Mapping the care transition from hospital to skilled nursing facility

Characterizing infection prevention programs and urinary tract infection prevention practices in nursing homes: A mixed-methods study

OBJECTIVE

US policies require robust nursing home (NH) infection prevention and control (IPC) programs to ensure safe care. We assessed IPC resources and practices related to catheter and non-catheter-associated urinary tract infection (CAUTI and UTI) prevention among NHs.

METHODS

We conducted a mixed-methods study from April 2018 through November 2019. Quantitative surveys assessed NH IPC program resources, practices, and communication during resident transfer. Semistructured qualitative interviews focused on IPC programs, CAUTI and UTI prevention practices, and resident transfer processes. Using a matrix as an analytic tool, findings from the quantitative survey data were combined with the qualitative data in the form of a joint display.

RESULTS

Representatives from 51 NHs completed surveys; interviews were conducted with 13 participants from 7 NHs. Infection preventionists (IPs) had limited experience and/or additional roles, and in 36.7% of NHs, IPs had no specific IPC training. IP turnover was often mentioned during interviews. Most facilities were aware of their CAUTI and UTI rates and reported using prevention practices, such as hydration (85.7%) or nurse-initiated catheter discontinuation (65.3%). Qualitative interviewees confirmed use of these practices and expressed additional concerns about overuse of urine testing and antibiotics. Although transfer sheets were used by 84% to communicate about infections, the information received was described as suboptimal.

CONCLUSIONS

NHs identified IP challenges related to turnover, limited education, and serving multiple roles. However, most NHs reported awareness of their CAUTI and UTI rates as well as their use of prevention practices. Importantly, we identified opportunities to enhance communication between NHs and hospitals to improve resident care and safety.

Reducing risks in complex care transitions in rural areas: a grounded theory

PURPOSE

Although previous research indicates that care transitions differ between rural and urban areas, the knowledge of challenges related to care transitions in rural areas appears limited. This study aimed to provide a deeper understanding of what registered nurses' perceive as the main concerns in care transitions from hospital care to home healthcare in rural areas, and how they handle these during the care transition process.

METHODS

A Constructivist Grounded Theory method based on individual interviews with 21 registered nurses.

RESULTS

The main concern in the transition process was "Care coordination in a complex context". The complexity stemmed from several environmental and organizational factors, creating a messy and fragmented context for registered nurses to navigate. The core category "Actively communicating to reduce patient safety risks" was explained by the three categories- "Collaborating on expected care needs", "Anticipating obstacles" and "Timing the departure".

CONCLUSIONS

The study shows a very complex and stressed process that includes several organizations and actors. Reducing risks during the transition process can be facilitated by clear guidelines, tools for communication across organizations and sufficient staffing.

Mortality and healthcare resource utilisation after cardiac arrest in the United States - A 10-year nationwide analysis prior to the COVID-19 pandemic

AIM

Understanding the public health burden of cardiac arrest (CA) is important to inform healthcare policies, particularly during healthcare crises such as the COVID-19 pandemic. This study aimed to analyse outcomes of in-hospital mortality and healthcare resource utilisation in adult patients with CA in the United States over the last decade prior to the COVID-19 pandemic.

METHODS

The United States (US) National Inpatient Sample was utilised to identify hospitalised adult patients with CA between 2010 and 2019. Logistic and Poisson regression models were used to analyse outcomes by adjusting for 47 confounders.

RESULTS

248,754 adult patients with CA (without "Do Not Resuscitate"-orders) were included in this study, out of which 57.5% were male. In-hospital mortality was high with 51.2% but improved significantly from 58.3% in 2010 to 46.4% in 2019 (P < 0.001). Particularly, elderly patients, non-white patients and patients requiring complex therapy had a higher mortality rate. Although the average hospital LOS decreased by 11%, hospital expenses have increased by 13% between 2010 and 2019 (each P < 0.001), presumably due to more frequent use of mechanical circulatory support (MCS, e.g. ECMO from 2.6% to 8.7% or Impella® micro-axial flow pump from 1.8% to 14.2%). Strong disparities existed among patient age groups and ethnicities across the US. Of note, the number of young adults with CA and opioid-induced CA has almost doubled within the study period.

CONCLUSION

Over the last ten years prior to the COVID-19 pandemic, CA-related survival has incrementally improved with shorter hospitalisations and increased medical expenses, while strong disparities existed among different age groups and ethnicities. National standards for CA surveillance should be considered to identify trends and differences in CA treatment to allow for standardised medical care.

Hospitalists Improving Transitions of Care Through Virtual Collaborative Rounding with Skilled Nursing Facilities-the HiToC SNF Study

BACKGROUND

Over one in five Medicare patients discharged to skilled nursing facilities (SNFs) are re-hospitalized within 30 days of discharge. Poor communication between the hospital and SNF upon hospital discharge is frequently cited as the most common cause of readmission.

AIM

The goal of this program was to assess the ability of a weekly post-discharge hospitalist led virtual rounding program to augment the written discharge summary sent to SNFs.

SETTING

Two academic hospitals and six SNFs in Baltimore, MD.

PARTICIPANTS

Hospitalists and medical directors or directors of nursing from the partner SNF.

PROGRAM DESCRIPTION

During weekly encounters, the hospitalist and SNF providers discussed the clinical status, discharge medications, treatment plan, and follow-up care of all discharged patients. The intervention took place from July 2021 to December 2021.

PROGRAM EVALUATION

During the study, 544 patients were discussed in a post-discharge virtual encounter. After the discussions, hospitalists identified clinically significant errors in 124 discharge summaries. A survey of participating hospitalists and SNF medical and nursing leadership indicated the intervention was thought to improve care transitions.

DISCUSSION

Our innovation was successful in identifying errors in discharge summaries and was thought to improve the transition of care by participating SNF and hospitalist providers.

Improving Communication From Hospital to Skilled Nursing Facility Through Standardized Hand-Off: A Quality Improvement Project

Background: Inadequate hand-off communication from hospital to skilled nursing facility (SNF) hinders SNF nurses’ ability to prepare for specific patient needs, including prescriptions for critical medications, such as controlled medications and intravenous (IV) antibiotics, resulting in delayed medication administration. This project aims to improve hand-off communication from hospital to SNF by utilizing a standardized hand-off tool. This project was conducted in an inpatient, 50-bed, post-hospital skilled nursing care unit of a local SNF. The participants included all 32 staff nurses employed by the SNF. Methods: Lewin’s change management theory (CMT) guided this quality improvement (QI) project. Baseline assessment included a one-month chart review of 76 patient charts that was conducted to assess the disparities related to ineffective hand-off and medication delays in the SNF before intervention. The wait time for the availability of prescriptions for controlled medications and IV antibiotics, and delays in medication administration were assessed. Intervention: Multiple randomly selected hospital-to-SNF hand-offs were observed. Semistructured interviews with all staff nurses were conducted using open-ended questions about hand-off structure and process matters. Data gathered from observation and interviews were used to create the standardized hand-off tool used in this project. In-service training on hand-off tool utilization for SNF nurses was conducted. Champions for each shift were cultivated to assist with project implementation. Results: After six weeks of implementation, a chart review of 101 patient charts was conducted to evaluate the effects of the hand-off tool on the wait time on the availability of prescriptions for controlled medications and IV antibiotics, and medication administration. The wait time of prescriptions availability during the hospital-to-SNF transition was decreased by 79% for controlled medications, with an associated 52.9% reduction in late administration, and decreased by 94% for IV antibiotics, with a 77.8% reduction in late administration. Conclusion: The use of standardized hand-off resulted in improved communication during the hospital-to-SNF hand-off and significantly decreased the wait time for the availability of prescriptions for controlled medications and IV antibiotics. Integrating standardized hand-off into the SNF policies can help sustain improved communication, medication management, and patient transition from hospital to SNF.

Factors Influencing Admission Decisions in Skilled Nursing Facilities: A Retrospective Quantitative Study (Preprint)

BACKGROUND

Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database.

OBJECTIVE

Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems.

METHODS

We extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process.

RESULTS

When analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category "Diseases of the Musculoskeletal System" are least often denied whereas those with diagnoses within the "Mental Illness" category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas "medicaid" holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts.

CONCLUSIONS

While many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients' needs.

Development of a workflow process mapping protocol to inform the implementation of regional patient navigation programs in breast oncology

BACKGROUND

Implementing city-wide patient navigation processes that support patients across the continuum of cancer care is impeded by a lack of standardized tools to integrate workflows and reduce gaps in care. The authors present an actionable workflow process mapping protocol for navigation process planning and improvement based on methods developed for the Translating Research Into Practice study.

METHODS

Key stakeholders at each study site were identified through existing community partnerships, and data on each site's navigation processes were collected using mixed methods through a series of team meetings. The authors used Health Quality Ontario's Quality Improvement Guide, service design principles, and key stakeholder input to map the collected data onto a template structured according to the case-management model.

RESULTS

Data collection and process mapping exercises resulted in a 10-step protocol that includes: 1) workflow mapping procedures to guide data collection on the series of activities performed by health care personnel that comprise a patient's navigation experience, 2) a site survey to assess program characteristics, 3) a semistructured interview guide to assess care coordination workflows, 4) a site-level swim lane workflow process mapping template, and 5) a regional high-level process mapping template to aggregate data from multiple site-level process maps.

CONCLUSIONS

This iterative, participatory approach to data collection and process mapping can be used by improvement teams to streamline care coordination, ultimately improving the design and delivery of an evidence-based navigation model that spans multiple treatment modalities and multiple health systems in a metropolitan area. This protocol is presented as an actionable toolkit so the work may be replicated to support other quality-improvement initiatives and efforts to design truly patient-centered breast cancer treatment experiences.

LAY SUMMARY

Evidence-based patient navigation in breast cancer care requires the integration of services through each phase of cancer treatment. The Translating Research Into Practice study aims to implement patient navigation for patients with breast cancer who are at risk for delays and are seeking care across 6 health systems in Boston, Massachusetts. The authors designed a 10-step protocol outlining procedures and tools that support a systematic assessment for health systems that want to implement breast cancer patient navigation services for patients who are at risk for treatment delays.

30-Day Readmission Reduction in a Skilled Facility Population Through Pharmacist-Driven Medication Reconciliation

BACKGROUND

Transitions of care can be difficult to manage and if not performed properly, can lead to increased readmissions and poor outcomes. Transitions are more complex when patients are discharged to skilled nursing facilities.

PURPOSE

We assessed the impact of pharmacist-led initiatives, including medication reconciliation, on readmission rates between an academic medical center and a local skilled nursing facility (SNF).

METHODS

We conducted a two-phase quality improvement project focusing on pharmacist-led medication reconciliation at different points in the transition process. All-cause 30-day readmission rates, medication reconciliation completion rates, and total pharmacist interventions were compared between the 2 groups.

RESULTS

The combined intervention and baseline cohorts resulted in a 29.8% relative reduction (14.5% vs. 20.6%) in readmission rates. Medication reconciliation was completed on 93.8% of SNF admitted patients in the first phase and 97.7% of patients in the second phase. Pharmacist interventions per reconciliation were 2.39 in the first phase compared with 1.82 in the second phase.

CONCLUSION

Pharmacist-led medication reconciliation can contribute to reduction of hospital readmissions from SNFs and is an essential part of the SNF transition process.

Written discharge communication of diagnostic and decision-making information for persons living with dementia during hospital to skilled nursing facility transitions

Hospital-to-skilled nursing facility (SNF) transitions constitute a vulnerable point in care for people with dementia and often precede important care decisions. These decisions necessitate accurate diagnostic/decision-making information, including dementia diagnosis, power of attorney for health care (POAHC), and code status; however, inter-setting communication during hospital-to-SNF transitions is suboptimal. This retrospective cohort study examined omissions of diagnostic/decision-making information in written discharge communication during hospital-to-SNF transitions. Omission rates were 22% for dementia diagnosis, 82% and 88% for POAHC and POAHC activation respectively, and 70% for code status. Findings highlight the need to clarify and intervene upon causes of hospital-to-SNF communication gaps.

Outcomes of a Nursing Home-to-Community Care Transition Program

OBJECTIVES

Most transitional care initiatives to reduce rehospitalization have focused on the transition that occurs between a patient's hospital discharge and return home. However, many patients are discharged from a skilled nursing facility (SNF) to their homes. The goal was to evaluate the effectiveness of the Mayo Clinic Care Transitions (MCCT) program (hereafter called program) among patients discharged from SNFs to their homes.

DESIGN

Propensity-matched control-intervention trial.

INTERVENTION

Patients in the intervention group received care management following nursing stay (a home visit and nursing phone calls).

SETTING AND PARTICIPANTS

Patients enrolled after discharge from an SNF to home were matched to patients who did not receive intervention because of refusal, program capacity, or distance. Patients were aged ≥60 years, at high risk for hospitalization, and discharged from an SNF.

METHODS

Program enrollees were matched through propensity score to nonenrollees on the basis of age, sex, comorbid health burden, and mortality risk score. Conditional logistic regression analysis examined 30-day hospitalization and emergency department (ED) use; Cox proportional hazards analyses examined 180-day hospital stay and ED use.

RESULTS

Each group comprised 160 patients [mean (standard deviation) age, 85.4 (7.4) years]. Thirty-day hospitalization and ED rates were 4.4% and 10.0% in the program group and 3.8% and 10.0% in the group with usual care (P = .76 for hospitalization; P > .99 for ED). At 180 days, hospitalization and ED rates were 30.6% and 46.3% for program patients compared with 11.3% and 25.0% in the comparison group (P < .001).

CONCLUSIONS AND IMPLICATIONS

We found no evidence of reduced hospitalization or ED visits by program patients vs the comparison group. Such findings are crucial because they illustrate how aggressive stabilization care within the SNF may mitigate the program role. Furthermore, we found higher ED and hospitalization rates at 180 days in program patients than the comparison group.

Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model

BACKGROUND

Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructured text.

OBJECTIVE

To present an NLP model that predicts nonhome discharge and a point-of-care implementation.

METHODS

We retrospectively collected age, preoperative notes, and radiology reports from 595 adults who underwent meningioma resection in an academic center from 1995 to 2015. A total of 32 algorithms were trained with the data; the 3 best performing algorithms were combined to form an ensemble. Predictive ability, assessed by area under the receiver operating characteristic curve (AUC) and calibration, was compared to a previously published model utilizing 52 neurosurgeon-selected variables. We then built a multi-institutional model by incorporating notes from 693 patients at another center into algorithm training. Permutation importance was used to analyze the relative importance of each input to model performance. Word clouds and non-negative matrix factorization were used to analyze predictive features of text.

RESULTS

The single-institution NLP model predicted nonhome discharge with AUC of 0.80 (95% CI = 0.74-0.86) on internal and 0.76 on holdout validation compared to AUC of 0.77 (95% CI = 0.73-0.81) and 0.74 for the 52-variable ensemble. The multi-institutional model performed similarly well with AUC = 0.78 (95% CI = 0.74-0.81) on internal and 0.76 on holdout validation. Preoperative notes most influenced predictions. The model is available at http://nlp-home.insds.org.

CONCLUSION

ML and NLP are underutilized in neurosurgery. Here, we construct a multi-institutional NLP model that predicts nonhome discharge.

Process mapping in healthcare: a systematic review

INTRODUCTION

Process mapping (PM) supports better understanding of complex systems and adaptation of improvement interventions to their local context. However, there is little research on its use in healthcare. This study (i) proposes a conceptual framework outlining quality criteria to guide the effective implementation, evaluation and reporting of PM in healthcare; (ii) reviews published PM cases to identify context and quality of PM application, and the reported benefits of using PM in healthcare.

METHODS

We developed the conceptual framework by reviewing methodological guidance on PM and empirical literature on its use in healthcare improvement interventions. We conducted a systematic review of empirical literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Inclusion criteria were: full text empirical study; describing the process through which PM has been applied in a healthcare setting; published in English. Databases searched are: Medline, Embase, HMIC-Health Management Information Consortium, CINAHL-Cumulative Index to Nursing and Allied Health Literature, Scopus. Two independent reviewers extracted and analysed data. Each manuscript underwent line by line coding. The conceptual framework was used to evaluate adherence of empirical studies to the identified PM quality criteria. Context in which PM is used and benefits of using PM were coded using an inductive thematic analysis approach.

RESULTS

The framework outlines quality criteria for each PM phase: (i) preparation, planning and process identification, (ii) data and information gathering, (iii) process map generation, (iv) analysis, (v) taking it forward. PM is used in a variety of settings and approaches to improvement. None of the reviewed studies (N = 105) met all ten quality criteria; 7% were compliant with 8/10 or 9/10 criteria. 45% of studies reported that PM was generated through multi-professional meetings and 15% reported patient involvement. Studies highlighted the value of PM in navigating the complexity characterising healthcare improvement interventions.

CONCLUSION

The full potential of PM is inhibited by variance in reporting and poor adherence to underpinning principles. Greater rigour in the application of the method is required. We encourage the use and further development of the proposed framework to support training, application and reporting of PM.

TRIAL REGISTRATION

Prospero ID: CRD42017082140.