The sensitivity of adverse event cost estimates to diagnostic coding error

Assessing the excess costs of the in-hospital adverse events covered by the AHRQ's Patient Safety Indicators in Switzerland

There currently exists no comprehensive and up-to date overview on the financial impact of the different adverse events covered by the Patient Safety Indicators (PSIs) from the Agency for Healthcare Research and Quality. We conducted a retrospective case-control study using propensity score matching on a national administrative data set of 1 million inpatients in Switzerland to compare excess costs associated with 16 different adverse events both individually and on a nationally aggregated level. After matching 8,986 cases with adverse events across the investigated PSIs to 26,931 controls, we used regression analyses to determine the excess costs associated with the adverse events and to control for other cost-related influences. The average excess costs associated with the PSI-related adverse events ranged from CHF 1,211 (PSI 18, obstetric trauma with instrument) to CHF 137,967 (PSI 10, postoperative acute kidney injuries) with an average of CHF 27,409 across all PSIs. In addition, adverse events were associated with 7.8-day longer stays, 2.5 times more early readmissions (within 18 days), and 4.1 times higher mortality rates on average. At a national level, the PSIs were associated with CHF 347 million higher inpatient costs in 2019, which corresponds to about 2.2% of the annual inpatient costs in Switzerland. By comparing the excess costs of different PSIs on a nationally aggregated level, we offer a financial perspective on the implications of in-hospital adverse events and provide recommendations for policymakers regarding specific investments in patient safety to reduce costs and suffering.

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure diagnostic coding intensity, built on commonly available administrative claims data, and demonstrated through a 2019 pneumonia acute inpatient cohort (N = 182,666). A Poisson additive model (PAM) is proposed to model risk-adjusted additional coded diagnoses. Excess coding intensity per patient visit was estimated as the difference between the observed and PAM-based expected counts of secondary diagnoses upon risk adjustment by patient-level characteristics. Incidence rate ratios were extracted for patient-level characteristics and further adjustments were explored by facility-level characteristics to account for facility and geographical differences. Facility-level factors contribute substantially to explain the remaining variability in excess diagnostic coding, even upon adjusting for patient-level risk factors. This approach can provide hospitals and stakeholders with a tool to identify outlying facilities that may experience substantial differences in processes and procedures compared to peers or general industry standards. The approach does not rely on the availability of clinical information or disease-specific markers, is generalizable to other patient cohorts, and can be expanded to use other sources of information, when available.

The impact of hospital harm on length of stay, costs of care and length of person-centred episodes of care: a retrospective cohort study

BACKGROUND

There is a lack of data in Canada on the longitudinal effects of adverse events that occur in hospital, specifically in the period after discharge. Our objective was to quantify the impact of adverse events on hospital length of stay, length of person-centred episodes of care (PCEs) and costs of PCEs, as well as their impact on the total health system.

METHODS

We conducted a population-based, retrospective cohort study using linked health administrative databases. We included adults in Ontario who had an acute hospital admission between Apr. 1, 2015, and Mar. 31, 2016. We grouped hospital admissions into 1 of 9 episode types and used the Canadian Institute for Health Information methodology for hospital harm to measure adverse events. We specified generalized linear models to estimate the impact of hospital harm on the following: incremental length of index acute hospital admission, incremental length of the PCE, and incremental costs of the PCE.

RESULTS

Out of 610 979 hospital admissions, 36 004 (5.9%) involved an occurrence of harm. The impact of harm on the incremental length of hospital stay ranged from 0.4 to 24.2 days (p < 0.001); the incremental length of the PCE ranged from 0.3 to 30.2 days (p < 0.001); and the incremental costs of the PCE ranged from $800 to $51 067 (p < 0.001). Total hospital days attributable to hospital harm amounted to 407 696, and the total attributable cost to the Ontario health system amounted to $1 088 330 376.

INTERPRETATION

We found that experiencing harm in hospital significantly affects both in-hospital and post-discharge use of health services and costs of care, and constitutes an enormous expense to Ontario's publicly funded health system.

Sources of variability in hospital administrative data: Clinical coding of postoperative ileus

BACKGROUND

Multiple studies have questioned the validity of clinical codes in hospital administrative data. We examined variability in reporting a postoperative ileus (POI).

OBJECTIVE

We aimed to analyse sources of coding variations to understand how clinical coding professionals arrive at POI coding decisions and to verify existing knowledge that current clinical coding practices lack standardised applications of regulatory guidelines.

METHOD

Two medical records (cases 1 and 2) were provided to 15 clinical coders employed by a midsize nonprofit hospital in the northwest region of the United States. After coding these cases, the study participants completed a survey, reported on the application of guidelines, and participated in a focus group led by a health information management regulatory compliance expert.

RESULTS

Only 5 of the 15 clinical coders correctly indicated no POI complication in case 1 where the physician documentation did not establish a link between the POI as a complication of care and the surgery. In contrast, 13 of the 15 study participants correctly coded case 2, which included clear physician documentation and contained the clinical parameters for the coding of the POI as a complication of care. Clinical coder education, credentials, certifications, and experience did not relate to the coding performance. The clinical coders inconsistently prioritised coding rules and valued experience more than education.

CONCLUSION AND IMPLICATIONS

The application of International Classification of Diseases, Ninth Revision, Clinical Modification; coding conventions; Centers for Medicare and Medicaid Services coding guidelines; and American Hospital Association coding clinic advice was subject to the clinical coders' interpretation; they perceived them as conflicting guidance. Their reliance on subjective experience in dealing with this conflicting guidance may limit the accuracy of reporting outcomes of clinical performance.

Spinal Cord Injury Clinical Registries: Improving Care across the SCI Care Continuum by Identifying Knowledge Gaps

Timely access and ongoing delivery of care and therapeutic interventions is needed to maximize recovery and function after traumatic spinal cord injury (tSCI). To ensure these decisions are evidence-based, access to consistent, reliable, and valid sources of clinical data is required. The Access to Care and Timing Model used data from the Rick Hansen SCI Registry (RHSCIR) to generate a simulation of healthcare delivery for persons after tSCI and to test scenarios aimed at improving outcomes and reducing the economic burden of SCI. Through model development, we identified knowledge gaps and challenges in the literature and current health outcomes data collection throughout the continuum of SCI care. The objectives of this article were to describe these gaps and to provide recommendations for bridging them. Accurate information on injury severity after tSCI was hindered by difficulties in conducting neurological assessments and classifications of SCI (e.g., timing), variations in reporting, and the lack of a validated SCI-specific measure of associated injuries. There was also limited availability of reliable data on patient factors such as multi-morbidity and patient-reported measures. Knowledge gaps related to structures (e.g., protocols) and processes (e.g., costs) at each phase of care have prevented comprehensive evaluation of system performance. Addressing these knowledge gaps will enhance comparative and cost-effectiveness evaluations to inform decision-making and standards of care. Recommendations to do so were: standardize data element collection and facilitate database linkages, validate and adopt more outcome measures for SCI, and increase opportunities for collaborations with stakeholders from diverse backgrounds.

Healthy Life-Years Lost and Excess Bed-Days Due to 6 Patient Safety Incidents: Empirical Evidence From English Hospitals

BACKGROUND

There is little satisfactory evidence on the harm of safety incidents to patients, in terms of lost potential health and life-years.

OBJECTIVE

To estimate the healthy life-years (HLYs) lost due to 6 incidents in English hospitals between the years 2005/2006 and 2009/2010, to compare burden across incidents, and estimate excess bed-days.

RESEARCH DESIGN

The study used cross-sectional analysis of the medical records of all inpatients treated in 273 English hospitals. Patients with 6 types of preventable incidents were identified. Total attributable loss of HLYs was estimated through propensity score matching by considering the hypothetical remaining length and quality of life had the incident not occurred.

RESULTS

The 6 incidents resulted in an annual loss of 68 HLYs and 934 excess bed-days per 100,000 population. Preventable pressure ulcers caused the loss of 26 HLYs and 555 excess bed-days annually. Deaths in low-mortality procedures resulted in 25 lost life-years and 42 bed-days. Deep-vein thrombosis/pulmonary embolisms cost 12 HLYs, and 240 bed-days. Postoperative sepsis, hip fractures, and central-line infections cost <6 HLYs and 100 bed-days each.

DISCUSSION

The burden caused by the 6 incidents is roughly comparable with the UK burden of Multiple Sclerosis (80 DALYs per 100,000), HIV/AIDS and Tuberculosis (63 DALYs), and Cervical Cancer (58 DALYs). There were marked differences in the harm caused by the incidents, despite the public attention all of them receive. Decision makers can use the results to prioritize resources into further research and effective interventions.

A New Strategy to Evaluate Technical Efficiency in Hospitals Using Homogeneous Groups of Casemix : How to Evaluate When There is Not DRGs?

The public health system has restricted economic resources. Because of that, it is necessary to know how the resources are being used and if they are properly distributed. Several works have applied classical approaches based in Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for this purpose. However, if we have hospitals with different casemix, this is not the best approach. In order to avoid biases in the comparisons, other works have recommended the use of hospital production data corrected by the weights from Diagnosis Related Groups (DRGs), to adjust the casemix of hospitals. However, not all countries have this tool fully implemented, which limits the efficiency evaluation. This paper proposes a new approach for evaluating the efficiency of hospitals. It uses a graph-based clustering algorithm to find groups of hospitals that have similar production profiles. Then, DEA is used to evaluate the technical efficiency of each group. The proposed approach is tested using the production data from 2014 of 193 Chilean public hospitals. The results allowed to identify different performance profiles of each group, that differs from other studies that employs data from partially implemented DRGs. Our results are able to deliver a better description of the resource management of the different groups of hospitals. We have created a website with the results ( bioinformatic.diinf.usach.cl/publichealth ). Data can be requested to the authors.

Quantifying the excess cost and resource utilisation for patients with complications associated with elective knee arthroscopy: a retrospective cohort study

BACKGROUND

Recent studies have demonstrated morbidity associated with elective knee arthroscopy. The objective of the current study was to quantify resource utilisation and costs associated with postoperative complications following an elective knee arthroscopy.

METHODS

We undertook a retrospective, longitudinal cohort study using routinely collected hospital data from Victorian public hospitals during the period from 1 July 2000 to 30 June 2009. A generalised linear model was used to examine relative cost and length of stay for venous thromboembolism, joint complications and infections. Log-transformed multiple linear regression and retransformation were used to determine the excess cost after adjustment.

RESULTS

We identified 166,770 episodes involving an elective knee arthroscopy. There were a total of 976(0.6%) complications, including 573 patients who had a venous thromboembolism (VTE) (0.3%), 227 patients with a joint complication (0.1%) and 141 patients with infections (0.1%). After adjustment, the excess 30-day cost per patient for venous thromboembolism was $USD +3227 (95% CI: $3211-3244), for joint complications it was $USD +2247 (95% CI: $2216-2280) and for infections it was $USD +4364 (95% CI: $4331-4397).

CONCLUSION

This is the first study to quantify resource utilisation for complications associated with elective knee arthroscopy. With growing attention focused on improving patient outcomes and containing costs, understanding the nature and impact of complications on resource utilisation is important.

An economic evaluation of perioperative adverse events associated with spinal surgery

BACKGROUND CONTEXT

Besides their clinical impact, the economic impact of health care-related adverse events (AEs) is significant. Although a number of studies have attempted to estimate the economic impact of AEs, few have directly linked costs to clinician-reported event severity.

PURPOSE

To estimate the economic impact in terms of the incremental cost and length of stay (LOS), attributable to different severity grades of AEs that occurred during perioperative spinal surgery.

STUDY DESIGN

Health economic evaluation of data from a prospective observational study from the perspective of an academic hospital.

PATIENT SAMPLE

Consecutive patients at a single, tertiary-quaternary care institution who have undergone inpatient spinal surgery.

OUTCOME MEASURES

The cost and LOS impacts with respect to the severity of the AEs.

METHODS

We analyzed 4 years of patient discharges between January 1, 2007 and December 31, 2010. The Spine Adverse Events Severity instrument was completed by the surgical team at discharge. Clinical impacts of the AEs were graded as I (requires no/minimal treatment), II (requires treatment and is not likely to cause long-term [>6 months] sequelae), III (requires treatment and is most likely to cause long-term sequelae), and IV (death). A total of 1,815 records were linked with the patient-level costing information. We matched each AE case with four control cases based on their propensity score for the risk of experiencing an AE, regressed against case characteristics. We estimated an incremental cost and LOS for each severity grade by calculating the differences in means across cases and controls. We conducted a sensitivity analysis by estimating the alternate models using generalized linear model (GLM) regression with a gamma log link.

RESULTS

Adverse events were reported in 316 (17.4%) cases, with 126 of these patients (40.2%) experiencing multiple events. The incremental cost/LOS for each severity grade are as follows: I=$4,224 (p=.0351)/3.63 days (p=.0001); II=$23,500 (p<.0001)/14.03 days (p<.0001); III=$147,285 (p=.0036)/74.50 days (p=.0018); and IV=$121,366 (p=.0323)/46.44 days (p=.0036). The total cost in millions/LOS (days) associated with each grade over the 4-year study period are as follows: I=$0.66 million/569.9 days; II=$2.96 million/1,767.8 days; III=$4.27 million/2,160.5 days; and IV=$0.49 million/185.8 days. Our sensitivity analysis produced comparable overall results using alternate modeling techniques. Overall, AEs contributed an estimated $8.38 million (16.0% of the total costs for all patients in the sample) in incremental costs and 4,684 additional bed days over the 4-year study period.

CONCLUSIONS

In this surgical spine cohort, AEs accounted for 16% of the total cost of in-hospital care. Higher severity AEs were progressively more costly on a per-case basis; however, the more frequent lower severity events (ie, Grade I and II) also had a substantial aggregate cost (43%). These results suggest that a strong business case exists for patient safety strategies focused not only on severe AEs but also on the reduction of lower severity events that may be more amenable to prevention efforts.