Data quality assessment of the Dutch Breast Implant Registry by automated data verification using medical billing data.

BACKGROUND: The Dutch Breast Implant Registry (DBIR) provides real-time population-based data to monitor and improve the quality and safety of breast implants and to trace patients in the event of an (inter)national recall. To serve these main goals, the capture rate and percentage of implants that are actually registered should be known and data should be complete. This study aimed to describe an automated verification process for capture rates and data completeness using medical billing data as the external source. METHODS: DBIR-data on reconstructive permanent breast implants and tissue expanders (TEs) from 2019 were compared to medical billing data. At the hospital level, the capture rate of DBIR and accuracy of the selected data points were assessed. RESULTS: In total, 2389 implants (1420 patients) were included from 12 participating hospitals (11% of all healthcare institutions registering in DBIR). DBIR had capture rates of 99% to 114% for inserted permanent implants and TEs and 49% for explanted permanent implants and TEs. Among the 9015 data points analyzed in DBIR, 8861 (98%) matched the medical billing data. Mastopexy had the highest matching percentage (100%) and capsulectomy the lowest (86%). CONCLUSION: This study showed varying capture rates in DBIR depending on the intervention group, indicating that registration of DBIR-data and medical billing codes could be further optimized. Data accuracy was high as only 2% of data points did not correspond to medical billing data. For future data verification, other external data sources could be explored, including sources that enable verification of cosmetic implants.

Use of hospital care among Dutch diabetes patients.

AIM: To provide insight into healthcare resource utilization and hospital expenditure of patients treated for diabetes in Dutch hospitals. MATERIALS AND METHODS: We conducted an observational cohort study of 193 840 patients aged ≥18 years and treated for diabetes mellitus in 65 Dutch hospitals in 2019 to 2020, using real-world reimbursement data. Consultations, hospitalizations, technology use, total hospital and diabetes care costs (encompassing all care for diabetes itself) were assessed during 1-year follow-up. In addition, expenditure was compared with that in the general Dutch population. RESULTS: Total hospital costs for all patients with diabetes were €1 352 690 257 (1.35 billion) per year, and 15.9% (€214 963 703) was associated with treatment of diabetes. Mean yearly costs per patient were €6978, with diabetes care costs of €1109. Mean hospital costs of patients exceeded that of the Dutch population three- to sixfold. Total hospital costs increased with age, whereas diabetes expenditure decreased with age (18-40 years, €1575; >70 years, €932). Of all patients with diabetes, 51.3% (n = 99 457) received care related to cardiovascular complications. Micro- and macrovascular complications, or a combination, increased hospital costs (1.4-5.3 times higher). CONCLUSIONS: The hospital resource use of Dutch diabetes patients is high, with a large burden of cardiovascular complications. Resource use is rooted mainly in hospital care of diabetes-related complications, not in the treatment of diabetes. Early treatment and prevention of complications remain imperative to taper future healthcare expenditure on patients with diabetes.

Healthcare expenditure and technology use in pediatric diabetes care.

BACKGROUND: Diabetes mellitus is one of the most common chronic diseases in childhood. With more advanced care options including ever-evolving technology, allocation of resources becomes increasingly important to guarantee equal care for all. Therefore, we investigated healthcare resource utilization, hospital costs, and its determinants in Dutch children with diabetes. METHODS: We conducted a retrospective, observational analysis with hospital claims data of 5,474 children with diabetes mellitus treated in 64 hospitals across the Netherlands between 2019-2020. RESULTS: Total hospital costs were €33,002,652 per year, and most of these costs were diabetes-associated (€28,151,381; 85.3%). Mean annual diabetes costs were €5,143 per child, and treatment-related costs determined 61.8%. Diabetes technology significantly increased yearly diabetes costs compared to no technology: insulin pumps € 4,759 (28.7% of children), Real-Time Continuous Glucose Monitoring € 7,259 (2.1% of children), and the combination of these treatment modalities € 9,579 (27.3% of children). Technology use increased treatment costs significantly (5.9 - 15.3 times), but lower all-cause hospitalisation rates were observed. In all age groups, diabetes technology use influenced healthcare consumption, yet in adolescence usage decreased and consumption patterns changed. CONCLUSIONS: These findings suggest that contemporary hospital costs of children with diabetes of all ages are driven primarily by the treatment of diabetes, with technology use as an important additive factor. The expected rise in technology use in the near future underlines the importance of insight into resource use and cost-effectiveness studies to evaluate if improved outcomes balance out these short-term costs of modern technology.

Between-Hospital and Between-Physician Variation in Outcomes and Costs in High- and Low-Complex Surgery: A Nationwide Multilevel Analysis.

OBJECTIVES: Clinicians and policy makers are increasingly exploring strategies to reduce unwarranted variation in outcomes and costs. Adequately accounting for case mix and better insight into the levels at which variation exists is crucial for such strategies. This nationwide study investigates variation in surgical outcomes and costs at the level of hospitals and individual physicians and evaluates whether these can be reliably compared on performance. METHODS: Variation was analyzed using 92 330 patient records collected from 62 Dutch hospitals who underwent surgery for colorectal cancer (n = 6640), urinary bladder cancer (n = 14 030), myocardial infarction (n = 31 870), or knee osteoarthritis (n = 39 790) in the period 2018 to 2019. Multilevel regression modeling with and without case-mix adjustment was used to partition variation in between-hospital and between-physician components for in-hospital mortality, intensive care unit admission, length of stay, 30-day readmission, 30-day reintervention, and in-hospital costs. Reliability was calculated for each treatment-outcome combination at both levels. RESULTS: Across outcomes, hospital-level variation relative to total variation ranged between ≤ 1% and 15%, and given the high caseloads, this typically yielded high reliability (> 0.9). In contrast, physician-level variation components were typically ≤ 1%, with limited opportunities to make reliable comparisons. The impact of case-mix adjustment was limited, but nonnegligible. CONCLUSIONS: It is not typically possible to make reliable comparisons among physicians due to limited partitioned variation and low caseloads. Nevertheless, for hospitals, the opposite often holds. Although variation-reduction efforts directed at hospitals are thus more likely to be successful, this should be approached cautiously, partly because level-specific variation and the impact of case mix vary considerably across treatments and outcomes.

Administrative healthcare data as an addition to the Dutch surgical aneurysm audit to evaluate mid-term reinterventions following abdominal aortic aneurysm repair: A pilot study.

BACKGROUND: The Dutch Surgical Aneurysm Audit (DSAA) is a nationwide mandatory quality registry that evaluates the perioperative outcomes of abdominal aortic aneurysms (AAAs). The DSAA includes perioperative outcomes that occur up to 30 days, but various complications following AAA repair occur after this period. Administrative healthcare data yield the possibility to evaluate later occuring outcomes such as reinterventions, without increasing the registration burden. The aim of this study is to assess the feasibility and the potential benefit of administrative healthcare data to evaluate mid-term reinterventions following intact AAA repair. METHOD: All patients that underwent primary endovascular aneurysm repair (EVAR) or open surgical repair (OSR) for an intact infrarenal AAA between January 2017 and December 2018 were selected from the DSAA. Subsequently, these patients were identified in a database containing reimbursement data. Healthcare activity codes that refer to reinterventions following AAA repair were examined to assess reinterventions within 12 and 15 months following EVAR and OSR. RESULTS: We selected 4043 patients from the DSAA, and 2059 (51%) patients could be identified in the administrative healthcare database. Reintervention rates of 10.4% following EVAR and 9.5% following OSR within 12 months (p = 0.719), and 11.5% following EVAR and 10.8% following OSR within 15 months (p = 0.785) were reported. CONCLUSION: Administrative healthcare data as an addition to the DSAA is potentially beneficial to evaluate mid-term reinterventions following intact AAA repair without increasing the registration burden for clinicians. Further validation is necessary before reliable implementation of this tool is warranted.