HPARSS Alert System for Childhood Cancer Survivors

Overseen By Robert J. Hayashi

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Washington University School of Medicine

Disqualifiers: Active cancer treatment, Late Effects Program, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

146 Participants Needed

Trial Summary

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the treatment EHR-based CDS Alert System for Sensory Deficit Screening?

Research shows that clinical decision support systems (CDS) integrated with electronic health records (EHR) can improve response times and decision-making in healthcare settings. For example, a study found that physicians responded quickly to alerts and often took action based on them, suggesting that similar systems could be effective in screening for sensory deficits in childhood cancer survivors. ^{1 2 3 4 5}

Is the HPARSS Alert System for Childhood Cancer Survivors safe for humans?

The research articles do not provide specific safety data for the HPARSS Alert System or its related names. They discuss clinical decision support systems in general, focusing on medication safety and preventing adverse drug events, but do not address the safety of the HPARSS system specifically. ^{3 6 7 8 9}

How does the HPARSS Alert System treatment differ from other treatments for childhood cancer survivors?

The HPARSS Alert System is unique because it focuses on providing ongoing monitoring and care for childhood cancer survivors through an alert system, which is not a standard treatment approach. This system aims to address the long-term health needs of survivors by integrating electronic health records and symptom management tools, which is different from traditional treatments that primarily focus on acute cancer care. ^{10 11 12 13 14}

What is the purpose of this trial?

The overall goal of this study is to attempt to overcome the organizational barriers that impede prompt screening for at-risk sensory deficits in childhood cancer survivors (CCS). Using a cross sectional design study, collaborators in the Informatics Research branch of the Institute of Informatics at the Washington University School of Medicine will identify CCS at risk for sensory deficits based upon their therapy exposure to generate the highlighting patients at risk for sensory screening (HPARSS) document. The investigators will utilize the HPARSS that will link therapy related risks for sensory deficits to specific screening procedures prompting the primary oncology provider to implement screening, diagnostic testing, and therapy.

Research Team

Robert J. Hayashi

Principal Investigator

Washington University School of Medicine

Eligibility Criteria

This trial is for childhood cancer survivors aged 7-17, who finished treatment between 6 months and 2 years ago at Washington University School of Medicine. They must speak English and not have had sensory deficit screening or therapy before.

Inclusion Criteria

I am receiving or have received chemotherapy or radiation therapy.

I am between 7 and 17 years old.

I was diagnosed with cancer before turning 18.

Exclusion Criteria

I have had tests or therapy for a condition that wasn't my main illness.

You or your parents cannot read or write.

In foster care or without a legal guardian

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Sensory Deficit Screening

Sensory deficit screening will be completed in the clinic for childhood cancer survivors identified as at risk based on prior treatment

9 months

Regular clinic visits as needed

Follow-up

Participants are monitored for safety and effectiveness after sensory deficit screening

4 weeks

Treatment Details

Interventions

• EHR-based CDS Alert System for Sensory Deficit Screening

Trial Overview The study tests a new system called HPARSS designed to help doctors identify kids at risk of sensory deficits after cancer treatment, prompting timely screening and intervention.

Participant Groups

Group I: Patients: Highlighting Patients at Risk for Sensory Screening (HPARSS) Experimental Treatment 1 Intervention

* The primary oncology team will utilize the HPARSS to identify patients who are at risk for sensory deficits based on prior treatment. The HPARSS will constitute a file of that will contain eligible patients based upon their past treatment and their corresponding risk for a particular sensory deficit based upon that treatment. The patient and parent/guardian will be approached to participate in the study. * Sensory deficit screening will be completed in the clinic and any screening results that indicate the need for referral for diagnostic testing or therapy will be scheduled by the primary treatment team staff.

Group II: Providers: Highlighting Patients at Risk for Sensory Screening (HPARSS) Active Control 1 Intervention

-Providers will complete a survey regarding their views of the HPARSS. The Acceptability of Intervention Measure has 4 questions and the Feasibility of Intervention Measure has 4 questions.

Who Is Running the Clinical Trial?

Washington University School of Medicine

Lead Sponsor

Trials

2,027

Recruited

2,353,000+

St. Jude Children's Research Hospital

Collaborator

Trials

451

Recruited

5,326,000+

Findings from Research

The Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC) effectively uses a Bayesian network to recommend optimal treatments, leading to significantly better 5-year metastasis-free probabilities for patients who followed its recommendations compared to those who did not.

In a 5-fold cross-validation, patients advised by DPAC had higher probabilities of being metastasis-free across various treatments, indicating its potential efficacy in guiding personalized breast cancer treatment, although some recommendations, like those regarding chemotherapy for node-negative patients, remain controversial.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. Jiang, X., Wells, A., Brufsky, A., et al.[2020]

A new alert system for inpatients successfully notified physicians about serious clinical conditions, with a high response rate of 70.2% to 1730 alerts over a 6-month period.

Physicians acted quickly, responding to 82.5% of alerts within 15 minutes, and indicated they would take action in 71.5% of cases, suggesting the system is effective in facilitating timely medical responses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Detecting alerts, notifying the physician, and offering action items: a comprehensive alerting system. Kuperman, GJ., Teich, JM., Bates, DW., et al.[2018]

The remote clinical decision support (CDS) service integrated with the Epic electronic health record showed a mean execution time of 0.74 seconds, indicating it can provide rapid recommendations for physicians treating children with minor blunt head trauma.

The CDS system was responsible for generating 60% of physician-initiated and 99% of nurse-initiated recommendations, demonstrating its effectiveness in supporting clinical decision-making in a multicenter trial setting.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. Goldberg, HS., Paterno, MD., Grundmeier, RW., et al.[2018]

A systematic review of 24 studies found that electronic clinical decision support (CDS) systems in oncology generally improve key outcomes, including reducing prescribing errors and medication-related safety events, particularly in computerized chemotherapy order entry.

Despite the positive findings, there is a significant lack of rigorous data on the effectiveness of CDS systems in oncology, highlighting the need for further research to fully understand their impact on patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice. Pawloski, PA., Brooks, GA., Nielsen, ME., et al.[2022]

The Appy CDS clinical decision support tool was developed to help identify pediatric patients at risk for appendicitis, successfully capturing similar populations with appendicitis rates of approximately 11.8% and 10.6% across two different health systems.

The tool is currently being evaluated in a pragmatic trial to reduce the need for advanced diagnostic imaging, indicating its potential to improve diagnosis and management of appendicitis in children.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of a Clinical Decision Support System for Pediatric Abdominal Pain in Emergency Department Settings Across Two Health Systems Within the HCSRN. Ekstrom, HL., Kharbanda, EO., Ballard, DW., et al.[2022]

Clinical decision support (CDS) systems integrated into electronic medical records are effective in detecting medication errors and adverse drug events, particularly in critically ill patients who are at higher risk.

Advancing CDS by using trajectory analyses and incorporating biomarkers can enhance alert systems, allowing for proactive adjustments in medication therapy to prevent adverse drug-related events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support for drug related events: Moving towards better prevention. Kane-Gill, SL., Achanta, A., Kellum, JA., et al.[2022]

A child abuse clinical decision support system (CA-CDSS) was successfully developed and implemented in two different electronic health record (EHR) systems, Epic and Allscripts, across multiple hospitals, demonstrating the feasibility of adapting such systems for different platforms.

The implementation process revealed significant variations in the types of triggers and alerts used in each EHR, highlighting the need for tailored adaptations and extensive usability evaluations to ensure effective integration and support for healthcare providers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record. McGinn, T., Feldstein, DA., Barata, I., et al.[2023]

The ADEAS system generated 2650 safety alerts for 931 patients over a 5-month period, with hospital pharmacists successfully contacting healthcare professionals in 10% of cases to prevent potential adverse drug events (ADEs).

The overall effectiveness of the clinical rules in ADEAS was low, with a rule effectiveness of 0.10 and a positive predictive value (PPV) of 0.08, indicating a need for refinement of the rules to improve their clinical relevance and efficiency.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice. Rommers, MK., Zwaveling, J., Guchelaar, HJ., et al.[2019]

The transition to a commercial electronic health record (EHR) system resulted in a significant increase in both the number of clinical decision support (CDS) alerts and the rate of overrides, particularly for drug-allergy and drug-drug interaction alerts, indicating potential challenges in alert management.

Despite the higher override rates in the commercial system, a review of overridden contraindicated drug-drug interactions found no adverse drug events, suggesting that the overrides did not lead to immediate harm, but highlighting the need for improved alert presentation and knowledge base to enhance patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System. Wong, A., Wright, A., Seger, DL., et al.[2018]

A new dataset has been developed to standardize the follow-up of childhood cancer survivors, focusing on the late effects of pediatric oncology treatments, through collaboration among multiple medical institutions in the Netherlands.

The PLEKsys computerized documentation system will be implemented across all Dutch pediatric oncology centers to facilitate the collection and evaluation of data on late effects, supporting the establishment of a National Pediatric Oncology Follow-up Registry.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The development of a new information model for a pediatric cancer registry on late treatment sequelae in The Netherlands. Jaspers, MW., Caron, H., Behrendt, H., et al.[2004]

Routine symptom screening in pediatric cancer care can enhance communication between patients and healthcare providers, potentially improving quality of life and even survival rates, as suggested by evidence from adult oncology.

Automating the reporting of adverse events through electronic health records (EHRs) could improve accuracy and reduce the workload for healthcare providers, which is crucial for managing both symptom and nonsymptom toxicities in pediatric cancer patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving symptom control and reducing toxicities for pediatric patients with hematological malignancies. Sung, L., Miller, TP., Phillips, R.[2021]

The Southern California Pediatric and Adolescent Cancer Survivorship (SC-PACS) consortium successfully enrolled 127 participants (65 childhood/adolescent cancer survivors and 62 parents/caregivers) to assess the survivorship needs of this population, highlighting the importance of monitoring and care for cancer survivors.

A significant majority (78%) of participants found the survivorship information useful, with 83% feeling they received adequate information about their cancer, indicating a strong demand for comprehensive survivorship care and education tailored to the unique needs of diverse cancer survivors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Southern California Pediatric and Adolescent Cancer Survivorship (SC-PACS): Establishing a Multi-Institutional Childhood, Adolescent, and Young Adult Cancer Survivorship Consortium in Southern California. Lin, C., Baca, N., Yun, C., et al.[2022]

A study involving 29 participants, including childhood cancer survivors and their caregivers, found that there is a high interest in using electronic personal health records (ePHRs) for managing health information, with 100% of survivors expressing interest.

Despite concerns about data security and privacy, most participants felt these issues would not hinder their willingness to adopt ePHRs, indicating a positive attitude towards digital health management tools.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic Personal Health Records for Childhood Cancer Survivors: An Exploratory Study. Sharp, LK., Carvalho, P., Southward, M., et al.[2021]

The SPARK web-based application was developed through three phases, involving 90 children aged 8-18, and showed significant improvements in usability and navigation, making it suitable for children undergoing cancer treatment.

In high-fidelity testing, 100% of participants understood how to use SPARK for symptom screening and found it easy to use, indicating its potential effectiveness in facilitating access to supportive care guidelines.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application. Cook, S., Vettese, E., Soman, D., et al.[2020]

References

1. United States pubmed.ncbi.nlm.nih.gov

A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. [2020]

2. United States pubmed.ncbi.nlm.nih.gov

Detecting alerts, notifying the physician, and offering action items: a comprehensive alerting system. [2018]

3. Ireland pubmed.ncbi.nlm.nih.gov

Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. [2018]

4. United States pubmed.ncbi.nlm.nih.gov

A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice. [2022]

5. England pubmed.ncbi.nlm.nih.gov

Development of a Clinical Decision Support System for Pediatric Abdominal Pain in Emergency Department Settings Across Two Health Systems Within the HCSRN. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Clinical decision support for drug related events: Moving towards better prevention. [2022]

7. Ireland pubmed.ncbi.nlm.nih.gov

Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record. [2023]

8. Netherlands pubmed.ncbi.nlm.nih.gov

Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice. [2019]

9. Germany pubmed.ncbi.nlm.nih.gov

Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System. [2018]

10. Netherlands pubmed.ncbi.nlm.nih.gov

The development of a new information model for a pediatric cancer registry on late treatment sequelae in The Netherlands. [2004]

11. United States pubmed.ncbi.nlm.nih.gov

Improving symptom control and reducing toxicities for pediatric patients with hematological malignancies. [2021]

12. United States pubmed.ncbi.nlm.nih.gov

Southern California Pediatric and Adolescent Cancer Survivorship (SC-PACS): Establishing a Multi-Institutional Childhood, Adolescent, and Young Adult Cancer Survivorship Consortium in Southern California. [2022]

13. United States pubmed.ncbi.nlm.nih.gov

Electronic Personal Health Records for Childhood Cancer Survivors: An Exploratory Study. [2021]

14. England pubmed.ncbi.nlm.nih.gov

Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application. [2020]