Comparative Perspectives on Geriatrics-Surgery Co-Management Program by Specialty and Staff Role

Co-management programs between geriatrics and surgical specialties have gained popularity in the last few years. Little is known about how these programs are perceived across surgical specialties and staff roles. We conducted a mixed methods study to assess perspectives on a geriatrics-surgery co-management program (GSCP) at a hospital where geriatricians co-manage patients 65 or older admitted to Orthopedic Trauma, General Trauma, and Neurosurgery. We used semi-structured interviews (n=13) and online surveys (n=45) to explore program value, facilitators, use, understanding, and impact by specialty and staff roles (physicians, advanced practice providers, nurses, case managers, social workers). Interview transcripts were analyzed using qualitative thematic analysis, and survey data were analyzed using Kruskal-Wallis, ANOVA, and Fisher’s exact tests. Interviews revealed three themes: 1) GSCP is valued because of geriatricians’ expertise in older adults, relationship with patients and families, and skill in addressing social determinants of health; 2) GSCP facilitators include consistent availability of geriatricians, clear communication, and collaboration via shared data-driven goals; and 3) GSCP use varies by surgical specialty and role depending on expertise and patient complexity. Survey data analysis affirmed interview themes and showed significant differences (p-values<0.05) between perspectives of surgical specialties and roles on GSCP use, understanding, impact, and which specialty should manage specific clinical issues. Findings suggest that while there are similarities across surgical specialties and roles regarding the value of, and facilitators for, a GSCP, specialties and roles differ in use, understanding, and perceived program impact on care. These findings suggest strategies for optimizing this intervention across groups.

Entry level caseload expectations and perception of full caseload capability during student physical therapist terminal clinical experiences: A pilot study

Purpose: The purpose of this study was to explore expected student physical therapist (PT) full caseload expectations across and within clinical settings and identify factors Clinical Instructor’s (CI) routinely report as contributing to their assessment of a student’s ability to manage a full caseload. Methods: A cross-sectional electronic survey design was used to collect data from CIs for student PTs in Michigan. A sample of convenience was utilized. Results: CIs (n=128) from six settings participated in this study. Respondents reported 32% of their employers had established caseload expectations for new graduate and student PTs. Within an 8-hour day, CIs considered a full student caseload measured in billable units to be 26 in outpatient ortho, 22.5 in outpatient neuro, 29 in outpatient mixed, 17.5 in paediatric, 18.5 in acute care, and 21.9 in inpatient rehab settings. Within an 8-hour day, CIs considered a full student caseload measured in patients per day to be 8.8 in outpatient ortho, 6.9 in outpatient neuro, 8.5 in outpatient mixed, 5.4 in paediatric, 7.1 in acute care, and 4.5 in inpatient rehab settings. Student capability was considered by 80% of CIs when determining student caseload. CIs reported patient complexity and accuracy of clinical reasoning as the most influential in determining a student’s capability to manage a full caseload. The ability to implement and retain feedback was reported as least influential. Conclusion: Most respondents indicated their site lacked defined and differing expectations for student PTs. The CIs consistently reported considering student capability of carrying a full caseload when making determinations of student performance on the CPI and were most influenced by patient complexity and clinical reasoning accuracy. CIs reported a range of full caseload productivity expectations for students both within and across settings, which may contribute to inconsistent assessment of student performance on the CPI. A poster presentation of this work was presented at APTA Educational Leadership Conference 2019.

How do healthcare providers construe patient complexity? A qualitative study of multimorbidity in HIV outpatient clinical practice

IntroductionPatient complexity is an increasingly used concept in clinical practice, policy debates and medical research. Yet the literature lacks a clear definition of its meaning and drivers from the health provider’s perspective. This shortcoming is problematic for clinical practice and medical education in the light of a rising number of multimorbid patients and the need for future healthcare providers that are adequately trained in treating complex patients.ObjectivesTo develop an empirically grounded framework of healthcare providers’ perceptions of patient complexity and to characterise the relationship between case complexity, care complexity and provider experience as complexity-contributing factors.DesignQualitative study based on semistructured in-depth interviews with healthcare practitioners.SettingA Swiss hospital-based HIV outpatient clinic.ParticipantsA total of 31 healthcare providers participated. Participants volunteered to take part and comprised 17 nurses, 8 junior physicians (interns) and 6 senior physicians (residents, fellows and attendings).ResultsPerceived patient complexity arises from the combination of case complexity drivers, the provider’s perceived controllability, and a set of complexity moderators at the levels of the patient, the care provider and the broader care context. We develop a conceptual framework that outlines key relationships among these complexity-contributing factors and present 10 key questions to help guide medical professionals in making complexity more explicit and more manageable in daily practice.ConclusionsThe framework presented in this study helps to advance a shared understanding of patient complexity. Our findings inform curriculum design and the teaching of essential skills to medical students in areas characterised by high patient complexity such as general internal medicine and geriatrics. From a policy perspective, our findings have important implications for the design of more effective healthcare interventions for complex patients.

Future of machine learning in paediatrics

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the ‘National Health Service Long Term Plan 2019’. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

Do Electronic Health Record Systems Increase Medicare Reimbursements? The Moderating Effect of the Recovery Audit Program

Electronic health record (EHR) systems allow physicians to automate the process of entering patient data relative to manual entry in traditional paper-based records. However, such automated data entry can lead to increased reimbursement requests by hospitals from Medicare by overstating the complexity of patients. The EHR module that has been alleged to increase reimbursements is the Computerized Physician Order Entry (CPOE) system, which populates patient charts with default templates and allows physicians to copy and paste data from previous charts of the patient and other patients’ records. To combat increased reimbursements by hospitals from Medicare, the Centers for Medicare & Medicaid Services implemented the Recovery Audit Program first as a pilot in six states between 2005 and 2009 and then, nationwide in the entire United States in 2010. We examine whether the adoption of CPOE systems by hospitals is associated with an increase in reported patient complexity and if the Recovery Audit Program helped to attenuate this relationship. We find that the adoption of CPOE systems significantly increases patient complexity reported by hospitals, corresponding to an estimate of $1 billion increase in Medicare reimbursements per year. This increase was attenuated when hospitals were regulated by the Recovery Audit Program. Notably, those recovery auditors who developed the ability to identify the use of default templates, copied and pasted data, and cloned records were the most effective in reducing increased reimbursements. These findings have implications on how to combat Medicare reimbursements paid by taxpayer dollars with the Recovery Audit Program and how this information technology (IT) audit can prevent the misuse of information systems to create artificial business value of IT by hospitals. Contributions to information systems and healthcare research, practice, and public policy are discussed. This paper was accepted by Chris Forman, information systems.

Assessing the Impact of Multi-Morbidity and Related Constructs on Patient Reported Safety in Primary Care: Generalized Structural Equation Modelling of Observational Data

We aimed to examine the complex relationships between patient safety processes and outcomes and multimorbidity using a comprehensive set of constructs: multimorbidity, polypharmacy, discordant comorbidity (diseases not sharing either pathogenesis nor management), morbidity burden and patient complexity. We used cross-sectional data from 4782 patients in 69 primary care centres in Spain. We constructed generalized structural equation models to examine the associations between multimorbidity constructs and patient-reported patient safety (PREOS-PC questionnaire). These associations were modelled through direct and indirect (mediated by increased interactions with healthcare) pathways. For women, a consistent association between higher levels of the multimorbidity constructs and lower levels of patient safety was observed via either pathway. The findings for men replicated these observations for polypharmacy, morbidity burden and patient complexity via indirect pathways. However, direct pathways showed unexpected associations between higher levels of multimorbidity and better safety. The consistent association between multimorbidity constructs and worse patient safety among women makes it advisable to target this group for the development of interventions, with particular attention to the role of comorbidity discordance. Further research, particularly qualitative research, is needed for clarifying the complex associations among men.

Higher Patient Complexities are Associated with Increased Length of Stay, Complications, and Readmissions After Total Hip Arthroplasty

Introduction: There is an increased incidence of complex patients undergoing total hip arthroplasty (THA), which demands a rigorous preoperative, intraoperative, and postoperative assessment. It is important how increases in patient complexity impact a variety of patient outcomes. Therefore, the purpose of our study is to determine if a higher Elixhauser Comorbidity Index (ECI), a measure of patient complexity, is correlated with: 1) longer hospital length of stay; 2) increased 90-day medical complications; 3) higher 90-day readmissions; and 4) greater two-year implant-related complications following primary THA. Materials and Methods: Patients undergoing primary THA from January 1, 2004 to December 31, 2015 were queried from the Medicare Standard Analytical Files using the International Classification of Disease, ninth revision (ICD-9) procedure code 81.51. The queried patients (387,831) were filtered by ECI scores of 1 to 5. Patients who have ECI scores of 2 to 5 represented the study cohorts and were matched according to age and sex to patients who have the lowest ECI score (ECI of 1). All cohorts were longitudinally followed to assess and compare hospital length of stay, 90-day medical complications, 90-day readmissions, and two-year implant-related complications. We compared odds-ratios (OR), 95% confidence intervals (95% CI), and p-values using logistic regression analyses and Welch’s t-tests. Results: Patients who have ECI scores greater than 1 had higher hospital length of stay (p<0.001), 90-day medical complications (p<0.001), 90-day readmissions (p<0.001), and two-year implant-related complications (p<0.001). Patients who have an ECI score of 2 (1.26, 95% CI: 1.20–1.32), ECI of 3 (1.61, 95% CI: 1.53–1.69), ECI of 4 (2.05, 95% CI: 1.95–2.14), and ECI of 5 (2.32, 95% CI: 2.21–2.43) had an increasing trend for readmissions, with higher ECI scores correlating with greater odds of readmission following primary THA. Two-year implant-related complications also showed a similar increasing trend with greater patient complexity. Patients who had an ECI score of 5 (2.54, 95% CI: 2.39–2.69) had more implant-related complications compared to patients who had an ECI score of 2 (1.39, 95% CI:1.31–1.48). Conclusion: The results of this study illustrate that a higher Elixhauser-Comorbidity Index is an independent risk factor for longer hospital length of stay, higher 90-day medical complications, greater 90-day readmissions, and increased two-year implant-related complications following primary THA. This study is important as it further defines and heightens awareness of adverse events for complex patients undergoing this procedure. Future studies can examine if these events can potentially be mitigated through reductions in ECI scores prior to surgery and increased incentives for the healthcare team.

Engaging Family Medicine Residents in a Structured Patient Panel Reassignment Process

Background and Objectives: The patient panels of graduating residents must be reassigned by the end of residency. This process affects over 1 million patients annually within the specialty of family medicine. The purpose of this project was to implement a structured, year-end reassignment system in a family medicine residency program. Methods: Our structured reassignment process took place from December 2017 through June 2020. Panel lists of current, active patients were generated and residents were responsible for reassigning their own panels during a panel reassignment night. We created a tip sheet that addressed patient complexity and continuity, a risk stratification algorithm based on patients’ medical and social complexity, and a tool that tracked the number of patients assigned to each future provider. Outcome measures included a resident satisfaction survey administered in 2018-2020 and patient-provider continuity measured with a run chart from December 2016 through August 2020. Results: The resident survey response rate was 75%. Seventy-three percent felt the panel reassignment night was very helpful; 87% thought the reassignment timeline was extremely reasonable, and 87% indicated that they had the necessary information to reassign their patients. Residents also felt confident that their patients were reassigned appropriately (33% extremely confident, 67% somewhat confident). Patient continuity improved with a 13-point run above the median, indicating nonrandom variation. Patient continuity remained above the median until the impact of COVID-19 in April 2020. Conclusion: Our structured reassignment process was received positively by residents and resulted in improved patient continuity.