Economic Burden of Postoperative Neurocognitive Disorders Among US Medicare Patients.

Importance: Postoperative neurocognitive disorders (PNDs) after surgical procedures are common and may be associated with increased health care expenditures. Objective: To quantify the economic burden associated with a PND diagnosis in 1 year following surgical treatment among older patients in the United States. Design, Setting, and Participants: This retrospective cohort study used claims data from the Bundled Payments for Care Improvement Advanced Model from 4285 hospitals that submitted Medicare Fee-for-service (FFS) claims between January 2013 and December 2016. All Medicare patients aged 65 years or older who underwent an inpatient hospital admission associated with a surgical procedure, did not experience a PND before index admission, and were not undergoing dialysis or concurrently enrolled in Medicaid were included. Data were analyzed from October 2019 and May 2020. Exposures: PND, defined as an International Classification of Diseases, Ninth or Tenth Revision, diagnosis of delirium, mild cognitive impairment, or dementia within 1 year of discharge from the index surgical admission. Main Outcomes and Measures: The primary outcome was total inflation-adjusted Medicare postacute care payments within 1 year after the index surgical procedure. Results: A total of 2 380 473 patients (mean [SD] age, 75.36 (7.31) years; 1 336 736 [56.1%] women) who underwent surgical procedures were included, of whom 44 974 patients (1.9%) were diagnosed with a PND. Among all patients, most were White (2 142 157 patients [90.0%]), presenting for orthopedic surgery (1 523 782 patients [64.0%]) in urban medical centers (2 179 893 patients [91.6%]) that were private nonprofits (1 798 749 patients [75.6%]). Patients with a PND, compared with those without a PND, experienced a significantly longer hospital length of stay (mean [SD], 5.91 [6.01] days vs 4.29 [4.18] days; P < .001), were less likely to be discharged home (9947 patients [22.1%] vs 914 925 patients [39.2%]; P < .001), and had a higher incidence of mortality at 1 year after treatment (4580 patients [10.2%] vs 103 767 patients [4.4%]; P < .001). After adjusting for patient and hospital characteristics, the presence of a PND within 1 year of the index procedure was associated with an increase of $17 275 (95% CI, $17 058-$17 491) in cost in the 1-year postadmission period (P < .001). Conclusions and Relevance: The findings of this cohort study suggest that among older Medicare patients undergoing surgical treatment, a diagnosis of a PND was associated with an increase in health care costs for up to 1 year following the surgical procedure. Given the magnitude of this cost burden, PNDs represent an appealing target for risk mitigation and improvement in value-based health care.

A Framework for Aligning Data from Multiple Institutions to Conduct Meaningful Analytics.

INTRODUCTION: Health systems can be supported by collaborative networks focused on data sharing and comparative analytics to identify and rapidly disseminate promising care practices. Standardized data collection, quality assessment, and cleansing is a necessary process to facilitate meaningful analytics for operations, quality improvement, and research. We developed a framework for aligning data from health care delivery systems using the High Value Healthcare Collaborative central registry. FRAMEWORK: The centralized data registry model allows for multiple layers of data quality assessment. Our framework uses an iterative approach, starting with clear specifications, maintaining ongoing dialogue with diverse stakeholders, and regular checkpoints to assess data conformance, completeness, and plausibility. LESSONS LEARNED: We found that an iterative communication process is critical for a central registry to ensure: 1) clarity of data specifications, 2) appropriate data quality, and 3) thorough understanding of data source, purpose, and context. Engaging teams from all participating institutions and incorporating diverse stakeholders of clinicians, information technologists, data analysts, operations managers, and health services researchers in all decision making processes supports development of high quality datasets for comparative analytics across multiple institutions. CONCLUSION: A standard data specification and submission process alone does not guarantee aligned data for a collaborative registry. Implementing an iterative data quality improvement framework with extensive communication proved to be effective for aligning data from multiple institutions to support meaningful analytics.

The Truth is in the Data - Differences in the Same Measure Based on Different Sources among HVHC Members Using ICU Length of Stay as an Example.

INTRODUCTION: Intensive Care Unit (ICU) length of stay is a strong indicator of severity of illness and cost in the care of sepsis patients. In this case study, we examine the difference between an electronic health record (EHR) based submissions with Centers for Medicare and Medicaid Services (CMS) payment data. METHODS: Member submitted EHR data contained 26,733 unique patient's records. The CMS data contained demographics, diagnosis, and revenue codes. After linking EHR data to CMS data, we found a discrepancy in ICU days from CMS claims vs. EHR data. Our hypothesis was that removing intermediate ICU LOS would result in a closer match from CMS claims with EHR data. We suspected the use of Intermediate ICU stays in our CMS ICU definition contaminated our ICU LOS data. This resulted in a review of the sepsis specification, further investigation of the data, and follow up conversations with the Member organizations. RESULTS: Agreement between EHR and CMS data improved from 73 percent to 86 percent once the Intermediate ICU time had been removed. DISCUSSION AND CONCLUSIONS: The inclusion of Intermediate ICU in the analysis of severely ill sepsis patients from CMS data diluted the importance of using an ICU LOS for estimating the severity of illness and the cost to the healthcare system. We must ensure that clinical definitions are consistent between data sources that were built for different purposes. Additionally, we learned that engaging with clinicians, analysts, and clinical coders early in the process is required to fully understand the complexities from different sources.

Data Cleaning in the Evaluation of a Multi-Site Intervention Project.

CONTEXT: The High Value Healthcare Collaborative (HVHC) sepsis project was a two-year multi-site project where Member health care delivery systems worked on improving sepsis care using a dissemination & implementation framework designed by HVHC. As part of the project evaluation, participating Members provided 5 data submissions over the project period. Members created data files using a uniform specification, but the data sources and methods used to create the data sets differed. Extensive data cleaning was necessary to get a data set usable for the evaluation analysis. CASE DESCRIPTION: HVHC was the coordinating center for the project and received and cleaned all data submissions. Submissions received 3 sequentially more detailed levels of checking by HVHC. The most detailed level evaluated validity by comparing values within-Member over time and between Member. For a subset of episodes Member-submitted data were compared to matched Medicare claims data. FINDINGS: Inconsistencies in data submissions, particularly for length-of-stay variables were common in early submissions and decreased with subsequent submissions. Multiple resubmissions were sometimes required to get clean data. Data checking also uncovered a systematic difference in the way Medicare and some members defined intensive care unit stay. CONCLUSIONS: Data checking is a critical for ensuring valid analytic results for projects using electronic health record data. It is important to budget sufficient resources for data checking. Interim data submissions and checks help find anomalies early. Data resubmissions should be checked as fixes can introduce new errors. Communicating with those responsible for creating the data set provides critical information.

An exploratory analysis of transfer times in a rural trauma system.

BACKGROUND: Delays to definitive care are of particular concern in rural trauma systems, where prehospital times are significantly longer than average. AIMS: We evaluated for differences between transferring hospitals in the total time required to transport patients to definitive care, and analyzed for associations between transport times and outcomes. SETTINGS AND DESIGN: We employed a cross-sectional design to analyze Level One Trauma Center registry data on interfacility transfer of 3,303 acute trauma patients. MATERIALS AND METHODS: We calculated time in minutes from injury to definitive care (total elapsed time (TET)), and analyzed for associations between TET and both mortality and length of hospital stay at our center. We mapped hospitals and catchment areas to illustrate statistics by transferring hospital. STATISTICAL ANALYSIS: We employed analysis of covariance (ANCOVA) to analyze for the effect of TET and injury severity category upon hospital length of stay, and for the effects of TET and air transport as compared to ground transport. We evaluated for likelihood of in-hospital mortality using logistic regression. RESULTS: TET had little or no effect upon length of hospital stay or in-hospital mortality. The effect of injury severity upon both length of stay and mortality was progressively greater with each categorical increase in severity. Air transport as compared to ground transport was associated with mild increases in length of stay and likelihood of mortality. Mapping revealed spatial patterns that were not evident by statistical analysis alone. CONCLUSIONS: Mapping of geographic variations holds promise as a supplement to quantitative needs assessments of trauma systems in rural regions and developing countries.

Secondary overtriage: the burden of unnecessary interfacility transfers in a rural trauma system.

IMPORTANCE: Unnecessary interfacility transfer of minimally injured patients to a level I trauma center (secondary overtriage) can cause inefficient use of resources and personnel within a regional trauma system. OBJECTIVE: To describe the burden of secondary overtriage in a rural trauma system with a single level I trauma center. DESIGN: Retrospective analysis of institutional trauma registry data. SETTING: Dartmouth Hitchcock Medical Center, a rural level I trauma center. PATIENTS: A total of 7793 injured patients evaluated by the trauma service at Dartmouth Hitchcock Medical Center from January 1, 2007, to December 31, 2011. EXPOSURE: Evaluation by the trauma service. MAIN OUTCOMES AND MEASURES: Patients transferred from another hospital to Dartmouth Hitchcock Medical Center who did not require an operation, had an Injury Severity Score lower than 15, and were discharged alive within 48 hours of admission. RESULTS: Of the 7793 evaluated patients, 4796 (62%) were transferred from other facilities. When compared with scene calls (n = 2997), transferred patients had a similar median Injury Severity Score of 9, but 24% of transferred adult patients and 49% of transferred pediatric patients met our definition of secondary overtriage. The overtriaged patients were most likely to have injuries of the head and neck (56%), followed by skin and soft-tissue injuries (41%). Seventy-two unique institutions transferred trauma patients to Dartmouth Hitchcock Medical Center, but 36% of the overtriaged patients were from 5 institutions. CONCLUSIONS AND RELEVANCE: The incidence of secondary overtriage in our rural trauma center is 26%, with head and neck injuries being the most common reason for transfer. Costs for transportation and additional evaluation for such a significant percentage of patients has important resource utilization implications. Effective regionalization of rural trauma care should include methods to limit secondary overtriage.

Balancing risk and benefit: maintenance of a thawed Group A plasma inventory for trauma patients requiring massive transfusion.

BACKGROUND: Transfusion of plasma and red blood cell (RBC) units in a balanced ratio approximating 1:1 has been shown in retrospective studies to be associated with improved outcomes for trauma patients. Our low-volume rural trauma center uses a trauma-activated transfusion algorithm. Plasma is thawed upon activation to avoid wastage. However, the time required for plasma thawing has made achievement of a 1:1 ratio early in resuscitation challenging. In this study, the time required for plasma thawing is characterized, and a potential solution is proposed. METHODS: A retrospective chart study of 38 moderately and massively transfused (≥6 U in the first 24 hours) trauma patients admitted from January 2008 to March 2012 was performed. We evaluated the time required to dispense plasma and the number of RBCs dispensed before plasma in these patients. RESULTS: The average time between the dispense of RBCs and plasma was 26 minutes (median, 28; range, 0-48 minutes). The average number of RBCs dispensed before plasma was 8 U (median, 7 U; range, 0-24 U). Nearly one third of massively transfused patients had 10 RBCs or greater dispensed before plasma was available. CONCLUSION: There exists the potential for delayed plasma availability owing to time required for thawing, which may compromise the ability to provide balanced plasma to RBC transfusion to trauma patients. Maintenance of a thawed Group AB plasma inventory may not be operationally feasible for rural centers with low trauma volumes. Use of a thawed Group A plasma inventory is a potential alternative to ensure rapid plasma availability. LEVEL OF EVIDENCE: Therapeutic study, level V.

Blood administration in helicopter emergency medical services patients associated with hypothermia.

INTRODUCTION: The infusion of packed red blood cells (PRBCs) in the severely injured patient is not a new practice. It is a potentially lifesaving procedure although it is not without inherent risk. This practice in the helicopter emergency medical services (HEMS) has not been examined in the literature. We attempt to determine factors associated with hypothermia (ie, < 35°C), including the transfusion of O negative blood. METHODS: This was a retrospective review using our trauma registry on all patients who arrived at our rural level 1 trauma center by HEMS from January 1, 2005, through June 30, 2009. Patient temperature on arrival was compared for patients with and without hypothermia transported by our HEMS service. RESULTS: During the study period, there were 707 HEMS transports by our service. Sixty (8.5%) were hypothermic, and 30 (4.2%) received PRBCs. There was a high likelihood of PRBC patients with hypothermia (odds ratio = 6.27; 95% confidence interval, 2.47-14.89; P < .05). DISCUSSION: HEMS trauma patients who have received blood are more likely to arrive hypothermic (ie, < 35°C). The clinical impact of giving PRBCs in the HEMS prehospital setting was not determined.

Comparing rural ground and air emergency medical services: a level I trauma center's experience.

We sought to compare differences in patients transported by ground and air emergency medical services directly from the scenes of their injuries to a rural level I trauma facility. Variables examined included age, gender, vital signs, Glasgow Coma Scale score, discharge location, length of stay, and survival metrics. Student t tests and odds ratios were used for analysis. Demographics and vital signs differed between trauma patients transported by air versus those transported by ground. Generally, length of stay was longer in air-transported patients, who also had poorer survival metrics with negligible risk of death. Significant differences exist in the markers of physiology such as vital signs, expected survival, and degree of injury.

Trauma patients without a trauma diagnosis: the data gap at a level one trauma center.

BACKGROUND: Trauma registries may contain records without a codable trauma diagnosis, creating a "data gap" that multiplies the number of invalid registry data fields. We designed an investigation intended to determine the incidence of registry records with noncodable trauma diagnoses, characterize those records, and determine the reasons for inadequate diagnosis data. METHODS: We used a retrospective cohort design. A query of trauma registry records spanning a 5-year period yielded 129 records with no injury severity score. Each patient's medical record was reviewed, sources of diagnostic information were noted, and diagnoses were categorized. RESULTS: In 57% of cases, we found documentation that the patient had sustained an injury, but the injury was inadequately documented in the discharge summary. In 19% of cases, although the registry record was valid, the diagnosis was not codable as trauma. In 17% of cases, clinical documentation was adequate, but the diagnosis was inadequately recorded in the trauma registry. In 13% of cases, no traumatic injury was sustained, although the registry record was valid. In 2% of cases, the trauma registry record itself was invalid. In 1% of cases, a coding error occurred. Particularly prominent among records with inadequate discharge documentation were cases of head and spine injury for which there was no radiographic evidence. CONCLUSIONS: The incidence of records with noncodable diagnoses might best be reduced through improved physician documentation, revision of trauma registry inclusion criteria, increased attention by trauma registrars to key sources of documentation, and direct communication with the attending physician when necessary.