Predictive Analytics In Healthcare: Medications as a Predictor of Medical Complexity

Adrenal insufficiency in pediatric kidney transplantation recipients

BACKGROUND

Immunosuppression of pediatric kidney transplant (PKT) recipients often includes corticosteroids. Prolonged corticosteroid exposure has been associated with secondary adrenal insufficiency (AI); however, little is known about its impact on PKT recipients.

METHODS

This was a retrospective cohort review of PKT recipients to evaluate AI prevalence, risk factors, and adverse effects. AI risk was assessed using morning cortisol (MC) and diagnosis confirmed by an ACTH stimulation test. Potential risk factors and adverse effects were tested for associations with MC levels and AI diagnosis.

RESULTS

Fifty-one patients (60.8% male, age 7.4 (IQR 3.8, 13.1) years; 1 patient counted twice for repeat transplant) were included. Patients at risk for AI (MC < 240 nmol/L) underwent definitive ACTH stimulation testing, confirming AI in 13/51 (25.5%) patients. Identified risk factors for AI included current prednisone dosage (p = .001), 6-month prednisone exposure (p = .02), daily prednisone administration (p = .002), and rejection episodes since transplant (p = .001). MC level (2.5 years (IQR 1.1, 5.1) post-transplant) was associated with current prednisone dosage (p < .001), 6-month prednisone exposure (p = .001), daily prednisone administration (p = .006), rejection episodes since transplant (p = .003), greater number of medications (β = -16.3, p < .001), 6-month hospitalization days (β = -3.3, p = .013), creatinine variability (β = -2.4, p = .025), and occurrence of acute kidney injury (β = -70.6, p = .01).

CONCLUSION

Greater corticosteroid exposure was associated with a lower MC level and confirmatory diagnosis of AI noted with an ACTH stimulation test. Adverse clinical findings with AI included greater medical complexity and kidney function lability. These data support systematic clinical surveillance for AI in PKT recipients treated with corticosteroids.

Lagging coverage for mental health services among children and adolescents through home and community-based Medicaid waivers

PROBLEM

Many states cover mental health home and community-based services (HCBS) for youth through 1915(c) Medicaid HCBS waivers that allow states to waive certain Medicaid eligibility criteria and define high-risk populations based on age, medical condition(s), and disability status. We sought to evaluate how States are covering children and adolescents with mental health needs through 1915(c) waivers compared to other youth waiver populations.

METHODS

Data elements were extracted from Medicaid 1915(c) approved waivers applications for all included waivers targeting any pediatric age range through October 31, 2018. Normalization criteria were developed and an aggregate overall coverage score and level of funding per person per waiver were calculated for each waiver.

FINDINGS

One hundred and forty-two waivers across 45 states were included in this analysis. Even though there was uniformity in the Medicaid applications, there was great heterogeneity in how waiver eligibility, transition plans, services covered, and wait lists were defined across group classifications. Those with mental health needs (termed serious emotional disturbance) represented 5% of waivers with the least annual funding per person per waiver.

CONCLUSIONS

We recommend greater links between public policy, infrastructure, health care providers, and a family-centered approach to extend coverage and scope of services for children and adolescents with mental health needs.

Patterns of Utilization and Expenditure Across Multiple Sclerosis Disease-Modifying Therapies: A Retrospective Cohort Study Using Claims Data from a Commercially Insured Population in the United States, 2010–2019

Introduction

Comparisons of healthcare utilization and expenditure among multiple sclerosis (MS) disease-modifying therapies (DMTs) are limited.

Methods

In this retrospective cohort study using commercial insurance claims data of a US population (2010–2019), we compared healthcare utilization and costs in MS across different DMTs. We assigned patients to different treatment arms: no DMT (ND), high-efficacy (HE) DMT (alemtuzumab, B cell depletion, cladribine, and natalizumab), and standard-efficacy (SE) DMT (dimethyl fumarate, glatiramer acetate, interferon beta, sphingosine-1-phosphate receptor modulator, and teriflunomide). We obtained healthcare costs and occurrences of healthcare services: outpatient visits, emergency room visits, hospitalizations, MS-related magnetic resonance imaging (MRI). We quantified relapses (based on MS-related hospitalizations, as well as outpatient visits with prescription of high-dose steroids) and medical complexity (based on unique drug classes of prescriptions). We calculated covariate-adjusted incidence rate ratio of healthcare services using negative binomial regression with ND as reference and covariate-adjusted mean cumulative healthcare costs using a generalized linear model with log-link function and gamma distribution.

Results

Among the 25,932 patients with MS (mean age 52.8 years, 75.2% women), both HE (mean age 54.0 years, 76.2% women) and SE (mean age 43.9 years, 75.6% women) groups had more non-pharmacy healthcare utilization than ND (mean age 57.6 years, 75.4% women), including overall outpatient doctor visits, neurology visits, and MS-related MRIs as well as relapses and medical complexities. Relative to ND, both HE and SE groups had higher pharmacy costs and overall healthcare costs 12 months after treatment initiation, despite having lower or equivalent non-pharmacy medical costs. In patients on DMT, pharmacy costs accounted for up to 65% of overall healthcare costs with over 85% of pharmacy costs attributable to DMT costs.

Conclusion

DMT cost is a key driver of the overall healthcare expenditure in MS. Future comparative and cost-effectiveness studies integrating claims and electronic health records data with better balancing of patient characteristics are warranted.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-022-00358-4.

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital-based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock-and-flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10-day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.

Prediction of patient evolution in terms of Clinical Risk Groups form routinely collected data using machine learning

Chronicity is a problem that is affecting quality of life and increasing healthcare costs worldwide. Predictive tools can help mitigate these effects by encouraging the patients' and healthcare system's proactivity. This research work uses supervised learning techniques to build a predictive model of the healthcare status of a chronic patient, using Clinical Risk Groups (CRGs) as a measure of chronicity and prescription and diagnosis data as predictors. The model is addressed to the whole population in our healthcare system regardless of the disease, as data used are widely available in a consistent way for all patients. We explore different ways to encode data that are appropriate for machine learning. Results suggest that these data alone can be used to build accurate models, and show that, in our set, prescription information has a higher predictive value than diagnosis.

Towards translational health policy: Findings from a state evaluation of programs targeting children with special health care needs

PURPOSE

Current evidence-based research suggests that early evaluation, comprehensive care plans, and appropriate referrals for childhood and adolescent behavioral and development needs is critical for successful family-centered outcomes. The overall purpose of this study was to conduct an assessment of a state public health program that offers diagnostic evaluation and coordination for children with behavioral and developmental disorders in the state of Virginia (Child Development Center programs, or CDC). A secondary purpose was to provide translational policy and advocacy targets based on key findings.

DESIGN AND METHOD

The evaluation of the scope of services of the CDC programs was done using qualitative interviews with a focus group interview (n = 23), interviews from representatives from individual centers ( n = 5 centers), and descriptive quantitative data elements for the fiscal year 2015.

RESULTS

After conducting the state public health evaluation, several translational health policy priorities emerged, including: (a) the need for integrated data standards, (b) Lack of developmental pediatric workforce, particularly in rural sectors of the state, and (c) Need for enhanced program support for care coordination.

CONCLUSION

Academic nurse and public health partnerships can aid in translation from research to policy among vulnerable populations and assist in communication to key stakeholders and legislators for iterative action and reassessment.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation

Background

Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress.

Objective

The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas.

Methods

On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated.

Results

Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger.

Conclusions

The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

A Real-World Observational Study of Time to Treatment Intensification Among Elderly Patients with Inadequately Controlled Type 2 Diabetes Mellitus

BACKGROUND

Among elderly patients, the management of type 2 diabetes mellitus (T2DM) is complicated by population heterogeneity and elderly-specific complexities. Few studies have been done to understand treatment intensification among elderly patients failing multiple oral antidiabetic drugs (OADs).

OBJECTIVE

To examine the association between time to treatment intensification of T2DM and elderly-specific patient complexities.

METHODS

In this observational, retrospective cohort study, elderly (aged ≥ 65 years) Medicare beneficiaries (n = 16,653) with inadequately controlled T2DM (hemoglobin A1c ≥ 8.0% despite 2 OADs) were included. Based on the consensus statement for diabetes care in elderly patients published by the American Diabetes Association and the American Geriatric Society, elderly-specific patient complexities were defined as the presence or absence of 5 geriatric syndromes: cognitive impairment; depression; falls and fall risk; polypharmacy; and urinary incontinence.

RESULTS

Overall, 48.7% of patients received intensified treatment during follow-up, with median time to intensification 18.5 months (95% CI = 17.7-19.3). Median time to treatment intensification was shorter for elderly patients with T2DM with polypharmacy (16.5 months) and falls and fall risk (12.7 months) versus those without polypharmacy (20.4 months) and no fall risk (18.6 months). Elderly patients with urinary incontinence had a longer median time to treatment intensification (18.6 months) versus those without urinary incontinence (14.6 months). The median time to treatment intensification did not significantly differ by the elderly-specific patient complexities that included cognitive impairment and depression. However, after adjusting for demographic, insurance, clinical characteristics, and health care utilization, we found that only polypharmacy was associated with time to treatment intensification (adjusted hazard ratio, 1.10; 95% CI = 1.04-1.15; P = 0.001).

CONCLUSIONS

Less than half of elderly patients with inadequately controlled T2DM received treatment intensification. Elderly-specific patient complexities were not associated with time to treatment intensification, emphasizing a positive effect of the integrated health care delivery model. Emerging health care delivery models that target integrated care may be crucial in providing appropriate treatment for elderly T2DM patients with complex conditions.

A Case Study: Analyzing City Vitality with Four Pillars of Activity-Live, Work, Shop, and Play

This case study evaluates and tracks vitality of a city (Seattle), based on a data-driven approach, using strategic, robust, and sustainable metrics. This case study was collaboratively conducted by the Downtown Seattle Association (DSA) and CDO Analytics teams. The DSA is a nonprofit organization focused on making the city of Seattle and its Downtown a healthy and vibrant place to Live, Work, Shop, and Play. DSA primarily operates through public policy advocacy, community and business development, and marketing. In 2010, the organization turned to CDO Analytics ( cdoanalytics.org ) to develop a process that can guide and strategically focus DSA efforts and resources for maximal benefit to the city of Seattle and its Downtown. CDO Analytics was asked to develop clear, easily understood, and robust metrics for a baseline evaluation of the health of the city, as well as for ongoing monitoring and comparisons of the vitality, sustainability, and growth. The DSA and CDO Analytics teams strategized on how to effectively assess and track the vitality of Seattle and its Downtown. The two teams filtered a variety of data sources, and evaluated the veracity of multiple diverse metrics. This iterative process resulted in the development of a small number of strategic, simple, reliable, and sustainable metrics across four pillars of activity: Live, Work, Shop, and Play. Data during the 5 years before 2010 were used for the development of the metrics and model and its training, and data during the 5 years from 2010 and on were used for testing and validation. This work enabled DSA to routinely track these strategic metrics, use them to monitor the vitality of Downtown Seattle, prioritize improvements, and identify new value-added programs. As a result, the four-pillar approach became an integral part of the data-driven decision-making and execution of the Seattle community's improvement activities. The approach described in this case study is actionable, robust, inexpensive, and easy to adopt and sustain. It can be applied to cities, districts, counties, regions, states, or countries, enabling cross-comparisons and improvements of vitality, sustainability, and growth.