considered for inclusion in the overall model selection process. Ultimately, we developed a final parsimonious, adjusted longitudinal model with primary predictors of time, arm, and their interaction, controlling for only significant baseline variables following a manual backward selection method. All analyses were conducted in SAS software (version 9.4, copyright 2012, The SAS Institute Inc., Cary, North Carolina).

The results of our study show that among seniors, PYL improved their understanding of home-based services and the services that may be required following a hospitalization. Educating seniors about what to expect regarding the transition home from a hospital before a hospitalization even occurs may enable seniors and their families to plan ahead instead of reacting to a hospitalization. PYL, a national, publicly available tool with links to local resources may potentially help in advancing transitional discharge care to prior to a hospitalization.

To our knowledge, this is one of the first websites and trials devoted to planning for seniors’ health trajectory as they age into their 70s, 80s, 90s, and 100s. Clinicians regularly discuss code status and powers of attorney during their end-of-life discussions with patients. We encourage clinicians to ask patients, “What about the 10 to 20 years before you die? Have you considered what you will do if you get sick or need help at home?” While not replacing a social worker, the ability of PYL to connect seniors to local resources makes it somewhat of a “virtual social worker.” With many physician practices unable to afford social workers, PYL provides a free-of-charge means of connecting seniors to area resources.

Acknowledgments

This work was supported through a Patient-Centered Outcomes Research Institute Award (IH-12-11-4259). Dr. Lindquist and Dr. Ciolino had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Research reported in this publication was also supported, in part, by the National Institutes of Health’s National Center for Advancing Translational Sciences, Grant Number UL1TR000150.

Disclaimer 

All statements in this manuscript, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors or Methodology Committee, or the National Institutes of Health.

Disclosure: The authors have nothing to report.

When seniors are discharged from the hospital, many will require additional support and therapy to regain their independence and return safely home.^1,2 Most seniors do not understand what their support needs will entail or the differences between therapy choices.³ To complicate the issue, seniors are often incapacitated and unable to make discharge selections for themselves.

Consequently, discharge planners and social workers often explain options to family members and loved ones, who frequently feel overwhelmed.^4,5 While often balancing jobs, loved ones are divided between wanting to stay with the senior in the hospital and driving to area skilled nursing facilities (SNFs) for consideration. Discharges are delayed waiting for families to make visits and choose an SNF. Longer lengths of stay are detrimental to seniors due to the increased risks of infection, functional loss, and cognitive decline.⁶

Although seniors comprised only 12% of the US population in 2003,⁷ they accounted for one-third of all hospitalizations, over 13.2 million hospital stays.⁸ Hospital stays for seniors resulted in hospital charges totaling nearly $329 billion, or 43.6% of national hospital bills in 2003.⁷ Seniors are also high consumers of postacute care services. By 2050, the number of individuals using long-term care services in any setting (eg, at home, assisted living, or SNFs) will be close to 27 million.^9-11 With the knowledge that many seniors will be hospitalized and subsequently require services thereafter, we sought to assist seniors in planning for their hospital discharge needs before they were hospitalized.

Our team developed PlanYourLifespan.org (PYL) to facilitate this planning for postdischarge needs and fill the knowledge gap in understanding postdischarge options. With funding from the Patient Centered Outcomes Research Institute, we aimed to test the effectiveness of PYL on improving knowledge of hospital discharge resources among seniors.

PlanYourLifespan.org

PlanYourLifespan.org (PYL) educates users on the health crises that often occur with age and connects them to posthospital and home-based resources available locally and nationally. PYL is personalized, dynamic, and adaptable in that all the information can be changed per the senior’s wishes or changing health needs.

Content of PYL

Previously, we conducted focus groups with seniors about current and perceived home needs and aging-in-place. Major themes of what advanced life events (ALEs) would impact aging-in-place were identified as follows: hospitalizations, falls, and Alzheimer’s.¹² We organized PYL around these health-related ALEs. Our multidisciplinary team of researchers, seniors, social workers, caregiver agencies, and Area Agencies on Aging representatives then determined what information and resources should be included.

Each section of PYL starts with a video of a senior discussing their real-life personal experiences, with subsections providing interactive information on what seniors can expect, types of resources available to support home needs, and choices to be made. Descriptions of types of settings for therapy, options available, and links to national/local resources (eg, quality indicators for SNFs) are also included. For example, by entering their zip code, users can identify their neighborhood SNFs, closest Area Agency on Aging, and what home caregiver agencies exist in their area.

Users can save their preferences and revisit their choices at any time. To support communication between seniors and their loved ones, a summary of their choices can be printed or e-mailed to relevant parties. For example, a senior uses PYL and can e-mail these choices to family members, which can stimulate a conversation about future posthospital care expectations.

As inadequate health literacy and cognitive impairment are prevalent among seniors, PYL presents information understandable at all levels of health literacy and sensitive to cognitive load.⁹ There is simplified, large-font, no mouse scrolling and audio available for the visually impaired.

Study Design and Randomization

To test PYL, a 2-armed (attention control [AC] and PYL intervention), parallel, randomized controlled trial was conducted. Participants were randomly assigned to 1 of the 2 conditions via a pregenerated central randomization list using equal (1:1) allocation and random permuted block design to ensure relatively equal allocation throughout the study. The AC condition exposed participants to the National Institute on Aging-sponsored website, Go4Life.nia.nih.gov, an educational website on physical activity for seniors. This website has comparable design and layout to PYL but does not include information about advanced planning. The AC condition controlled for the possibility that regular contact with the study team may improve outcomes in participants randomized to the intervention website.

The trial was conducted from October 2014 to September 2015 in Chicago, Illinois; Fort Wayne, Indiana; and Houston, Texas. Inclusion criteria were as follows: age 65 and older, English-speaking, scoring ≥4 questions correctly on the Brief Cognitive Screen,¹⁴ and current self-reported use of a computer or smartphone with internet. Participants were excluded if they had previously participated in the focus groups or beta testing of the PYL website. Community-based patient partners/stakeholders drove subject recruitment in their communities through word of mouth, e-mail bursts, newsletters, and flyers. At the Area Agency on Aging and community centers where services such as food vouchers and case management are provided, participants were recruited on-site and given study information. At the clinical sites, staff referred potential participants. Study materials such as flyers and information sheets were also located in the clinic waiting rooms. The Villages, nonprofit, grassroots, membership organizations that are redefining aging by being a key resource to community members wishing to age in place, heavily relied on electronic recruitment using their regular e-newsletters and e-mail lists to recruit potential participants. Potential subjects were also recruited by distributing flyers at local senior centers and senior housing buildings. Interested seniors contacted research staff who explained the study and assessed their eligibility. If eligible, subjects were scheduled for a face-to-face interview.

At the face-to-face encounter, all study subjects completed a written consent, answered baseline questions, and were randomized to either arm. Next, research staff introduced all study subjects to the website to which they were randomized and provided instructions on its use. Staff were present to assist with questions as needed on navigation but did not assist with decision making for either website. A minimum of 15 minutes and a maximum of 45 minutes was allotted for navigating either website. After navigating their website, participants were administered an immediate in-person posttest survey. One month and 3 months after the face-to-face encounter, research staff contacted all study participants over the phone to complete a follow-up survey. Staff attempted to reach participants up to 3 times by phone. Data were entered into Research Electronic Data Capture survey software.¹⁵ This study was approved by the Northwestern University Institutional Review Board.

Understanding of Posthospital Discharge and Home Services

As part of the larger trial, which included behavioural outcomes that will be reported elsewhere, we sought to explore the effects of PYL on participants’ knowledge and understanding of posthospital discharge and home services (UHS). Participants were asked to respond to 6 questions at baseline, immediate posttest, and at the 1- and 3-month follow-up time points. Knowledge items were developed by the study team in conjunction with the patient/partner stakeholders. UHS scores were calculated as the sum of the 6 questions (each scored 0 if incorrect and 1 if correct) with a possible range of 0-6.

Covariates

Demographic information, self-reported health, importance of religion, and existence of a power of attorney, living will, advanced directive (eg, Physician Orders for Life-Sustaining Treatment) were obtained via self-report. Participants were asked about their general and social self-efficacy using the validated Self-efficacy Scale¹⁶ and their social support using the Lubben Social Network Scale–6.¹⁷ Health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine–Short Form.¹⁸ To measure burden of disease, participant comorbidities were measured using a nonvalidated 9-item dichotomous response condition list, which included some items adapted from the Charlson Comorbidity Index and the Elixhauser Comorbidity Index.

Statistical Analysis

Data analysis included all available data in the intention-to-treat dataset. As UHS was collected at multiple time points up to 3 months postintervention, we employed linear- mixed modeling with random participant effect and fixed arm, time, and time-by-arm interaction terms. The time-by-arm interaction allows for comparison of UHS slopes (or trajectories) across arms. Analyses explored multiple potential covariates, including current utilization of services, physical function, comorbidities, social support, health literacy, self-efficacy, and sociodemographics. Those covariates found to have a significant association (P < 0.05) with outcome were considered for inclusion in the overall model selection process. Ultimately, we developed a final parsimonious, adjusted longitudinal model with primary predictors of time, arm, and their interaction, controlling for only significant baseline variables following a manual backward selection method. All analyses were conducted in SAS software (version 9.4, copyright 2012, The SAS Institute Inc., Cary, North Carolina).

The results of our study show that among seniors, PYL improved their understanding of home-based services and the services that may be required following a hospitalization. Educating seniors about what to expect regarding the transition home from a hospital before a hospitalization even occurs may enable seniors and their families to plan ahead instead of reacting to a hospitalization. PYL, a national, publicly available tool with links to local resources may potentially help in advancing transitional discharge care to prior to a hospitalization.

To our knowledge, this is one of the first websites and trials devoted to planning for seniors’ health trajectory as they age into their 70s, 80s, 90s, and 100s. Clinicians regularly discuss code status and powers of attorney during their end-of-life discussions with patients. We encourage clinicians to ask patients, “What about the 10 to 20 years before you die? Have you considered what you will do if you get sick or need help at home?” While not replacing a social worker, the ability of PYL to connect seniors to local resources makes it somewhat of a “virtual social worker.” With many physician practices unable to afford social workers, PYL provides a free-of-charge means of connecting seniors to area resources.

Acknowledgments

This work was supported through a Patient-Centered Outcomes Research Institute Award (IH-12-11-4259). Dr. Lindquist and Dr. Ciolino had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Research reported in this publication was also supported, in part, by the National Institutes of Health’s National Center for Advancing Translational Sciences, Grant Number UL1TR000150.

Disclaimer 

All statements in this manuscript, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors or Methodology Committee, or the National Institutes of Health.

Disclosure: The authors have nothing to report.

When seniors are discharged from the hospital, many will require additional support and therapy to regain their independence and return safely home.^1,2 Most seniors do not understand what their support needs will entail or the differences between therapy choices.³ To complicate the issue, seniors are often incapacitated and unable to make discharge selections for themselves.

Consequently, discharge planners and social workers often explain options to family members and loved ones, who frequently feel overwhelmed.^4,5 While often balancing jobs, loved ones are divided between wanting to stay with the senior in the hospital and driving to area skilled nursing facilities (SNFs) for consideration. Discharges are delayed waiting for families to make visits and choose an SNF. Longer lengths of stay are detrimental to seniors due to the increased risks of infection, functional loss, and cognitive decline.⁶

Although seniors comprised only 12% of the US population in 2003,⁷ they accounted for one-third of all hospitalizations, over 13.2 million hospital stays.⁸ Hospital stays for seniors resulted in hospital charges totaling nearly $329 billion, or 43.6% of national hospital bills in 2003.⁷ Seniors are also high consumers of postacute care services. By 2050, the number of individuals using long-term care services in any setting (eg, at home, assisted living, or SNFs) will be close to 27 million.^9-11 With the knowledge that many seniors will be hospitalized and subsequently require services thereafter, we sought to assist seniors in planning for their hospital discharge needs before they were hospitalized.

Our team developed PlanYourLifespan.org (PYL) to facilitate this planning for postdischarge needs and fill the knowledge gap in understanding postdischarge options. With funding from the Patient Centered Outcomes Research Institute, we aimed to test the effectiveness of PYL on improving knowledge of hospital discharge resources among seniors.

PlanYourLifespan.org

PlanYourLifespan.org (PYL) educates users on the health crises that often occur with age and connects them to posthospital and home-based resources available locally and nationally. PYL is personalized, dynamic, and adaptable in that all the information can be changed per the senior’s wishes or changing health needs.

Content of PYL

Previously, we conducted focus groups with seniors about current and perceived home needs and aging-in-place. Major themes of what advanced life events (ALEs) would impact aging-in-place were identified as follows: hospitalizations, falls, and Alzheimer’s.¹² We organized PYL around these health-related ALEs. Our multidisciplinary team of researchers, seniors, social workers, caregiver agencies, and Area Agencies on Aging representatives then determined what information and resources should be included.

Each section of PYL starts with a video of a senior discussing their real-life personal experiences, with subsections providing interactive information on what seniors can expect, types of resources available to support home needs, and choices to be made. Descriptions of types of settings for therapy, options available, and links to national/local resources (eg, quality indicators for SNFs) are also included. For example, by entering their zip code, users can identify their neighborhood SNFs, closest Area Agency on Aging, and what home caregiver agencies exist in their area.

Users can save their preferences and revisit their choices at any time. To support communication between seniors and their loved ones, a summary of their choices can be printed or e-mailed to relevant parties. For example, a senior uses PYL and can e-mail these choices to family members, which can stimulate a conversation about future posthospital care expectations.

As inadequate health literacy and cognitive impairment are prevalent among seniors, PYL presents information understandable at all levels of health literacy and sensitive to cognitive load.⁹ There is simplified, large-font, no mouse scrolling and audio available for the visually impaired.

Study Design and Randomization

To test PYL, a 2-armed (attention control [AC] and PYL intervention), parallel, randomized controlled trial was conducted. Participants were randomly assigned to 1 of the 2 conditions via a pregenerated central randomization list using equal (1:1) allocation and random permuted block design to ensure relatively equal allocation throughout the study. The AC condition exposed participants to the National Institute on Aging-sponsored website, Go4Life.nia.nih.gov, an educational website on physical activity for seniors. This website has comparable design and layout to PYL but does not include information about advanced planning. The AC condition controlled for the possibility that regular contact with the study team may improve outcomes in participants randomized to the intervention website.

The trial was conducted from October 2014 to September 2015 in Chicago, Illinois; Fort Wayne, Indiana; and Houston, Texas. Inclusion criteria were as follows: age 65 and older, English-speaking, scoring ≥4 questions correctly on the Brief Cognitive Screen,¹⁴ and current self-reported use of a computer or smartphone with internet. Participants were excluded if they had previously participated in the focus groups or beta testing of the PYL website. Community-based patient partners/stakeholders drove subject recruitment in their communities through word of mouth, e-mail bursts, newsletters, and flyers. At the Area Agency on Aging and community centers where services such as food vouchers and case management are provided, participants were recruited on-site and given study information. At the clinical sites, staff referred potential participants. Study materials such as flyers and information sheets were also located in the clinic waiting rooms. The Villages, nonprofit, grassroots, membership organizations that are redefining aging by being a key resource to community members wishing to age in place, heavily relied on electronic recruitment using their regular e-newsletters and e-mail lists to recruit potential participants. Potential subjects were also recruited by distributing flyers at local senior centers and senior housing buildings. Interested seniors contacted research staff who explained the study and assessed their eligibility. If eligible, subjects were scheduled for a face-to-face interview.

At the face-to-face encounter, all study subjects completed a written consent, answered baseline questions, and were randomized to either arm. Next, research staff introduced all study subjects to the website to which they were randomized and provided instructions on its use. Staff were present to assist with questions as needed on navigation but did not assist with decision making for either website. A minimum of 15 minutes and a maximum of 45 minutes was allotted for navigating either website. After navigating their website, participants were administered an immediate in-person posttest survey. One month and 3 months after the face-to-face encounter, research staff contacted all study participants over the phone to complete a follow-up survey. Staff attempted to reach participants up to 3 times by phone. Data were entered into Research Electronic Data Capture survey software.¹⁵ This study was approved by the Northwestern University Institutional Review Board.

Understanding of Posthospital Discharge and Home Services

As part of the larger trial, which included behavioural outcomes that will be reported elsewhere, we sought to explore the effects of PYL on participants’ knowledge and understanding of posthospital discharge and home services (UHS). Participants were asked to respond to 6 questions at baseline, immediate posttest, and at the 1- and 3-month follow-up time points. Knowledge items were developed by the study team in conjunction with the patient/partner stakeholders. UHS scores were calculated as the sum of the 6 questions (each scored 0 if incorrect and 1 if correct) with a possible range of 0-6.

Covariates

Demographic information, self-reported health, importance of religion, and existence of a power of attorney, living will, advanced directive (eg, Physician Orders for Life-Sustaining Treatment) were obtained via self-report. Participants were asked about their general and social self-efficacy using the validated Self-efficacy Scale¹⁶ and their social support using the Lubben Social Network Scale–6.¹⁷ Health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine–Short Form.¹⁸ To measure burden of disease, participant comorbidities were measured using a nonvalidated 9-item dichotomous response condition list, which included some items adapted from the Charlson Comorbidity Index and the Elixhauser Comorbidity Index.

Statistical Analysis

Data analysis included all available data in the intention-to-treat dataset. As UHS was collected at multiple time points up to 3 months postintervention, we employed linear- mixed modeling with random participant effect and fixed arm, time, and time-by-arm interaction terms. The time-by-arm interaction allows for comparison of UHS slopes (or trajectories) across arms. Analyses explored multiple potential covariates, including current utilization of services, physical function, comorbidities, social support, health literacy, self-efficacy, and sociodemographics. Those covariates found to have a significant association (P < 0.05) with outcome were considered for inclusion in the overall model selection process. Ultimately, we developed a final parsimonious, adjusted longitudinal model with primary predictors of time, arm, and their interaction, controlling for only significant baseline variables following a manual backward selection method. All analyses were conducted in SAS software (version 9.4, copyright 2012, The SAS Institute Inc., Cary, North Carolina).

The results of our study show that among seniors, PYL improved their understanding of home-based services and the services that may be required following a hospitalization. Educating seniors about what to expect regarding the transition home from a hospital before a hospitalization even occurs may enable seniors and their families to plan ahead instead of reacting to a hospitalization. PYL, a national, publicly available tool with links to local resources may potentially help in advancing transitional discharge care to prior to a hospitalization.

To our knowledge, this is one of the first websites and trials devoted to planning for seniors’ health trajectory as they age into their 70s, 80s, 90s, and 100s. Clinicians regularly discuss code status and powers of attorney during their end-of-life discussions with patients. We encourage clinicians to ask patients, “What about the 10 to 20 years before you die? Have you considered what you will do if you get sick or need help at home?” While not replacing a social worker, the ability of PYL to connect seniors to local resources makes it somewhat of a “virtual social worker.” With many physician practices unable to afford social workers, PYL provides a free-of-charge means of connecting seniors to area resources.

Acknowledgments

This work was supported through a Patient-Centered Outcomes Research Institute Award (IH-12-11-4259). Dr. Lindquist and Dr. Ciolino had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Research reported in this publication was also supported, in part, by the National Institutes of Health’s National Center for Advancing Translational Sciences, Grant Number UL1TR000150.

Disclaimer 

All statements in this manuscript, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors or Methodology Committee, or the National Institutes of Health.

Disclosure: The authors have nothing to report.

Successful smoking cessation interventions result in substantial gains in health and life expectancy by reducing smoking-related illnesses and preventing premature deaths.^1,2 The Department of Health and Human Services recommends clinicians use hospitalization as “an opportunity to promote smoking cessation’’ and ‘‘to prescribe medications to alleviate withdrawal symptoms”³ because individual readiness to quit may be high during hospitalizations. A meta-analysis of 50 studies (21 from the United States) examining the efficacy of hospital-initiated smoking cessation interventions concluded that smoking cessation support programs that began in the hospital and continued for at least 1 month postdischarge significantly increase the likelihood of patients being smoke-free in the long term.⁴ The most efficacious strategies included counseling and pharmacotherapy rather than counseling alone.³ Most inpatient smoking cessation studies have focused on quit-rates or medical outcomes, while fewer studies have looked at healthcare utilization.

However, previous research has shown that smoking cessation for inpatients has relatively immediate economic and health benefits. Patients who quit smoking during hospitalizations for cardiovascular disease are less likely to be readmitted or to die during follow-up.^5,6 Patients with acute myocardial infarction (AMI), unstable angina, heart failure, and chronic obstructive pulmonary disease who received an inpatient smoking cessation intervention had reductions in inpatient readmission rates.⁷ A 1% reduction in overall smoking rates would lead to an annual reduction of 3,022 hospitalizations for stroke and 1,684 hospitalizations for AMI.⁸ One comprehensive program, the Ottawa Model for Smoking Cessation (OMSC), found that a hospital-initiated intervention increased long-term cessation rates by 15% in cardiac patients and by 11% in general hospital populations.^9,10 The applicability of this result to US healthcare systems is unknown. This paper adds to the existing literature by evaluating the impact of an inpatient smoking cessation program on healthcare utilization among patients hospitalized for any reason, rather than solely focused on those with cardiopulmonary diagnoses.

The current study focuses on an inpatient smoking cessation program at a teaching hospital in the Rocky Mountain region. The hospital implemented a smoking cessation treatment program on July 1, 2013, based on the OMSC. The goal was to identify and support inpatient adult smokers who wanted to make a quit attempt and help them remain smoke-free after discharge. The objective of the current study was to determine the effect of the program on 30-day readmission rates and length of stay (LOS) of the index hospitalization. Although the general cost effectiveness of properly structured smoking cessation programs are well established,^11-13 the healthcare utilization effects of inpatient smoking cessation programs are not well understood.

Data

The study population consists of patients over age 18 who were admitted to the hospital between July 1, 2012, and July 1, 2014. Baseline smoking status was assessed at hospital admission and recorded in Epic (Epic Systems Corporation, Verona, Wisconsin), the electronic medical records system, as a current smoker (every day and some days), former smoker, never smoker, and never assessed. To check the accuracy of recorded smoking status, a random sample of 819 inpatients was selected and contacted via telephone for verification; 93% of Epic-identified smokers confirmed that they were smokers at hospital admission.¹⁴

Intervention

The intervention, which launched July 13, 2014, modified the Epic system to automatically alert providers viewing a tobacco user’s medical record that the patient should receive standardized orders for a bedside consultation with a Tobacco Treatment Specialist (TTS) and a prescription for nicotine replacement therapy (NRT) while in the hospital.¹⁵ Previously, referrals for tobacco treatment were done on an ad-hoc basis by the physician, and NRT was not routinely available. This system-level intervention standardized and automated the referral process. For patients with a bedside consultation order, TTS used a patient-centered approach (motivational interviewing) to explore patients’ motivation to quit smoking and offered NRT to improve comfort and safety while in the hospital. Patients who chose to make a quit attempt received a free 2-week supply of NRT at discharge and 6 months of free follow-up counseling by interactive voice response (IVR) telephone technology that included (a) prerecorded advice keyed to individual patient needs, (b) a warm-transfer option to speak with a live TTS (later dropped), and (c) a collection of patient smoking and cessation treatment measures.¹⁵

Statistical Analysis

We used an intent-to-treat (ITT) framework for the analysis, which considers everyone eligible for the treatment to be in the treatment group. The approach ignores treatment nonacceptance, nonadherence, protocol deviations, withdrawal from treatment, and cessation outcomes, thus providing conservative estimates of outcomes.^16-18

Readmission rates and LOS were estimated by using a “difference-in-differences” model, comparing outcomes between smokers before versus after the introduction of the cessation treatment program with nonsmokers before versus after program introduction. The difference-in-differences method looks at the difference pre-and-post in the exposed group (smokers) and unexposed group (nonsmokers). Subtracting the difference between the 2 groups gives an estimate of the policy effect controlling for background trends.¹⁹ The smoking cessation treatment effect on readmission is measured by the coefficient on the interaction term between the smoking variable and an indicator that the program is operational. The coefficient is the “difference-in-differences.”

Other control variables include demographic factors (gender, age, race), hospitalization payer (Medicare, Medicaid, commercial), and the service line of the admission. We also included a severity of illness variable from the APR-DRG Grouper (3M, Maplewood, Minnesota)²⁰ and the number of days spent in the intensive care unit. For the readmission model, we included LOS as a control variable, because individuals with longer LOS had a better opportunity to access the intervention.

For readmissions, the model was estimated by using a probit model, predicting the effect of each of the intervention variables and the control variables on the marginal probability of a readmission. Because patients can appear in both the pre- and postyears, clustered standard errors were used, which correct for the lack of independence from multiple observations from the same individual.²¹ For LOS, a truncated negative binomial model was used. The negative binomial model is a specification for count models with a mass of observations plus a long right tail. The truncation is because zero and negative values for LOS are not possible. The dependent variable represents the number of days the individual was hospitalized. For both models, the reported coefficients represent the marginal effect of the independent variable on the dependent variable. This was calculated using the “margins” command in Stata version 13 (StataCorp LLC, College Station, Texas).

This study investigated the effect of an inpatient smoking cessation program, based on a successful Canadian model, on inpatient readmission rates and LOS. The program showed no effect on 30-day readmission rates or LOS. We see several potential explanations for the absence of a detectable impact.

First, the ITT approach reflected real-world implementation of smoking cessation services. The ITT approach adopts the hospital’s perspective because the hospital will assess overall effectiveness without regard to programmatic limitations. The intervention group for this analysis included individuals who were offered but declined treatment, individuals who accepted treatment but failed to quit smoking, and individuals who both accepted treatment and quit smoking. If the analysis had focused only on the latter group, an effect would have been more likely to be found. Further analysis of the subset of patients who accepted the intervention and quit smoking is warranted. Nevertheless, hospitals cannot expect all inpatient smokers, or even a majority, to embrace an offer of cessation treatment. This also emphasizes the challenges hospitals will face in offering tobacco cessation programs to smokers in a timely way. Reasons for patients not receiving orders varied but included issues with weekend admissions.

Second, the timeframe of the analysis is limited to the inpatient stay (for LOS) and 30 days (for readmission). A longer-term analysis might have found an effect. However, we examined this from the hospital perspective. For the hospital, LOS is a key cost driver; thus, reductions in LOS would create a strong financial incentive for hospitals to implement smoking cessation programs. Similarly, reducing readmissions is now a priority for hospitals because of new Medicare rules that penalize hospitals for readmissions. Thus, the 2 outcomes we examined are outcomes that are financially important to hospitals.

There are several limitations to our analysis. First, the difference-in-differences model assumes that in the absence of treatment, the average change in the dependent variables would have been the same for both the treatment and control groups, also known as the parallel trends assumption. Specification tests showed this assumption was met for the preperiod. Second, our study relies on electronic health record data to identify smokers. However, 93% of individuals who were identified as smokers confirmed their smoking status upon interview. Finally, we looked at all categories of inpatient admissions. Improvement in LOS and short-term readmission rates may be limited to patients admitted for specific conditions, such as cardiovascular and respiratory conditions.

There are a number of plausible reasons for our null finding. First, the “dose” of intervention may have been too weak; that is, the number of smokers who were offered the treatment, accepted the treatment, and adhered to the treatment may have been too low, leading to too few smokers quitting smoking and, thus, no effect of the intervention on our outcomes. This follows directly from the ITT design of the study.²³ This suggests that hospitals who wish to adopt smoking cessation programs need to focus on ensuring a timely offering of treatment and encouragement of uptake by smokers.

A second reason for the null finding may have been the short duration for the NRT, which was only offered for 2 weeks. Research suggests that use of NRT for less than 4 weeks is associated with a reduced likelihood of smoking cessation.²⁴ However, a review of the literature concludes that the duration of NRT is less important than the dosage and the combination of NRT with other forms of smoking cessation therapy.²⁵ It is important to note that this study used NRT; other treatments such as Chantix could have different effectiveness.^26,27 Further research on different treatment approaches, including longer duration of NRT, would be appropriate.

Disclosure

 The authors have no competing interests or conflicts to report. The study was supported by contract number 15FLA68717 from the Colorado Department of Public Health and Environment.

Successful smoking cessation interventions result in substantial gains in health and life expectancy by reducing smoking-related illnesses and preventing premature deaths.^1,2 The Department of Health and Human Services recommends clinicians use hospitalization as “an opportunity to promote smoking cessation’’ and ‘‘to prescribe medications to alleviate withdrawal symptoms”³ because individual readiness to quit may be high during hospitalizations. A meta-analysis of 50 studies (21 from the United States) examining the efficacy of hospital-initiated smoking cessation interventions concluded that smoking cessation support programs that began in the hospital and continued for at least 1 month postdischarge significantly increase the likelihood of patients being smoke-free in the long term.⁴ The most efficacious strategies included counseling and pharmacotherapy rather than counseling alone.³ Most inpatient smoking cessation studies have focused on quit-rates or medical outcomes, while fewer studies have looked at healthcare utilization.

However, previous research has shown that smoking cessation for inpatients has relatively immediate economic and health benefits. Patients who quit smoking during hospitalizations for cardiovascular disease are less likely to be readmitted or to die during follow-up.^5,6 Patients with acute myocardial infarction (AMI), unstable angina, heart failure, and chronic obstructive pulmonary disease who received an inpatient smoking cessation intervention had reductions in inpatient readmission rates.⁷ A 1% reduction in overall smoking rates would lead to an annual reduction of 3,022 hospitalizations for stroke and 1,684 hospitalizations for AMI.⁸ One comprehensive program, the Ottawa Model for Smoking Cessation (OMSC), found that a hospital-initiated intervention increased long-term cessation rates by 15% in cardiac patients and by 11% in general hospital populations.^9,10 The applicability of this result to US healthcare systems is unknown. This paper adds to the existing literature by evaluating the impact of an inpatient smoking cessation program on healthcare utilization among patients hospitalized for any reason, rather than solely focused on those with cardiopulmonary diagnoses.

The current study focuses on an inpatient smoking cessation program at a teaching hospital in the Rocky Mountain region. The hospital implemented a smoking cessation treatment program on July 1, 2013, based on the OMSC. The goal was to identify and support inpatient adult smokers who wanted to make a quit attempt and help them remain smoke-free after discharge. The objective of the current study was to determine the effect of the program on 30-day readmission rates and length of stay (LOS) of the index hospitalization. Although the general cost effectiveness of properly structured smoking cessation programs are well established,^11-13 the healthcare utilization effects of inpatient smoking cessation programs are not well understood.

Data

The study population consists of patients over age 18 who were admitted to the hospital between July 1, 2012, and July 1, 2014. Baseline smoking status was assessed at hospital admission and recorded in Epic (Epic Systems Corporation, Verona, Wisconsin), the electronic medical records system, as a current smoker (every day and some days), former smoker, never smoker, and never assessed. To check the accuracy of recorded smoking status, a random sample of 819 inpatients was selected and contacted via telephone for verification; 93% of Epic-identified smokers confirmed that they were smokers at hospital admission.¹⁴

Intervention

The intervention, which launched July 13, 2014, modified the Epic system to automatically alert providers viewing a tobacco user’s medical record that the patient should receive standardized orders for a bedside consultation with a Tobacco Treatment Specialist (TTS) and a prescription for nicotine replacement therapy (NRT) while in the hospital.¹⁵ Previously, referrals for tobacco treatment were done on an ad-hoc basis by the physician, and NRT was not routinely available. This system-level intervention standardized and automated the referral process. For patients with a bedside consultation order, TTS used a patient-centered approach (motivational interviewing) to explore patients’ motivation to quit smoking and offered NRT to improve comfort and safety while in the hospital. Patients who chose to make a quit attempt received a free 2-week supply of NRT at discharge and 6 months of free follow-up counseling by interactive voice response (IVR) telephone technology that included (a) prerecorded advice keyed to individual patient needs, (b) a warm-transfer option to speak with a live TTS (later dropped), and (c) a collection of patient smoking and cessation treatment measures.¹⁵

Statistical Analysis

We used an intent-to-treat (ITT) framework for the analysis, which considers everyone eligible for the treatment to be in the treatment group. The approach ignores treatment nonacceptance, nonadherence, protocol deviations, withdrawal from treatment, and cessation outcomes, thus providing conservative estimates of outcomes.^16-18

Readmission rates and LOS were estimated by using a “difference-in-differences” model, comparing outcomes between smokers before versus after the introduction of the cessation treatment program with nonsmokers before versus after program introduction. The difference-in-differences method looks at the difference pre-and-post in the exposed group (smokers) and unexposed group (nonsmokers). Subtracting the difference between the 2 groups gives an estimate of the policy effect controlling for background trends.¹⁹ The smoking cessation treatment effect on readmission is measured by the coefficient on the interaction term between the smoking variable and an indicator that the program is operational. The coefficient is the “difference-in-differences.”

Other control variables include demographic factors (gender, age, race), hospitalization payer (Medicare, Medicaid, commercial), and the service line of the admission. We also included a severity of illness variable from the APR-DRG Grouper (3M, Maplewood, Minnesota)²⁰ and the number of days spent in the intensive care unit. For the readmission model, we included LOS as a control variable, because individuals with longer LOS had a better opportunity to access the intervention.

For readmissions, the model was estimated by using a probit model, predicting the effect of each of the intervention variables and the control variables on the marginal probability of a readmission. Because patients can appear in both the pre- and postyears, clustered standard errors were used, which correct for the lack of independence from multiple observations from the same individual.²¹ For LOS, a truncated negative binomial model was used. The negative binomial model is a specification for count models with a mass of observations plus a long right tail. The truncation is because zero and negative values for LOS are not possible. The dependent variable represents the number of days the individual was hospitalized. For both models, the reported coefficients represent the marginal effect of the independent variable on the dependent variable. This was calculated using the “margins” command in Stata version 13 (StataCorp LLC, College Station, Texas).

This study investigated the effect of an inpatient smoking cessation program, based on a successful Canadian model, on inpatient readmission rates and LOS. The program showed no effect on 30-day readmission rates or LOS. We see several potential explanations for the absence of a detectable impact.

First, the ITT approach reflected real-world implementation of smoking cessation services. The ITT approach adopts the hospital’s perspective because the hospital will assess overall effectiveness without regard to programmatic limitations. The intervention group for this analysis included individuals who were offered but declined treatment, individuals who accepted treatment but failed to quit smoking, and individuals who both accepted treatment and quit smoking. If the analysis had focused only on the latter group, an effect would have been more likely to be found. Further analysis of the subset of patients who accepted the intervention and quit smoking is warranted. Nevertheless, hospitals cannot expect all inpatient smokers, or even a majority, to embrace an offer of cessation treatment. This also emphasizes the challenges hospitals will face in offering tobacco cessation programs to smokers in a timely way. Reasons for patients not receiving orders varied but included issues with weekend admissions.

Second, the timeframe of the analysis is limited to the inpatient stay (for LOS) and 30 days (for readmission). A longer-term analysis might have found an effect. However, we examined this from the hospital perspective. For the hospital, LOS is a key cost driver; thus, reductions in LOS would create a strong financial incentive for hospitals to implement smoking cessation programs. Similarly, reducing readmissions is now a priority for hospitals because of new Medicare rules that penalize hospitals for readmissions. Thus, the 2 outcomes we examined are outcomes that are financially important to hospitals.

There are several limitations to our analysis. First, the difference-in-differences model assumes that in the absence of treatment, the average change in the dependent variables would have been the same for both the treatment and control groups, also known as the parallel trends assumption. Specification tests showed this assumption was met for the preperiod. Second, our study relies on electronic health record data to identify smokers. However, 93% of individuals who were identified as smokers confirmed their smoking status upon interview. Finally, we looked at all categories of inpatient admissions. Improvement in LOS and short-term readmission rates may be limited to patients admitted for specific conditions, such as cardiovascular and respiratory conditions.

There are a number of plausible reasons for our null finding. First, the “dose” of intervention may have been too weak; that is, the number of smokers who were offered the treatment, accepted the treatment, and adhered to the treatment may have been too low, leading to too few smokers quitting smoking and, thus, no effect of the intervention on our outcomes. This follows directly from the ITT design of the study.²³ This suggests that hospitals who wish to adopt smoking cessation programs need to focus on ensuring a timely offering of treatment and encouragement of uptake by smokers.

A second reason for the null finding may have been the short duration for the NRT, which was only offered for 2 weeks. Research suggests that use of NRT for less than 4 weeks is associated with a reduced likelihood of smoking cessation.²⁴ However, a review of the literature concludes that the duration of NRT is less important than the dosage and the combination of NRT with other forms of smoking cessation therapy.²⁵ It is important to note that this study used NRT; other treatments such as Chantix could have different effectiveness.^26,27 Further research on different treatment approaches, including longer duration of NRT, would be appropriate.

Disclosure

 The authors have no competing interests or conflicts to report. The study was supported by contract number 15FLA68717 from the Colorado Department of Public Health and Environment.

Successful smoking cessation interventions result in substantial gains in health and life expectancy by reducing smoking-related illnesses and preventing premature deaths.^1,2 The Department of Health and Human Services recommends clinicians use hospitalization as “an opportunity to promote smoking cessation’’ and ‘‘to prescribe medications to alleviate withdrawal symptoms”³ because individual readiness to quit may be high during hospitalizations. A meta-analysis of 50 studies (21 from the United States) examining the efficacy of hospital-initiated smoking cessation interventions concluded that smoking cessation support programs that began in the hospital and continued for at least 1 month postdischarge significantly increase the likelihood of patients being smoke-free in the long term.⁴ The most efficacious strategies included counseling and pharmacotherapy rather than counseling alone.³ Most inpatient smoking cessation studies have focused on quit-rates or medical outcomes, while fewer studies have looked at healthcare utilization.

However, previous research has shown that smoking cessation for inpatients has relatively immediate economic and health benefits. Patients who quit smoking during hospitalizations for cardiovascular disease are less likely to be readmitted or to die during follow-up.^5,6 Patients with acute myocardial infarction (AMI), unstable angina, heart failure, and chronic obstructive pulmonary disease who received an inpatient smoking cessation intervention had reductions in inpatient readmission rates.⁷ A 1% reduction in overall smoking rates would lead to an annual reduction of 3,022 hospitalizations for stroke and 1,684 hospitalizations for AMI.⁸ One comprehensive program, the Ottawa Model for Smoking Cessation (OMSC), found that a hospital-initiated intervention increased long-term cessation rates by 15% in cardiac patients and by 11% in general hospital populations.^9,10 The applicability of this result to US healthcare systems is unknown. This paper adds to the existing literature by evaluating the impact of an inpatient smoking cessation program on healthcare utilization among patients hospitalized for any reason, rather than solely focused on those with cardiopulmonary diagnoses.

The current study focuses on an inpatient smoking cessation program at a teaching hospital in the Rocky Mountain region. The hospital implemented a smoking cessation treatment program on July 1, 2013, based on the OMSC. The goal was to identify and support inpatient adult smokers who wanted to make a quit attempt and help them remain smoke-free after discharge. The objective of the current study was to determine the effect of the program on 30-day readmission rates and length of stay (LOS) of the index hospitalization. Although the general cost effectiveness of properly structured smoking cessation programs are well established,^11-13 the healthcare utilization effects of inpatient smoking cessation programs are not well understood.

Data

The study population consists of patients over age 18 who were admitted to the hospital between July 1, 2012, and July 1, 2014. Baseline smoking status was assessed at hospital admission and recorded in Epic (Epic Systems Corporation, Verona, Wisconsin), the electronic medical records system, as a current smoker (every day and some days), former smoker, never smoker, and never assessed. To check the accuracy of recorded smoking status, a random sample of 819 inpatients was selected and contacted via telephone for verification; 93% of Epic-identified smokers confirmed that they were smokers at hospital admission.¹⁴

Intervention

The intervention, which launched July 13, 2014, modified the Epic system to automatically alert providers viewing a tobacco user’s medical record that the patient should receive standardized orders for a bedside consultation with a Tobacco Treatment Specialist (TTS) and a prescription for nicotine replacement therapy (NRT) while in the hospital.¹⁵ Previously, referrals for tobacco treatment were done on an ad-hoc basis by the physician, and NRT was not routinely available. This system-level intervention standardized and automated the referral process. For patients with a bedside consultation order, TTS used a patient-centered approach (motivational interviewing) to explore patients’ motivation to quit smoking and offered NRT to improve comfort and safety while in the hospital. Patients who chose to make a quit attempt received a free 2-week supply of NRT at discharge and 6 months of free follow-up counseling by interactive voice response (IVR) telephone technology that included (a) prerecorded advice keyed to individual patient needs, (b) a warm-transfer option to speak with a live TTS (later dropped), and (c) a collection of patient smoking and cessation treatment measures.¹⁵

Statistical Analysis

We used an intent-to-treat (ITT) framework for the analysis, which considers everyone eligible for the treatment to be in the treatment group. The approach ignores treatment nonacceptance, nonadherence, protocol deviations, withdrawal from treatment, and cessation outcomes, thus providing conservative estimates of outcomes.^16-18

Readmission rates and LOS were estimated by using a “difference-in-differences” model, comparing outcomes between smokers before versus after the introduction of the cessation treatment program with nonsmokers before versus after program introduction. The difference-in-differences method looks at the difference pre-and-post in the exposed group (smokers) and unexposed group (nonsmokers). Subtracting the difference between the 2 groups gives an estimate of the policy effect controlling for background trends.¹⁹ The smoking cessation treatment effect on readmission is measured by the coefficient on the interaction term between the smoking variable and an indicator that the program is operational. The coefficient is the “difference-in-differences.”

Other control variables include demographic factors (gender, age, race), hospitalization payer (Medicare, Medicaid, commercial), and the service line of the admission. We also included a severity of illness variable from the APR-DRG Grouper (3M, Maplewood, Minnesota)²⁰ and the number of days spent in the intensive care unit. For the readmission model, we included LOS as a control variable, because individuals with longer LOS had a better opportunity to access the intervention.

For readmissions, the model was estimated by using a probit model, predicting the effect of each of the intervention variables and the control variables on the marginal probability of a readmission. Because patients can appear in both the pre- and postyears, clustered standard errors were used, which correct for the lack of independence from multiple observations from the same individual.²¹ For LOS, a truncated negative binomial model was used. The negative binomial model is a specification for count models with a mass of observations plus a long right tail. The truncation is because zero and negative values for LOS are not possible. The dependent variable represents the number of days the individual was hospitalized. For both models, the reported coefficients represent the marginal effect of the independent variable on the dependent variable. This was calculated using the “margins” command in Stata version 13 (StataCorp LLC, College Station, Texas).

This study investigated the effect of an inpatient smoking cessation program, based on a successful Canadian model, on inpatient readmission rates and LOS. The program showed no effect on 30-day readmission rates or LOS. We see several potential explanations for the absence of a detectable impact.

First, the ITT approach reflected real-world implementation of smoking cessation services. The ITT approach adopts the hospital’s perspective because the hospital will assess overall effectiveness without regard to programmatic limitations. The intervention group for this analysis included individuals who were offered but declined treatment, individuals who accepted treatment but failed to quit smoking, and individuals who both accepted treatment and quit smoking. If the analysis had focused only on the latter group, an effect would have been more likely to be found. Further analysis of the subset of patients who accepted the intervention and quit smoking is warranted. Nevertheless, hospitals cannot expect all inpatient smokers, or even a majority, to embrace an offer of cessation treatment. This also emphasizes the challenges hospitals will face in offering tobacco cessation programs to smokers in a timely way. Reasons for patients not receiving orders varied but included issues with weekend admissions.

Second, the timeframe of the analysis is limited to the inpatient stay (for LOS) and 30 days (for readmission). A longer-term analysis might have found an effect. However, we examined this from the hospital perspective. For the hospital, LOS is a key cost driver; thus, reductions in LOS would create a strong financial incentive for hospitals to implement smoking cessation programs. Similarly, reducing readmissions is now a priority for hospitals because of new Medicare rules that penalize hospitals for readmissions. Thus, the 2 outcomes we examined are outcomes that are financially important to hospitals.

There are several limitations to our analysis. First, the difference-in-differences model assumes that in the absence of treatment, the average change in the dependent variables would have been the same for both the treatment and control groups, also known as the parallel trends assumption. Specification tests showed this assumption was met for the preperiod. Second, our study relies on electronic health record data to identify smokers. However, 93% of individuals who were identified as smokers confirmed their smoking status upon interview. Finally, we looked at all categories of inpatient admissions. Improvement in LOS and short-term readmission rates may be limited to patients admitted for specific conditions, such as cardiovascular and respiratory conditions.

There are a number of plausible reasons for our null finding. First, the “dose” of intervention may have been too weak; that is, the number of smokers who were offered the treatment, accepted the treatment, and adhered to the treatment may have been too low, leading to too few smokers quitting smoking and, thus, no effect of the intervention on our outcomes. This follows directly from the ITT design of the study.²³ This suggests that hospitals who wish to adopt smoking cessation programs need to focus on ensuring a timely offering of treatment and encouragement of uptake by smokers.

A second reason for the null finding may have been the short duration for the NRT, which was only offered for 2 weeks. Research suggests that use of NRT for less than 4 weeks is associated with a reduced likelihood of smoking cessation.²⁴ However, a review of the literature concludes that the duration of NRT is less important than the dosage and the combination of NRT with other forms of smoking cessation therapy.²⁵ It is important to note that this study used NRT; other treatments such as Chantix could have different effectiveness.^26,27 Further research on different treatment approaches, including longer duration of NRT, would be appropriate.

Disclosure

 The authors have no competing interests or conflicts to report. The study was supported by contract number 15FLA68717 from the Colorado Department of Public Health and Environment.

Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8^th leading cause of death with 55,227 deaths among adults annually.¹ In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).² This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).^3,4

These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.⁵ We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.

Setting and Patients

We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.⁶ Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.

We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.

For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).⁷ In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).

Definitions of Guideline-Concordant and Discordant Antibiotic Therapy

The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.

Statistical Analysis

Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.

To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.⁸ For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.⁹ We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.¹⁰

We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.

All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).

In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.¹¹ We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.

At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.^12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.¹⁴

There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.

There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.¹⁵ surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,¹⁶ and the majority do not choose HCAP guideline-concordant antibiotics when tested.¹⁷ Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO¹⁸ and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.^19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.⁵ Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.

In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.

Disclosure

 This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.

Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8^th leading cause of death with 55,227 deaths among adults annually.¹ In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).² This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).^3,4

These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.⁵ We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.

Setting and Patients

We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.⁶ Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.

We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.

For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).⁷ In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).

Definitions of Guideline-Concordant and Discordant Antibiotic Therapy

The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.

Statistical Analysis

Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.

To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.⁸ For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.⁹ We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.¹⁰

We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.

All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).

In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.¹¹ We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.

At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.^12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.¹⁴

There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.

There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.¹⁵ surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,¹⁶ and the majority do not choose HCAP guideline-concordant antibiotics when tested.¹⁷ Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO¹⁸ and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.^19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.⁵ Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.

In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.

Disclosure

 This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.

Bacterial pneumonia remains an important cause of morbidity and mortality in the United States, and is the 8^th leading cause of death with 55,227 deaths among adults annually.¹ In 2005, the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) collaborated to update guidelines for hospital-acquired pneumonia (HAP), ventilator-associated pneumonia, and healthcare-associated pneumonia (HCAP).² This broad document outlines an evidence-based approach to diagnostic testing and antibiotic management based on the epidemiology and risk factors for these conditions. The guideline specifies the following criteria for HCAP: hospitalization in the past 90 days, residence in a skilled nursing facility (SNF), home infusion therapy, hemodialysis, home wound care, family members with multidrug resistant organisms (MDRO), and immunosuppressive diseases or medications, with the presumption that these patients are more likely to be harboring MDRO and should thus be treated empirically with broad-spectrum antibiotic therapy. Prior studies have shown that patients with HCAP have a more severe illness, are more likely to have MDRO, are more likely to be inadequately treated, and are at a higher risk for mortality than patients with community-acquired pneumonia (CAP).^3,4

These guidelines are controversial, especially in regard to the recommendations to empirically treat broadly with 2 antibiotics targeting Pseudomonas species, whether patients with HCAP merit broader spectrum coverage than patients with CAP, and whether the criteria for defining HCAP are adequate to predict which patients are harboring MDRO. It has subsequently been proposed that HCAP is more related to CAP than to HAP, and a recent update to the guideline removed recommendations for treatment of HCAP and will be placing HCAP into the guidelines for CAP instead.⁵ We sought to investigate the degree of uptake of the ATS and IDSA guideline recommendations by physicians over time, and whether this led to a change in outcomes among patients who met the criteria for HCAP.

Setting and Patients

We identified patients discharged between July 1, 2007, and November 30, 2011, from 488 US hospitals that participated in the Premier database (Premier Inc., Charlotte, North Carolina), an inpatient database developed for measuring quality and healthcare utilization. The database is frequently used for healthcare research and has been described previously.⁶ Member hospitals are in all regions of the US and are generally reflective of US hospitals. This database contains multiple data elements, including sociodemographic information, International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) diagnosis and procedure codes, hospital and physician information, source of admission, and discharge status. It also includes a date-stamped log of all billed items and services, including diagnostic tests, medications, and other treatments. Because the data do not contain identifiable information, the institutional review board at our medical center determined that this study did not constitute human subjects research.

We included all patients aged ≥18 years with a principal diagnosis of pneumonia or with a secondary diagnosis of pneumonia paired with a principal diagnosis of respiratory failure, acute respiratory distress syndrome, respiratory arrest, sepsis, or influenza. Patients were excluded if they were transferred to or from another acute care institution, had a length of stay of 1 day or less, had cystic fibrosis, did not have a chest radiograph, or did not receive antibiotics within 48 hours of admission.

For each patient, we extracted age, gender, principal diagnosis, comorbidities, and the specialty of the attending physician. Comorbidities were identified from ICD-9-CM secondary diagnosis codes and Diagnosis Related Groups by using Healthcare Cost and Utilization Project Comorbidity Software, version 3.1, based on the work of Elixhauser (Agency for Healthcare Research and Quality, Rockville, Maryland).⁷ In order to ensure that patients had HCAP, we required the presence of ≥1 HCAP criteria, including hospitalization in the past 90 days, hemodialysis, admission from an SNF, or immune suppression (which was derived from either a secondary diagnosis for neutropenia, hematological malignancy, organ transplant, acquired immunodeficiency virus, or receiving immunosuppressant drugs or corticosteroids [equivalent to ≥20 mg/day of prednisone]).

Definitions of Guideline-Concordant and Discordant Antibiotic Therapy

The ATS and IDSA guidelines recommended the following antibiotic combinations for HCAP: an antipseudomonal cephalosporin or carbapenem or a beta-lactam/lactamase inhibitor, plus an antipseudomonal quinolone or aminoglycoside, plus an antibiotic with activity versus methicillin resistant Staphylococcus aureus (MRSA), such as vancomycin or linezolid. Based on these guidelines, we defined the receipt of fully guideline-concordant antibiotics as 2 recommended antibiotics for Pseudomonas species plus 1 for MRSA administered by the second day of admission. Partially guideline-concordant antibiotics were defined as 1 recommended antibiotic for Pseudomonas species plus 1 for MRSA by the second day of hospitalization. Guideline-discordant antibiotics were defined as all other combinations.

Statistical Analysis

Descriptive statistics on patient characteristics are presented as frequency, proportions for categorical factors, and median with interquartile range (IQR) for continuous variables for the full cohort and by treatment group, defined as fully or partially guideline-concordant antibiotic therapy or discordant therapy. Hospital rates of fully guideline-concordant treatment are presented overall and by hospital characteristics. The association of hospital characteristics with rates of fully guideline-concordant therapy were assessed by using 1-way analysis of variance tests.

To assess trends across hospitals for the association between the use of guideline-concordant therapy and mortality, progression to respiratory failure as measured by the late initiation of invasive mechanical ventilation (day 3 or later), and the length of stay among survivors, we divided the 4.5-year study period into 9 intervals of 6 months each; 292 hospitals that submitted data for all 9 time points were examined in this analysis. Based on the distribution of length of stay in the first time period, we created an indicator variable for extended length of stay with length of stay at or above the 75th percentile, defined as extended. For each hospital at each 6-month interval, we then computed risk-standardized guideline-concordant treatment (RS-treatment) rates and risk-standardized in-hospital outcome rates similar to methods used by the Centers for Medicare and Medicaid Services for public reporting.⁸ For each hospital at each time interval, we estimated a predicted rate of guideline-concordant treatment as the sum of predicted probabilities of guideline-concordant treatment from patient factors and the random intercept for the hospital in which they were admitted. We then calculated the expected rate of guideline-concordant treatment as the sum of expected probabilities of treatment received from patient factors only. RS-treatment was then calculated as the ratio of predicted to expected rates multiplied by the overall unadjusted mean treatment rate from all patients.⁹ We repeated the same modeling strategy to calculate risk-standardized outcome (RS-outcome) rates for each hospital across all time points. All models were adjusted for patient demographics and comorbidities. Similar models using administrative data have moderate discrimination for mortality.¹⁰

We then fit mixed-effects linear models with random hospital intercept and slope across time for the RS-treatment and outcome rates, respectively. From these models, we estimated the mean slope for RS-treatment and for RS-outcome over time. In addition, we estimated a slope or trend over time for each hospital for treatment and for outcome and evaluated the correlation between the treatment and outcome trends.

All analyses were performed using the Statistical Analysis System version 9.4 (SAS Institute Inc., Cary, NC) and STATA release 13 (StataCorp, LLC, College Station, Texas).

In this large, retrospective cohort study, we found that there was a substantial gap between the empiric antibiotics recommended by the ATS and IDSA guidelines and the empiric antibiotics that patients actually received. Over the study period, we saw an increased adherence to guidelines, in spite of growing evidence that HCAP risk factors do not adequately predict which patients are at risk for infection with an MDRO.¹¹ We used this change in antibiotic prescribing behavior over time to determine if there was a clinical impact on patient outcomes and found that at the hospital level, there were no improvements in mortality, excess length of stay, or progression to respiratory failure despite a doubling in guideline-concordant antibiotic use.

At least 2 other large studies have assessed the association between guideline-concordant therapy and outcomes in HCAP.^12,13 Both found that guideline-concordant therapy was associated with increased mortality, despite propensity matching. Both were conducted at the individual patient level by using administrative data, and results were likely affected by unmeasured clinical confounders, with sicker patients being more likely to receive guideline-concordant therapy. Our focus on the outcomes at the hospital level avoids this selection bias because the overall severity of illness of patients at any given hospital would not be expected to change over the study period, while physician uptake of antibiotic prescribing guidelines would be expected to increase over time. Determining the correlation between increases in guideline adherence and changes in patient outcome may offer a better assessment of the impact of guideline adherence. In this regard, our results are similar to those achieved by 1 quality improvement collaborative that was aimed at increasing guideline concordant therapy in ICUs. Despite an increase in guideline concordance from 33% to 47% of patients, they found no change in overall mortality.¹⁴

There were several limitations to our study. We did not have access to microbiologic data, so we were unable to determine which patients had MDRO infection or determine antibiotic-pathogen matching. However, the treating physicians in our study population presumably did not have access to this data at the time of treatment either because the time period we examined was within the first 48 hours of hospitalization, the interval during which cultures are incubating and the patients are being treated empirically. In addition, there may have been HCAP patients that we failed to identify, such as patients who were admitted in the past 90 days to a hospital that does not submit data to Premier. However, it is unlikely that prescribing for such patients should differ systematically from what we observed. While the database draws from 488 hospitals nationwide, it is possible that practices may be different at facilities that are not contained within the Premier database, such as Veterans Administration Hospitals. Similarly, we did not have readings for chest x-rays; hence, there could be some patients in the dataset who did not have pneumonia. However, we tried to overcome this by including only those patients with a principal diagnosis of pneumonia or sepsis with a secondary pneumonia diagnosis, a chest x-ray, and antibiotics administered within the first 48 hours of admission.

There are likely several reasons why so few HCAP patients in our study received guideline-concordant antibiotics. A lack of knowledge about the ATS and IDSA guidelines may have impacted the physicians in our study population. El-Solh et al.¹⁵ surveyed physicians about the ATS-IDSA guidelines 4 years after publication and found that only 45% were familiar with the document. We found that the rate of prescribing at least partially guideline-concordant antibiotics rose steadily over time, supporting the idea that the newness of the guidelines was 1 barrier. Additionally, prior studies have shown that many physicians may not agree with or choose to follow guidelines, with only 20% of physicians indicating that guidelines have a major impact on their clinical decision making,¹⁶ and the majority do not choose HCAP guideline-concordant antibiotics when tested.¹⁷ Alternatively, clinicians may not follow the guidelines because of a belief that the HCAP criteria do not adequately indicate patients who are at risk for MDRO. Previous studies have demonstrated the relative inability of HCAP risk factors to predict patients who harbor MDRO¹⁸ and suggest that better tools such as clinical scoring systems, which include not only the traditional HCAP risk factors but also prior exposure to antibiotics, prior culture data, and a cumulative assessment of both intrinsic and extrinsic factors, could more accurately predict MDRO and lead to a more judicious use of broad-spectrum antimicrobial agents.^19-25 Indeed, these collective findings have led the authors of the recently updated guidelines to remove HCAP as a clinical entity from the hospital-acquired or ventilator-associated pneumonia guidelines and place them instead in the upcoming updated guidelines on the management of CAP.⁵ Of these 3 explanations, the lack of familiarity fits best with our observation that guideline-concordant therapy increased steadily over time with no evidence of reaching a plateau. Ironically, as consensus was building that HCAP is a poor marker for MDROs, routine empiric treatment with vancomycin and piperacillin-tazobactam (“vanco and zosyn”) have become routine in many hospitals. Additional studies are needed to know if this trend has stabilized or reversed.

In conclusion, clinicians in our large, nationally representative sample treated the majority of HCAP patients as though they had CAP. Although there was an increase in the administration of guideline-concordant therapy over time, this increase was not associated with improved outcomes. This study supports the growing consensus that HCAP criteria do not accurately predict which patients benefit from broad-spectrum antibiotics for pneumonia, and most patients fare well with antibiotics targeting common community-acquired organisms.

Disclosure

 This work was supported by grant # R01HS018723 from the Agency for Healthcare Research and Quality. Dr. Lagu is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Lindenauer is supported by grant K24HL132008 from the National Heart, Lung, and Blood Institute. The funding agency had no role in the data acquisition, analysis, or manuscript preparation for this study. Drs. Haessler and Rothberg had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Haessler, Lagu, Lindenauer, Skiest, Zilberberg, Higgins, and Rothberg conceived of the study and analyzed and interpreted the data. Dr. Lindenauer acquired the data. Dr. Pekow and Ms. Priya carried out the statistical analyses. Dr. Haessler drafted the manuscript. All authors critically reviewed the manuscript for accuracy and integrity. All authors certify no potential conflicts of interest. Preliminary results from this study were presented in oral and poster format at IDWeek in 2012 and 2013.

Patient satisfaction and the ability to effectively communicate with hospitalized patients has become a core tenet to providing high-quality healthcare. Over the past few decades, medicine has gradually moved away from many paternalistic practices, and the profession has sought to engage patients as true partners in their own care. It is in this setting that effective communication has risen to be a key factor in the patient and provider relationship. It has also become a closely monitored quality metric tied to financial incentives and penalties. Most importantly, it has been well documented that failures in communication are a frequent cause of adverse events that compromise the ability of healthcare providers to provide safe and effective care.¹ It is in this climate that healthcare systems have worked to implement solutions designed to engage patients and their families to improve their healthcare experience. These solutions vary from low to high tech and include patient whiteboards, provider face cards, and web-based patient portals. Despite the numerous innovative solutions being implemented by hospitalists, studies supporting their effectiveness are few. There continues to be limited evidence on the value of these practices and whether they positively impact the desired outcomes of patient satisfaction and engagement.

In this issue of the Journal of Hospital Medicine, Goyal et al.² performed a systematic review to evaluate whether the use of bedside visual tools for hospitalized medical patients impacts patient satisfaction, patient–provider communication, and provider identification and understanding of roles. The authors were able to identify 16 studies that evaluated the use of these tools, which included provider face cards and whiteboards. The majority of the studies reviewed showed a positive effect on provider identification, understanding providers’ role, and patient satisfaction. The authors found that of the tools evaluated, whiteboards and picture-based techniques were the most effective visually based interventions. However, the authors also highlighted the difficulty in identifying 1 optimal approach to the use of these tools as a result of variations in content, format, and outcome measurement.

Variation in the use of visual tools to improve communication and patient satisfaction limits the ability to identify and evaluate the most effective approaches to their use. Without a streamlined approach, these tools may not produce the desired effect of improving patient and provider communication, which is essential in providing high-quality inpatient care and ensuring patient satisfaction. It has been documented that many patients cannot even identify their providers in the hospital setting, which limits the ability of the patient to be fully engaged in decisions made about their care.³ In addition, substantial portions of hospitalized patients do not understand their plan of care.⁴ Patients’ understanding of their plan of care is essential for patients to provide informed consent for hospital treatments and better prepare them to assume their own care after discharge, with a full understanding of their diagnosis.⁵ It has become increasingly clear that healthcare providers must incorporate effective approaches in their daily workflow to address these findings.

Aside from patient satisfaction and engagement, the effect communications failures have on patient safety have been evaluated and recognized. From the National Academy of Medicine’s report emphasizing patient-centered care to the addition of patients’ active engagement in their care as a National Patient Safety Goal by The Joint Commission, the medical field has committed to a continued focus in this area.^5,6

The business case can also be made for identifying effective tools that improve patient satisfaction and patient–provider communication. Private and public health insurance providers have incentivized high performance in these areas and have now begun to levy penalties for underperformers. As patients’ level of satisfaction and engagement continue to be assessed via patient surveys, healthcare systems continue to search for effective practices to improve performance in patient-perceived provider communication. Patients’ reporting of their assessment of nurse and physician communication through questions such as “How often did nurses/doctors explain things in a way you could understand?” will continue to be a moving target requiring future studies of effective interventions

Are visual aids the effective tools that hospitals need to improve communication and patient satisfaction, or are they merely decorations? The whiteboard provides an excellent example of the effectiveness that can be seen with the use of these tools. Used to improve patient-provider communication in medicine, the whiteboard has become almost ubiquitous in patient hospital rooms.⁷ It is now an expected aspect of hospital design and has inspired the development of higher tech solutions, including patient tablets and media walls. It is known to enhance the interaction for both the provider and patient and facilitate the exchange of complicated medical information within an anxiety prone environment in a simple manner by using short phrases or drawings.⁶ Yet, there is a scarcity of strong evidence to support the most effective approach to the use of whiteboards in improving patient satisfaction and communication. Standardizing how the whiteboard is used during the patient interaction will allow for the effectiveness of this tool to be realized and evaluated and prevent it from becoming another ornamental fixture on our hospital walls.

The systematic review by Goyal et al.² is a necessary step in the evaluation of common communication tools for their effectiveness and ability to improve patient satisfaction. This exhaustive review of key studies in this area is an excellent addition to the current literature, which has a paucity of extensive evaluations of these approaches. It provides an important signal that visual tools are more than decorative and can be effective when a streamlined approach is utilized. It highlights the importance of identifying effective best practices for the use of these tools that can be studied empirically and subsequently disseminated for widespread use. Continued work is necessary to fill this void and to enable healthcare professionals to provide the highest level of safe, effective, and engaging care that our patients deserve.

Disclosure

The authors have no conflicts of interest.

Patient satisfaction and the ability to effectively communicate with hospitalized patients has become a core tenet to providing high-quality healthcare. Over the past few decades, medicine has gradually moved away from many paternalistic practices, and the profession has sought to engage patients as true partners in their own care. It is in this setting that effective communication has risen to be a key factor in the patient and provider relationship. It has also become a closely monitored quality metric tied to financial incentives and penalties. Most importantly, it has been well documented that failures in communication are a frequent cause of adverse events that compromise the ability of healthcare providers to provide safe and effective care.¹ It is in this climate that healthcare systems have worked to implement solutions designed to engage patients and their families to improve their healthcare experience. These solutions vary from low to high tech and include patient whiteboards, provider face cards, and web-based patient portals. Despite the numerous innovative solutions being implemented by hospitalists, studies supporting their effectiveness are few. There continues to be limited evidence on the value of these practices and whether they positively impact the desired outcomes of patient satisfaction and engagement.

In this issue of the Journal of Hospital Medicine, Goyal et al.² performed a systematic review to evaluate whether the use of bedside visual tools for hospitalized medical patients impacts patient satisfaction, patient–provider communication, and provider identification and understanding of roles. The authors were able to identify 16 studies that evaluated the use of these tools, which included provider face cards and whiteboards. The majority of the studies reviewed showed a positive effect on provider identification, understanding providers’ role, and patient satisfaction. The authors found that of the tools evaluated, whiteboards and picture-based techniques were the most effective visually based interventions. However, the authors also highlighted the difficulty in identifying 1 optimal approach to the use of these tools as a result of variations in content, format, and outcome measurement.

Variation in the use of visual tools to improve communication and patient satisfaction limits the ability to identify and evaluate the most effective approaches to their use. Without a streamlined approach, these tools may not produce the desired effect of improving patient and provider communication, which is essential in providing high-quality inpatient care and ensuring patient satisfaction. It has been documented that many patients cannot even identify their providers in the hospital setting, which limits the ability of the patient to be fully engaged in decisions made about their care.³ In addition, substantial portions of hospitalized patients do not understand their plan of care.⁴ Patients’ understanding of their plan of care is essential for patients to provide informed consent for hospital treatments and better prepare them to assume their own care after discharge, with a full understanding of their diagnosis.⁵ It has become increasingly clear that healthcare providers must incorporate effective approaches in their daily workflow to address these findings.

Aside from patient satisfaction and engagement, the effect communications failures have on patient safety have been evaluated and recognized. From the National Academy of Medicine’s report emphasizing patient-centered care to the addition of patients’ active engagement in their care as a National Patient Safety Goal by The Joint Commission, the medical field has committed to a continued focus in this area.^5,6

The business case can also be made for identifying effective tools that improve patient satisfaction and patient–provider communication. Private and public health insurance providers have incentivized high performance in these areas and have now begun to levy penalties for underperformers. As patients’ level of satisfaction and engagement continue to be assessed via patient surveys, healthcare systems continue to search for effective practices to improve performance in patient-perceived provider communication. Patients’ reporting of their assessment of nurse and physician communication through questions such as “How often did nurses/doctors explain things in a way you could understand?” will continue to be a moving target requiring future studies of effective interventions

Are visual aids the effective tools that hospitals need to improve communication and patient satisfaction, or are they merely decorations? The whiteboard provides an excellent example of the effectiveness that can be seen with the use of these tools. Used to improve patient-provider communication in medicine, the whiteboard has become almost ubiquitous in patient hospital rooms.⁷ It is now an expected aspect of hospital design and has inspired the development of higher tech solutions, including patient tablets and media walls. It is known to enhance the interaction for both the provider and patient and facilitate the exchange of complicated medical information within an anxiety prone environment in a simple manner by using short phrases or drawings.⁶ Yet, there is a scarcity of strong evidence to support the most effective approach to the use of whiteboards in improving patient satisfaction and communication. Standardizing how the whiteboard is used during the patient interaction will allow for the effectiveness of this tool to be realized and evaluated and prevent it from becoming another ornamental fixture on our hospital walls.

The systematic review by Goyal et al.² is a necessary step in the evaluation of common communication tools for their effectiveness and ability to improve patient satisfaction. This exhaustive review of key studies in this area is an excellent addition to the current literature, which has a paucity of extensive evaluations of these approaches. It provides an important signal that visual tools are more than decorative and can be effective when a streamlined approach is utilized. It highlights the importance of identifying effective best practices for the use of these tools that can be studied empirically and subsequently disseminated for widespread use. Continued work is necessary to fill this void and to enable healthcare professionals to provide the highest level of safe, effective, and engaging care that our patients deserve.

Disclosure

The authors have no conflicts of interest.

Patient satisfaction and the ability to effectively communicate with hospitalized patients has become a core tenet to providing high-quality healthcare. Over the past few decades, medicine has gradually moved away from many paternalistic practices, and the profession has sought to engage patients as true partners in their own care. It is in this setting that effective communication has risen to be a key factor in the patient and provider relationship. It has also become a closely monitored quality metric tied to financial incentives and penalties. Most importantly, it has been well documented that failures in communication are a frequent cause of adverse events that compromise the ability of healthcare providers to provide safe and effective care.¹ It is in this climate that healthcare systems have worked to implement solutions designed to engage patients and their families to improve their healthcare experience. These solutions vary from low to high tech and include patient whiteboards, provider face cards, and web-based patient portals. Despite the numerous innovative solutions being implemented by hospitalists, studies supporting their effectiveness are few. There continues to be limited evidence on the value of these practices and whether they positively impact the desired outcomes of patient satisfaction and engagement.

In this issue of the Journal of Hospital Medicine, Goyal et al.² performed a systematic review to evaluate whether the use of bedside visual tools for hospitalized medical patients impacts patient satisfaction, patient–provider communication, and provider identification and understanding of roles. The authors were able to identify 16 studies that evaluated the use of these tools, which included provider face cards and whiteboards. The majority of the studies reviewed showed a positive effect on provider identification, understanding providers’ role, and patient satisfaction. The authors found that of the tools evaluated, whiteboards and picture-based techniques were the most effective visually based interventions. However, the authors also highlighted the difficulty in identifying 1 optimal approach to the use of these tools as a result of variations in content, format, and outcome measurement.

Variation in the use of visual tools to improve communication and patient satisfaction limits the ability to identify and evaluate the most effective approaches to their use. Without a streamlined approach, these tools may not produce the desired effect of improving patient and provider communication, which is essential in providing high-quality inpatient care and ensuring patient satisfaction. It has been documented that many patients cannot even identify their providers in the hospital setting, which limits the ability of the patient to be fully engaged in decisions made about their care.³ In addition, substantial portions of hospitalized patients do not understand their plan of care.⁴ Patients’ understanding of their plan of care is essential for patients to provide informed consent for hospital treatments and better prepare them to assume their own care after discharge, with a full understanding of their diagnosis.⁵ It has become increasingly clear that healthcare providers must incorporate effective approaches in their daily workflow to address these findings.

Aside from patient satisfaction and engagement, the effect communications failures have on patient safety have been evaluated and recognized. From the National Academy of Medicine’s report emphasizing patient-centered care to the addition of patients’ active engagement in their care as a National Patient Safety Goal by The Joint Commission, the medical field has committed to a continued focus in this area.^5,6

The business case can also be made for identifying effective tools that improve patient satisfaction and patient–provider communication. Private and public health insurance providers have incentivized high performance in these areas and have now begun to levy penalties for underperformers. As patients’ level of satisfaction and engagement continue to be assessed via patient surveys, healthcare systems continue to search for effective practices to improve performance in patient-perceived provider communication. Patients’ reporting of their assessment of nurse and physician communication through questions such as “How often did nurses/doctors explain things in a way you could understand?” will continue to be a moving target requiring future studies of effective interventions

Are visual aids the effective tools that hospitals need to improve communication and patient satisfaction, or are they merely decorations? The whiteboard provides an excellent example of the effectiveness that can be seen with the use of these tools. Used to improve patient-provider communication in medicine, the whiteboard has become almost ubiquitous in patient hospital rooms.⁷ It is now an expected aspect of hospital design and has inspired the development of higher tech solutions, including patient tablets and media walls. It is known to enhance the interaction for both the provider and patient and facilitate the exchange of complicated medical information within an anxiety prone environment in a simple manner by using short phrases or drawings.⁶ Yet, there is a scarcity of strong evidence to support the most effective approach to the use of whiteboards in improving patient satisfaction and communication. Standardizing how the whiteboard is used during the patient interaction will allow for the effectiveness of this tool to be realized and evaluated and prevent it from becoming another ornamental fixture on our hospital walls.

The systematic review by Goyal et al.² is a necessary step in the evaluation of common communication tools for their effectiveness and ability to improve patient satisfaction. This exhaustive review of key studies in this area is an excellent addition to the current literature, which has a paucity of extensive evaluations of these approaches. It provides an important signal that visual tools are more than decorative and can be effective when a streamlined approach is utilized. It highlights the importance of identifying effective best practices for the use of these tools that can be studied empirically and subsequently disseminated for widespread use. Continued work is necessary to fill this void and to enable healthcare professionals to provide the highest level of safe, effective, and engaging care that our patients deserve.

Disclosure

The authors have no conflicts of interest.

If you haven’t started reporting quality data for the Merit-Based Incentive Payment System (MIPS), there’s still time to avoid a 4% cut to your Medicare payments.

Under the Pick Your Pace approach being offered this year, the Centers for Medicare & Medicaid Services allows clinicians to test the system by reporting on one quality measure for one patient through paper-based claims. Be sure to append a Quality Data Code (QDC) to the claim form for care provided up to Dec. 31, 2017, in order to avoid a penalty in payment year 2019.

Consider this measure:

Measure #113: Colorectal Cancer Screening

This measure is aimed at capturing the percentage of patients aged 50-75 years who were screened for colorectal cancer.

What you need to do: The patient should be screened for colorectal cancer during calendar 2017 (or specified alternative time frame) using an appropriate test. Document the screening and results in the medical record.

Appropriate tests include:

• Fecal occult blood test (FOBT) during the performance period (calendar 2017).
• Flexible sigmoidoscopy during the performance period or the 4 years prior.
• Colonoscopy during the performance period or the 9 years prior.
• Computed tomography (CT) colonography during the performance period or the 4 years prior.
• Fecal immunochemical DNA test (FIT-DNA) during the measurement period or the 2 years prior.

Eligible cases include patients aged 50-75 years of age on the date of the encounter and a patient encounter during the performance period. Applicable codes include (CPT or HCPCS): 99201, 99202, 99203, 99204, 99205, 99212, 99213, 99214, 99215, 99341, 99342, 99343, 99344, 99345, 99347, 99348, 99349, 99350, G0402, G0438, G0439.

To get credit under MIPS, be sure to include a QDC that shows that you successfully performed the measure or had a good reason for not doing so. For instance, CPT II 3017F indicates that colorectal cancer screening results were documented and reviewed. The exclusion code G9711 should be used for patients with a diagnosis or past history of total colectomy or colorectal cancer.

CMS has a full list measures available for claims-based reporting at qpp.cms.gov. The American Medical Association has also created a step-by-step guide for reporting on one quality measure.

Certain clinicians are exempt from reporting and do not face a penalty under MIPS:

• Those who enrolled in Medicare for the first time during a performance period.
• Those who have Medicare Part B allowed charges of $30,000 or less.
• Those who have 100 or fewer Medicare Part B patients.
• Those who are significantly participating in an Advanced Alternative Payment Model (APM).

If you haven’t started reporting quality data for the Merit-Based Incentive Payment System (MIPS), there’s still time to avoid a 4% cut to your Medicare payments.

Under the Pick Your Pace approach being offered this year, the Centers for Medicare & Medicaid Services allows clinicians to test the system by reporting on one quality measure for one patient through paper-based claims. Be sure to append a Quality Data Code (QDC) to the claim form for care provided up to Dec. 31, 2017, in order to avoid a penalty in payment year 2019.

Consider this measure:

Measure #113: Colorectal Cancer Screening

This measure is aimed at capturing the percentage of patients aged 50-75 years who were screened for colorectal cancer.

What you need to do: The patient should be screened for colorectal cancer during calendar 2017 (or specified alternative time frame) using an appropriate test. Document the screening and results in the medical record.

Appropriate tests include:

• Fecal occult blood test (FOBT) during the performance period (calendar 2017).
• Flexible sigmoidoscopy during the performance period or the 4 years prior.
• Colonoscopy during the performance period or the 9 years prior.
• Computed tomography (CT) colonography during the performance period or the 4 years prior.
• Fecal immunochemical DNA test (FIT-DNA) during the measurement period or the 2 years prior.

Eligible cases include patients aged 50-75 years of age on the date of the encounter and a patient encounter during the performance period. Applicable codes include (CPT or HCPCS): 99201, 99202, 99203, 99204, 99205, 99212, 99213, 99214, 99215, 99341, 99342, 99343, 99344, 99345, 99347, 99348, 99349, 99350, G0402, G0438, G0439.

To get credit under MIPS, be sure to include a QDC that shows that you successfully performed the measure or had a good reason for not doing so. For instance, CPT II 3017F indicates that colorectal cancer screening results were documented and reviewed. The exclusion code G9711 should be used for patients with a diagnosis or past history of total colectomy or colorectal cancer.

CMS has a full list measures available for claims-based reporting at qpp.cms.gov. The American Medical Association has also created a step-by-step guide for reporting on one quality measure.

Certain clinicians are exempt from reporting and do not face a penalty under MIPS:

• Those who enrolled in Medicare for the first time during a performance period.
• Those who have Medicare Part B allowed charges of $30,000 or less.
• Those who have 100 or fewer Medicare Part B patients.
• Those who are significantly participating in an Advanced Alternative Payment Model (APM).

If you haven’t started reporting quality data for the Merit-Based Incentive Payment System (MIPS), there’s still time to avoid a 4% cut to your Medicare payments.

Under the Pick Your Pace approach being offered this year, the Centers for Medicare & Medicaid Services allows clinicians to test the system by reporting on one quality measure for one patient through paper-based claims. Be sure to append a Quality Data Code (QDC) to the claim form for care provided up to Dec. 31, 2017, in order to avoid a penalty in payment year 2019.

Consider this measure:

Measure #113: Colorectal Cancer Screening

This measure is aimed at capturing the percentage of patients aged 50-75 years who were screened for colorectal cancer.

What you need to do: The patient should be screened for colorectal cancer during calendar 2017 (or specified alternative time frame) using an appropriate test. Document the screening and results in the medical record.

Appropriate tests include:

• Fecal occult blood test (FOBT) during the performance period (calendar 2017).
• Flexible sigmoidoscopy during the performance period or the 4 years prior.
• Colonoscopy during the performance period or the 9 years prior.
• Computed tomography (CT) colonography during the performance period or the 4 years prior.
• Fecal immunochemical DNA test (FIT-DNA) during the measurement period or the 2 years prior.

Eligible cases include patients aged 50-75 years of age on the date of the encounter and a patient encounter during the performance period. Applicable codes include (CPT or HCPCS): 99201, 99202, 99203, 99204, 99205, 99212, 99213, 99214, 99215, 99341, 99342, 99343, 99344, 99345, 99347, 99348, 99349, 99350, G0402, G0438, G0439.

To get credit under MIPS, be sure to include a QDC that shows that you successfully performed the measure or had a good reason for not doing so. For instance, CPT II 3017F indicates that colorectal cancer screening results were documented and reviewed. The exclusion code G9711 should be used for patients with a diagnosis or past history of total colectomy or colorectal cancer.

CMS has a full list measures available for claims-based reporting at qpp.cms.gov. The American Medical Association has also created a step-by-step guide for reporting on one quality measure.

Certain clinicians are exempt from reporting and do not face a penalty under MIPS:

• Those who enrolled in Medicare for the first time during a performance period.
• Those who have Medicare Part B allowed charges of $30,000 or less.
• Those who have 100 or fewer Medicare Part B patients.
• Those who are significantly participating in an Advanced Alternative Payment Model (APM).

The FDA’s MedWatch program safety labeling changes for boxed warnings are compiled quarterly for drugs and therapeutic biologics where important changes have been made to the safety information. These and other label changes are searchable in the Drug Safety Labeling Changes (SLC) database, where data are available to the public in downloadable and searchable formats. Boxed warnings are ordinarily used to highlight either adverse reactions so serious in proportion to the potential bene t from the drug that it is essential that it be considered in assessing the risks and bene ts of using the drug; or serious adverse reactions that can be prevented/reduced in frequency or severity by appropriate use of the drug; or FDA approved the drug with restrictions to ensure safe use because FDA concluded that the drug can be safely used only if distribution or use is restricted.

PRANDIMET (METFORMIN HYDROCHLORIDE; REPAGLINIDE):

• Edited boxed warning April 7, 2017

Post-marketing cases of metforminassociated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin-associated lactic acidosis is often subtle, accompanied only by nonspecific symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anyhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue PrandiMet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

AVANDAMET (METFORMIN HYDROCHLORIDE; ROSIGLITAZONE MALEATE):

• Edited and updated boxed warning April 7, 2017

WARNING: CONGESTIVE HEART FAILURE and LACTIC ACIDOSIS

Rosiglitazone maleate: CONGESTIVE HEART FAILURE

• Thiazolidinediones, including rosiglitazone, cause or exacerbate congestive heart failure in some patients. After initiation of Avandamet, and after dose increases, observe patients carefully for signs and symptoms of heart failure (including excessive, rapid weight gain, dyspnea, and/or edema). If these signs and symptoms develop, the heart failure should be managed according to current standards of care. Furthermore, discontinuation or dose reduction of Avandamet must be considered.
• Avandamet is not recommended in patients with symptomatic heart failure. Initiation of Avandamet in patients with established NYHA Class III or IV heart failure is contraindicated.

Metformin hydrochloride: LACTIC ACIDOSIS

• Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin- associated lactic acidosis often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin- associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/L), anion gap acidosis (without evidence of ketonuria or ketonemia), and increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.
• Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase
  inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment. Steps to reduce the risk of and manage metformin-associated lactic acidosis in these highrisk groups are provided in the Full Prescribing Information.
• If metformin-associated lactic acidosis is suspected, immediately discontinue Avandamet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

OPTIRAY (160, 240, 300, 320, AND 350):

• Edited boxed warning April 7, 2017

PLR conversion, addition of the following:

WARNING: NOT FOR INTRATHECAL USE

Inadvertent intrathecal administration may cause death, convulsions, cerebral hemorrhage, coma, paralysis, arachnoiditis, acute renal failure, cardiac arrest, seizures, rhabdomyolysis, hyperthermia, and brain edema.

GLUMETZA (METFORMIN HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

LACTIC ACIDOSIS

Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metforminassociated lactic acidosis is often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio, and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue Glumetza and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

SABRIL (VIGABATRIN):

• Edited boxed warning April 7, 2017

WARNING: PERMANENT VISION LOSS

Because of the risk of permanent vision loss, Sabril is available only through a restricted program under a Risk Evaluation and Mitigation Strategy (REMS) called the Vigabatrin REMS Program. Further information is available at www.vigabatrinREMS.com or 1-866-244-8175.

VALCYTE (VALGANCICLOVIR HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

WARNING: HEMATOLOGIC TOXICITY, IMPAIRMENT OF FERTILITY, FETAL TOXICITY, MUTAGENESIS AND CARCINOGENESIS

• Hematologic Toxicity: Severe leukopenia, neutropenia, anemia, thrombocytopenia, pancytopenia, and bone marrow failure including aplastic anemia have been reported in patients treated with Valcyte.
• Impairment of Fertility: Based on animal data, Valcyte may cause temporary or permanent inhibition of spermatogenesis in males and suppression of fertility in females.

The FDA’s MedWatch program safety labeling changes for boxed warnings are compiled quarterly for drugs and therapeutic biologics where important changes have been made to the safety information. These and other label changes are searchable in the Drug Safety Labeling Changes (SLC) database, where data are available to the public in downloadable and searchable formats. Boxed warnings are ordinarily used to highlight either adverse reactions so serious in proportion to the potential bene t from the drug that it is essential that it be considered in assessing the risks and bene ts of using the drug; or serious adverse reactions that can be prevented/reduced in frequency or severity by appropriate use of the drug; or FDA approved the drug with restrictions to ensure safe use because FDA concluded that the drug can be safely used only if distribution or use is restricted.

PRANDIMET (METFORMIN HYDROCHLORIDE; REPAGLINIDE):

• Edited boxed warning April 7, 2017

Post-marketing cases of metforminassociated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin-associated lactic acidosis is often subtle, accompanied only by nonspecific symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anyhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue PrandiMet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

AVANDAMET (METFORMIN HYDROCHLORIDE; ROSIGLITAZONE MALEATE):

• Edited and updated boxed warning April 7, 2017

WARNING: CONGESTIVE HEART FAILURE and LACTIC ACIDOSIS

Rosiglitazone maleate: CONGESTIVE HEART FAILURE

• Thiazolidinediones, including rosiglitazone, cause or exacerbate congestive heart failure in some patients. After initiation of Avandamet, and after dose increases, observe patients carefully for signs and symptoms of heart failure (including excessive, rapid weight gain, dyspnea, and/or edema). If these signs and symptoms develop, the heart failure should be managed according to current standards of care. Furthermore, discontinuation or dose reduction of Avandamet must be considered.
• Avandamet is not recommended in patients with symptomatic heart failure. Initiation of Avandamet in patients with established NYHA Class III or IV heart failure is contraindicated.

Metformin hydrochloride: LACTIC ACIDOSIS

• Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin- associated lactic acidosis often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin- associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/L), anion gap acidosis (without evidence of ketonuria or ketonemia), and increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.
• Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase
  inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment. Steps to reduce the risk of and manage metformin-associated lactic acidosis in these highrisk groups are provided in the Full Prescribing Information.
• If metformin-associated lactic acidosis is suspected, immediately discontinue Avandamet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

OPTIRAY (160, 240, 300, 320, AND 350):

• Edited boxed warning April 7, 2017

PLR conversion, addition of the following:

WARNING: NOT FOR INTRATHECAL USE

Inadvertent intrathecal administration may cause death, convulsions, cerebral hemorrhage, coma, paralysis, arachnoiditis, acute renal failure, cardiac arrest, seizures, rhabdomyolysis, hyperthermia, and brain edema.

GLUMETZA (METFORMIN HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

LACTIC ACIDOSIS

Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metforminassociated lactic acidosis is often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio, and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue Glumetza and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

SABRIL (VIGABATRIN):

• Edited boxed warning April 7, 2017

WARNING: PERMANENT VISION LOSS

Because of the risk of permanent vision loss, Sabril is available only through a restricted program under a Risk Evaluation and Mitigation Strategy (REMS) called the Vigabatrin REMS Program. Further information is available at www.vigabatrinREMS.com or 1-866-244-8175.

VALCYTE (VALGANCICLOVIR HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

WARNING: HEMATOLOGIC TOXICITY, IMPAIRMENT OF FERTILITY, FETAL TOXICITY, MUTAGENESIS AND CARCINOGENESIS

• Hematologic Toxicity: Severe leukopenia, neutropenia, anemia, thrombocytopenia, pancytopenia, and bone marrow failure including aplastic anemia have been reported in patients treated with Valcyte.
• Impairment of Fertility: Based on animal data, Valcyte may cause temporary or permanent inhibition of spermatogenesis in males and suppression of fertility in females.

The FDA’s MedWatch program safety labeling changes for boxed warnings are compiled quarterly for drugs and therapeutic biologics where important changes have been made to the safety information. These and other label changes are searchable in the Drug Safety Labeling Changes (SLC) database, where data are available to the public in downloadable and searchable formats. Boxed warnings are ordinarily used to highlight either adverse reactions so serious in proportion to the potential bene t from the drug that it is essential that it be considered in assessing the risks and bene ts of using the drug; or serious adverse reactions that can be prevented/reduced in frequency or severity by appropriate use of the drug; or FDA approved the drug with restrictions to ensure safe use because FDA concluded that the drug can be safely used only if distribution or use is restricted.

PRANDIMET (METFORMIN HYDROCHLORIDE; REPAGLINIDE):

• Edited boxed warning April 7, 2017

Post-marketing cases of metforminassociated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin-associated lactic acidosis is often subtle, accompanied only by nonspecific symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anyhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue PrandiMet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

AVANDAMET (METFORMIN HYDROCHLORIDE; ROSIGLITAZONE MALEATE):

• Edited and updated boxed warning April 7, 2017

WARNING: CONGESTIVE HEART FAILURE and LACTIC ACIDOSIS

Rosiglitazone maleate: CONGESTIVE HEART FAILURE

• Thiazolidinediones, including rosiglitazone, cause or exacerbate congestive heart failure in some patients. After initiation of Avandamet, and after dose increases, observe patients carefully for signs and symptoms of heart failure (including excessive, rapid weight gain, dyspnea, and/or edema). If these signs and symptoms develop, the heart failure should be managed according to current standards of care. Furthermore, discontinuation or dose reduction of Avandamet must be considered.
• Avandamet is not recommended in patients with symptomatic heart failure. Initiation of Avandamet in patients with established NYHA Class III or IV heart failure is contraindicated.

Metformin hydrochloride: LACTIC ACIDOSIS

• Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metformin- associated lactic acidosis often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin- associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/L), anion gap acidosis (without evidence of ketonuria or ketonemia), and increased lactate/pyruvate ratio; and metformin plasma levels generally > 5 mcg/mL.
• Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase
  inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment. Steps to reduce the risk of and manage metformin-associated lactic acidosis in these highrisk groups are provided in the Full Prescribing Information.
• If metformin-associated lactic acidosis is suspected, immediately discontinue Avandamet and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

OPTIRAY (160, 240, 300, 320, AND 350):

• Edited boxed warning April 7, 2017

PLR conversion, addition of the following:

WARNING: NOT FOR INTRATHECAL USE

Inadvertent intrathecal administration may cause death, convulsions, cerebral hemorrhage, coma, paralysis, arachnoiditis, acute renal failure, cardiac arrest, seizures, rhabdomyolysis, hyperthermia, and brain edema.

GLUMETZA (METFORMIN HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

LACTIC ACIDOSIS

Postmarketing cases of metformin-associated lactic acidosis have resulted in death, hypothermia, hypotension, and resistant bradyarrhythmias. The onset of metforminassociated lactic acidosis is often subtle, accompanied only by nonspeci c symptoms such as malaise, myalgias, respiratory distress, somnolence, and abdominal pain. Metformin-associated lactic acidosis was characterized by elevated blood lactate levels (> 5 mmol/Liter), anion gap acidosis (without evidence of ketonuria or ketonemia), an increased lactate/pyruvate ratio, and metformin plasma levels generally > 5 mcg/mL.

Risk factors for metformin-associated lactic acidosis include renal impairment, concomitant use of certain drugs (eg, carbonic anhydrase inhibitors such as topiramate), age 65 years old or greater, having a radiological study with contrast, surgery and other procedures, hypoxic states (eg, acute congestive heart failure), excessive alcohol intake, and hepatic impairment.

Steps to reduce the risk of and manage metformin-associated lactic acidosis in these high risk groups are provided in the full prescribing information.

If metformin-associated lactic acidosis is suspected, immediately discontinue Glumetza and institute general supportive measures in a hospital setting. Prompt hemodialysis is recommended.

SABRIL (VIGABATRIN):

• Edited boxed warning April 7, 2017

WARNING: PERMANENT VISION LOSS

Because of the risk of permanent vision loss, Sabril is available only through a restricted program under a Risk Evaluation and Mitigation Strategy (REMS) called the Vigabatrin REMS Program. Further information is available at www.vigabatrinREMS.com or 1-866-244-8175.

VALCYTE (VALGANCICLOVIR HYDROCHLORIDE):

• Edited boxed warning April 7, 2017

WARNING: HEMATOLOGIC TOXICITY, IMPAIRMENT OF FERTILITY, FETAL TOXICITY, MUTAGENESIS AND CARCINOGENESIS

• Hematologic Toxicity: Severe leukopenia, neutropenia, anemia, thrombocytopenia, pancytopenia, and bone marrow failure including aplastic anemia have been reported in patients treated with Valcyte.
• Impairment of Fertility: Based on animal data, Valcyte may cause temporary or permanent inhibition of spermatogenesis in males and suppression of fertility in females.

The selective dopamine norepinephrine reuptake inhibitor JZP-110 was effective at treating excessive sleepiness in obstructive sleep apnea patients, according to an abstract on a study to be presented at the CHEST annual meeting.

In a 12-week, phase 3 trial, adult patients with obstructive sleep apnea were randomized to receive placebo or once-daily JZP-110 at dosages of 37.5 mg, 75 mg, 150 mg, or 300 mg. A total of 459 patients were included in the final analysis.

At the end of the trial, the mean reduction from baseline on a maintenance of wakefulness test was 0.2 minutes for patients in the placebo group, 4.7 minutes in the 37.5-mg group, 9.1 minutes in the 75-mg group, 11 minutes in the 150-mg group, and 13 minutes in the 300-mg group. Mean changes in Epworth Sleepiness Scale scores were –3.3 for patients in the placebo group, –5.1 in the 37.5-mg group, –5 in the 75-mg group, –7.7 in the 150-mg group, and –7.9 in the 300-mg group.

A significantly higher rate of patients in the 75-mg (72.4%), 150-mg (89.7%), and 300-mg (88.7%) groups improved on the Patient Global Impression of Change scale, compared with patients in the placebo (49.1%) and 37.5-mg (55.4%) groups.

Just under 68% of patients who received JZP-110 experienced at least one adverse event, compared with 47.9% of patients who received placebo. The most common adverse events were headache, nausea, decreased appetite, and anxiety. Six serious adverse events were reported over the study period, but none was related to JZP-110.

The study is scheduled to be presented on Sunday, Oct. 29, from 1:30 p.m. to 1:45 p.m. in Room 601A of the Toronto Convention Centre South Building as part of the “Obstructive Sleep Apnea: Insights & Management” session, which will run from 1:30 p.m. to 3 p.m.

[email protected]

The selective dopamine norepinephrine reuptake inhibitor JZP-110 was effective at treating excessive sleepiness in obstructive sleep apnea patients, according to an abstract on a study to be presented at the CHEST annual meeting.

In a 12-week, phase 3 trial, adult patients with obstructive sleep apnea were randomized to receive placebo or once-daily JZP-110 at dosages of 37.5 mg, 75 mg, 150 mg, or 300 mg. A total of 459 patients were included in the final analysis.

At the end of the trial, the mean reduction from baseline on a maintenance of wakefulness test was 0.2 minutes for patients in the placebo group, 4.7 minutes in the 37.5-mg group, 9.1 minutes in the 75-mg group, 11 minutes in the 150-mg group, and 13 minutes in the 300-mg group. Mean changes in Epworth Sleepiness Scale scores were –3.3 for patients in the placebo group, –5.1 in the 37.5-mg group, –5 in the 75-mg group, –7.7 in the 150-mg group, and –7.9 in the 300-mg group.

A significantly higher rate of patients in the 75-mg (72.4%), 150-mg (89.7%), and 300-mg (88.7%) groups improved on the Patient Global Impression of Change scale, compared with patients in the placebo (49.1%) and 37.5-mg (55.4%) groups.

Just under 68% of patients who received JZP-110 experienced at least one adverse event, compared with 47.9% of patients who received placebo. The most common adverse events were headache, nausea, decreased appetite, and anxiety. Six serious adverse events were reported over the study period, but none was related to JZP-110.

The study is scheduled to be presented on Sunday, Oct. 29, from 1:30 p.m. to 1:45 p.m. in Room 601A of the Toronto Convention Centre South Building as part of the “Obstructive Sleep Apnea: Insights & Management” session, which will run from 1:30 p.m. to 3 p.m.

[email protected]

The selective dopamine norepinephrine reuptake inhibitor JZP-110 was effective at treating excessive sleepiness in obstructive sleep apnea patients, according to an abstract on a study to be presented at the CHEST annual meeting.

In a 12-week, phase 3 trial, adult patients with obstructive sleep apnea were randomized to receive placebo or once-daily JZP-110 at dosages of 37.5 mg, 75 mg, 150 mg, or 300 mg. A total of 459 patients were included in the final analysis.

At the end of the trial, the mean reduction from baseline on a maintenance of wakefulness test was 0.2 minutes for patients in the placebo group, 4.7 minutes in the 37.5-mg group, 9.1 minutes in the 75-mg group, 11 minutes in the 150-mg group, and 13 minutes in the 300-mg group. Mean changes in Epworth Sleepiness Scale scores were –3.3 for patients in the placebo group, –5.1 in the 37.5-mg group, –5 in the 75-mg group, –7.7 in the 150-mg group, and –7.9 in the 300-mg group.

A significantly higher rate of patients in the 75-mg (72.4%), 150-mg (89.7%), and 300-mg (88.7%) groups improved on the Patient Global Impression of Change scale, compared with patients in the placebo (49.1%) and 37.5-mg (55.4%) groups.

Just under 68% of patients who received JZP-110 experienced at least one adverse event, compared with 47.9% of patients who received placebo. The most common adverse events were headache, nausea, decreased appetite, and anxiety. Six serious adverse events were reported over the study period, but none was related to JZP-110.

The study is scheduled to be presented on Sunday, Oct. 29, from 1:30 p.m. to 1:45 p.m. in Room 601A of the Toronto Convention Centre South Building as part of the “Obstructive Sleep Apnea: Insights & Management” session, which will run from 1:30 p.m. to 3 p.m.

[email protected]