Audit Logs Research Articles

416 On-Call Junior Neurosurgery Residents Spend 9 Hours of Their On-Call Shift Actively Using the Electronic Health Record

INTRODUCTION: The Electronic Health Record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees. METHODS: We utilized the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and 3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and 3 years, which in our training program represent the primary participants in the in-house on-call pool. RESULTS: Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between active EHR time between residents as PGY-2 and PGY-3 on paired t-tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review. CONCLUSIONS: Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden which could be reduced.

APTHunter: Detecting Advanced Persistent Threats in Early Stages

We propose APTHunter, a system for prompt detection of Advanced and Persistent Threats (APTs) in early stages. We provide an approach for representing the indicators of compromise that appear in the cyber threat intelligence reports and the relationships among them as provenance queries that capture the attacker’s malicious behavior. We use the kernel audit log as a reliable source for system activities and develop an optimized whole system provenance graph that provides the causal relationships and information flows among system entities in a compact format. Then, we model the threat hunting as a behavior match problem by applying provenance queries to the optimized provenance graph to find any hits as indicators of an APT attack. We evaluate APTHunter on adversarial engagements from DARPA over different OS platforms, as well as real-world APT campaigns. Based on our experimental results, APTHunter promptly and reliably detects attack artifacts in early stages.

A Blockchain-Enabled Framework for Improving the Software Audit Process

Audits are an essential component of every organization, particularly those involving software development. In addition to several testing cycles, software auditing has become an essential software development milestone. Software auditing is a continual activity that enables a business to remain ahead of the curve and predict potential software problems. Audits, whether undertaken in-house or by external auditors, entail a significant amount of time and work. Consistent audits provide financial and economic benefits, as well as legal benefits. The most essential advantage of audits is safeguarding your system from internal and external assaults. Audit logs serve a crucial role in the auditing process; they typically capture all system operations and occurrences. They are used as evidence providers during an inquiry and by auditors to monitor the privacy and security of information and systems. Auditors confirm the accuracy of data pertaining to businesses and their activities. To determine if these acts exceed the limitations established by organizations, governments, and other parties, dependable information is essential. Infractions of such rules or corporate standards may be indicative of fraud, malpractice, risk, or inefficiency. Despite the existence of automated audit tools, audit policy, and audit logs, many audit frauds are reported on a daily basis. To make the audit process transparent and secure, this research proposes a blockchain-enabled framework SSFTA to aid software auditors in conducting a transparent and effective audit process. The proposed framework is evaluated using a case study. The findings demonstrated that the suggested framework makes the auditing process simple and transparent.

Evaluation of a national e-booking system for medical consultation in primary care in a universal health system

PurposeThe Rendez-vous Santé Québec is a national online booking (e-booking) system of medical appointments in primary care rolled out in 2018 in Québec (Canada). The objectives of this study were to describe the adoption by targeted users, and analyze the facilitating and limiting factors at the technological, individual and organizational levels to inform policy makers. MethodsA mixed methods evaluation was conducted involving interviews with key stakeholders (n = 40), audit logs of the system in 2019, and a population-based survey (n = 2 003). All data were combined to analyze facilitating and limiting factors, based on the DeLone and McLean framework. ResultsThe RVSQ e-booking system had a low adoption across the province mainly because it was poorly aligned with the diversity of organizational and professional practices. The other commercial e-booking systems already used by clinics seemed better adapted to interdisciplinary care, patient prioritization and advanced access. e-Booking system was appreciated by patients, but has implications for the performance of primary care organization that goes beyond scheduling management issues, with potential detrimental consequences for care continuity and appropriateness. Further research is needed to define how e-booking systems could support a better alignment between primary care innovative practices and improve the fit between patients' needs and resources availability in primary care.

Identifying a Clinical Informatics or Electronic Health Record Expert Witness for Medical Professional Liability Cases.

The health care field is experiencing widespread electronic health record (EHR) adoption. New medical professional liability (i.e., malpractice) cases will likely involve the review of data extracted from EHRs as well as EHR workflows, audit logs, and even the potential role of the EHR in causing harm. Reviewing printed versions of a patient's EHRs can be difficult due to differences in printed versus on-screen presentations, redundancies, and the way printouts are often grouped by document or information type rather than chronologically. Simply recreating an accurate timeline often requires experts with training and experience in designing, developing, using, and reviewing EHRs and audit logs. Additional expertise is required if questions arise about data's meaning, completeness, accuracy, and timeliness or ways that the EHR's user interface or automated clinical decision support tools may have contributed to alleged events. Such experts often come from the sociotechnical field of clinical informatics that studies the design, development, implementation, use, and evaluation of information and communications technology, specifically, EHRs. Identifying well-qualified EHR experts to aid a legal team is challenging. Based on literature review and experience reviewing cases, we identified seven criteria to help in this assessment. The criteria are education in clinical informatics; clinical informatics knowledge; experience with EHR design, development, implementation, and use; communication skills; academic publications on clinical informatics; clinical informatics certification; and membership in informatics-related professional organizations. While none of these criteria are essential, understanding the breadth and depth of an individual's qualifications in each of these areas can help identify a high-quality, clinical informatics expert witness.

Relationships Between EHR-Based Audit Log Data and Physician Burnout and Clinical Practice Process Measures

ObjectiveTo explore the relationship of electronic health record (EHR)–based audit log data with physician burnout and clinical practice process measures. MethodsFrom September 4 to October 7, 2019, we surveyed physicians in a larger academic medical department and matched responses to August 1 through October 31, 2019, EHR-based audit log data. Multivariable regression analysis evaluated the relationship between log data and burnout and the interrelationship between log data and turnaround time for In Basket messages and percentage of encounters closed within 24 hours. ResultsOf the 537 physicians surveyed, 413 (77%) responded. On multivariable analysis, number of In Basket messages received per day (each additional message: odds ratio, 1.04 [95% CI, 1.02 to 1.07]; P<.001) and time spent in the EHR outside scheduled patient care (each additional hour: odds ratio, 1.01 [95% CI, 1.00 to 1.02]; P=.04) were associated with burnout. Time spent doing In Basket work (each additional minute: parameter estimate, −0.11 [95% CI, −0.19 to −0.03]; P=.01) and in the EHR outside scheduled patient care (each additional hour: parameter estimate, 0.04 [95% CI, 0.01 to 0.06]; P=.002) were associated with turnaround time (days per message) for In Basket messages. None of the variables explored were independently associated with percentage of encounters closed within 24 hours. ConclusionElectronic health record–based audit log data of workload relate to odds of burnout and responsiveness to patient-related inquiries and results. Further study is needed to determine whether interventions that reduce the number of and time spent doing In Basket messages or time spent in the EHR outside scheduled patient care reduce physician burnout and improve clinical practice process measures.

Classifying clinical work settings using EHR audit logs: a machine learning approach.

We used electronic health record (EHR)-based raw audit logs to classify the work settings of anesthesiology physicians providing care in both surgical intensive care units (ICUs) and operating rooms. Observational study. Attending anesthesiologists who worked at least 1 shift in 1 of 4 surgical ICUs in calendar year 2019 were included. Time-stamped EHR-based audit log events for each week were used to create event frequencies and represented as a term frequency-inverse document frequency matrix. Primary classification outcome of interest was a physician's clinical work setting. Performance of multiple supervised machine learning classifiers were evaluated. A total of 24 attending physicians were included; physicians performed a median (IQR) of 2545 (906-5071) EHR-based actions per week and worked a median (IQR) of 5 (3-7) weeks in a surgical ICU. A random forest classifier yielded the best discriminative performance (mean [SD] area under receiver operating characteristic curve, 0.88 [0.05]; mean [SD] area under precision-recall curve, 0.72 [0.13]). Model explanations illustrated that clinical activities related to signing of clinical notes, printing handoff data, and updating diagnosis information were associated with the positive prediction of working in a surgical ICU setting. A random forest classifier using a frequency-based feature engineering approach successfully predicted work settings of physicians with multiple clinical responsibilities with high accuracy. These findings highlight opportunities for using audit logs for automated assessment of clinician activities and their work settings, thereby affording the ability to accurately assess context-specific work characteristics (eg, workload).

Mapping of Virtual Machines Using Machine Learning Algorithms for Detection of Faulty Nodes

A distributed system is characterized by a large number of nodes that are linked to a network and are mostly used for transaction processing. Large set of users are likely to communicate information over the network to the nodes, consistency and dependability remain a critical problem in the distributed environments. Independent failure of the component is one of the major problems in the distributed systems as it slowly impacts the performance of the other nodes in the system. The quality of service - QoS of a distributed network may be improved by a quick way of detecting problematic nodes. Sometime heavy nodes required high computation for transaction processing while idle nodes take low computation. In this paper, we proposed identification of straggler nodes in distributed environment with the help of hybrid machine learning algorithm. The work basically carried out to set up of large number of virtual machines and collect current log audits of each VM. According to the available parameters of audit files to each machine, algorithms decide that specific node is overheated or ideal condition. In expensive experimental analysis we demonstrate a accuracy of proposed hybrid machine learning algorithm. The proposed algorithm produces higher precision up to 4.5% than state-of-art methods. Key highlights of the VM mapping strategy were also investigated through a scrutiny of ongoing contracts. Main focus remains on machine learning (ML) to distinguish PM (Physical Machine) congestion, determining VMs from crowded PMs, and VM conditions as major exercises. This paper aims to review and characterize research on the planning and status of VMs that use ML using asset usage history. Energy productivity, VM migration, and quality of service were the main exhibition boundaries used to investigate cloud data center presentations.

THE IMPACT OF OWNERSHIP STRUCTURE AND DEBT ON AUDIT QUALITY IN NON-FINANCIAL COMPANIES LISTED ON ASE

Financial information issued by companies is vital to investors and capital providers for their investment decisions, while factors that impact audit quality should be ascertained. In Jordan, the effect of ownership structure and debt on audit quality has not been adequately studied. The current study purposes to examine the effect of ownership structure and debt on audit quality for non-financial companies listed on the Amman Stock Exchange. The ownership structure and debt represent the independent variables in this study, while audit quality is the dependent variable. Managerial ownership (MANOW), family ownership (FAMOW) and foreign ownership (FOROW) were used as proxies for the ownership structure, debt (DEBT) was represented by debt to equity, while audit quality (AQ) was represented by a log audit fee. Panel data approach with annual reports of 88 ASE listed industrial and services companies were employed, covering the 2009-2019 period. fixed effect and 2SLS methods were employed to examine the study hypotheses. The results of the study indicate the importance of the study variables in influencing the audit quality, ownership structure (MANOW, FAMOW, FOROW) positively affects the audit quality, and increased debt positively affects the audit quality.

Measurement and automation of workflows for improved clinician interaction: upgrading EHRs for 21st century healthcare value.

In this editorial, we highlight 5 manuscripts that address aspects of clinician interaction with the electronic health record (EHR). A Perspective describes the efforts of the Office of the National Coordinator for Health Information Technology (ONC) in establishing priorities for workflow automation in healthcare settings.1 A second Perspective summarizes a panel sponsored by the American College of Medical Informatics (ACMI) at the 2021 AMIA Symposium that examined a provocative question: Are EHRs dumbing down clinicians?2 Three manuscripts focus on what can be learned from EHR audit log data.3–5 First, Zayas-Cabán et al1 describe how the ONC led a multidisciplinary effort of stakeholders from industrial engineering, computer science, and finance to investigate automation in health care. They define automation as “the creation and application of technology to monitor and control the delivery of products and services.”6 The process of key informant interviews, focus groups, and a review of pertinent literature identified 6 priorities and their related strategies for advancing workflow automation. These priorities focused on leveraging high quality, interoperable data, and engaging clinicians in the EHR design, implementation, and evaluation processes. Then, relevant, and effective workflows could be identified that add value, not a burden, in clinician processes. The supporting strategies revolved around education, convening multiple interested parties, prioritizing appropriate workflows, and using policies and the market to incentivize automated solutions' development, testing, and evaluation. Examples of priority workflows for automation include reimbursement and prior authorizations, medication reconciliation, care management for newly diagnosed patients, and public health and adverse event reporting. The goal is to upgrade EHR technology to increase efficiency, improve health outcomes, and deliver more value for patients, caregivers, clinicians, and staff involved in healthcare. These priorities and strategies provide some solutions to the inadequacies of EHRs as outlined by Melton et al.2

On-Call Junior Neurosurgery Residents Spend 9 hours of Their On-Call Shift Actively Using the Electronic Health Record.

The electronic health record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees. To describe the amount of EHR time spent by postgraduate year (PGY)-2 and PGY-3 neurosurgery residents during on-call days and the distribution of EHR activities in which they engage. This cohort study used the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and PGY-3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and PGY-3, which, in our training program, represent the primary participants in the in-house on-call pool. Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between Active EHR Time between residents as PGY-2 and PGY-3 on paired t -tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review. Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden, which could be reduced.

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach.

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15767634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

Using machine learning techniques for insider threat detection

This paper presents an analysis of algorithms and approaches used to solve the problem of identifying insider threats using machine learning techniques. Internal threat detection in the context of this research is reduced to the task of detecting anomalies in the audit logs of access subjects' actions. The paper formalizes the main directions of insider threats detection and presents popular machine learning algorithms. The paper raises the problem of objective evaluation of research and development in the subject area. Based on the analysis recommendations for the implementation of internal threat detection systems using machine learning algorithms are developed.

SMURF: Efficient and Scalable Metadata Access for Distributed Applications

In parallel with big data processing and analysis dominating the usage of distributed and Cloud infrastructures, the demand for distributed metadata access and transfer has increased. The volume of data generated by many application domains exceeds petabytes, while the corresponding metadata amounts to terabytes or even more. This article proposes a novel solution for efficient and scalable metadata access for distributed applications across wide-area networks, dubbed SMURF. Our solution combines novel pipelining and concurrent transfer mechanisms with reliability, provides distributed continuum caching and semantic locality-aware prefetching strategies to sidestep fetching latency, and achieves scalable and high-performance metadata fetch/prefetch services in the Cloud. We incorporate the phenomenon of semantic locality awareness for increased prefetch prediction rate using real-life application I/O traces from Yahoo! Hadoop audit logs and propose a novel prefetch predictor. By effectively caching and prefetching metadata based on the access patterns, our continuum caching and prefetching mechanism significantly improves the local cache hit rate and reduces the average fetching latency. We replay approximately 20 Million metadata access operations from real audit traces, where SMURF achieves 90% accuracy during prefetch prediction and reduced the average fetch latency by 50% compared to the state-of-the-art mechanisms.

Audit Trail in Pharma: A Review

“Audit trail” (also known as audit log) management in the pharmaceutical industry has become an integral part of the quality system because of the current international regulatory demand associated with a pharmaceutical product. Regulators, industry, and standards-setting organizations have recognized the significance of an audit trail, as it is associated with data integrity problems. Industries need to rely more on a risk-based approach to the review of audit trails and establish more nimble and flexible standards to complement these efforts. Pharmaceutical audits by governing bodies issued warning letters to many companies, and they have recognized that quality needs to be built into systems and processes throughout the lifecycle of the product. The audit trails feature is a key source and helps manufacturers to minimize the risk, to maintain the reliability, and security of electronic data to recover lost data. The review study shares some insight into the anatomy of an audit trail, what are its benefits, its types, features, regulatory guidance that are associated with various regulatory agencies, the review of an audit trail, and how to best comply with the audit trail. This article reiterates an interpretation that an audit trail feature plays a vital role to assure compliance.

Team is brain: leveraging EHR audit log data for new insights into acute care processes.

To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. We selected one widely-used process outcome: emergency department (ED)-based team time to deliver tissue plasminogen activator (tPA) to patients with acute ischemic stroke (AIS). We evaluated Epic audit log data (that tracks EHR user-interactions) for 3052 AIS patients aged 18+ who received tPA after presenting to an ED at three Northern California health systems (Stanford Health Care, UCSF Health, and Kaiser Permanente Northern California). Our primary outcome was door-to-needle time (DNT) and we assessed bivariate and multivariate relationships with six audit log-derived measures of treatment team busyness and prior team experience. Prior team experience was consistently associated with shorter DNT; teams with greater prior experience specifically on AIS cases had shorter DNT (minutes) across all sites: (Site 1: -94.73, 95% CI: -129.53 to 59.92; Site 2: -80.93, 95% CI: -130.43 to 31.43; Site 3: -42.95, 95% CI: -62.73 to 23.17). Teams with greater prior experience across all types of cases also had shorter DNT at two sites: (Site 1: -6.96, 95% CI: -14.56 to 0.65; Site 2: -19.16, 95% CI: -36.15 to 2.16; Site 3: -11.07, 95% CI: -17.39 to 4.74). Team busyness was not consistently associated with DNT across study sites. EHR audit log data offers a novel, scalable approach to measure key contextual factors relevant to clinical process outcomes across multiple sites. Audit log-based measures of team experience were associated with better process outcomes for AIS care, suggesting opportunities to study underlying mechanisms and improve care through deliberate training, team-building, and scheduling to maximize team experience.

Using electronic health record audit log data for research: insights from early efforts.

Electronic health record audit logs capture a time-sequenced record of clinician activities while using the system. Audit log data therefore facilitate unobtrusive measurement at scale of clinical work activities and workflow as well as derivative, behavioral proxies (eg, teamwork). Given its considerable research potential, studies leveraging these data have burgeoned. As the field has matured, the challenges of using the data to answer significant research questions have come into focus. In this Perspective, we draw on our research experiences and insights from the broader audit log literature to advance audit log research. Specifically, we make 2 complementary recommendations that would facilitate substantial progress toward audit log-based measures that are: (1) transparent and validated, (2) standardized to allow for multisite studies, (3) sensitive to meaningful variability, (4) broader in scope to capture key aspects of clinical work including teamwork and coordination, and (5) linked to patient and clinical outcomes.

LogKernel: A Threat Hunting Approach Based on Behaviour Provenance Graph and Graph Kernel Clustering

Cyber threat hunting is a proactive search process for hidden threats in an organization’s information system. It is a crucial component of active defense against advanced persistent threats (APTs). However, most of the current threat hunting methods rely on Cyber Threat Intelligence (CTI), which can find known attacks but cannot find unknown attacks that have not been disclosed by CTI. In this paper, we propose LogKernel, a threat hunting method based on graph kernel clustering which can effectively separate attack behaviour from benign activities. LogKernel first abstracts system audit logs into behaviour provenance graphs (BPGs) and then clusters graphs by embedding them into a continuous space using a graph kernel. In particular, we designed a new graph kernel clustering method based on the characteristics of BPGs, which can capture both structure information and rich label information of the BPGs. To reduce false positives, LogKernel further quantifies the threat of abnormal behaviour. We evaluate LogKernel on the malicious dataset, which includes seven simulated attack scenarios, and the DAPRA CADETS dataset, which includes four attack scenarios. The result shows that LogKernel can hunt all attack scenarios among them, and compared to the state-of-the-art methods, it can find unknown attacks.

Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation.

BackgroundElectronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging.ObjectiveThis study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency.MethodsWe developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score–matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases.ResultsTime-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between the audit data–derived workflow measures and the time-motion observation. Two key unobservable times from audit data, physician entry into and exiting the examination room, were imputed based on time-motion studies. CM-SHARE was launched with 6 pilot PCPs in April 2016. During the prestudy period (April 1, 2015, to April 1, 2016), 870 control patients with 2845 encounters were matched with 870 case patients and encounters, and 727 case patients with 852 encounters were matched with 727 control patients and 3754 encounters in the poststudy period (June 1, 2016, to June 30, 2017). Total encounter time was slightly shorter (mean −2.7, SD 1.4 minutes, 95% CI −4.7 to −0.9; mean –1.6, SD 1.1 minutes, 95% CI −3.2 to −0.1) for cases than controls for both periods. CM-SHARE saves physicians approximately 2 minutes EHR time in the examination room (mean −2.0, SD 1.3, 95% CI −3.4 to −0.9) compared with prestudy period and poststudy period controls (mean −1.9, SD 0.9, 95% CI −3.8 to −0.5). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases with 3412 control patients, having 1119 and 4240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (≤20 minutes; 5.3-minute reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17%-33% reductions).ConclusionsCombining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings.

DELIA: Distributed Efficient Log Integrity Audit Based on Hierarchal Multi-Party State Channel

Audit log contains the trace of different activities in computing systems, which makes it critical for security management, censorship, and forensics. However, experienced attackers may delete or modify the audit log after their attacks, which makes the audit log unavailable in attack investigation. In this article, we focus on the log integrity audit in the same domain, in which a number of servers update audit logs for a single or several organizations as an alliance. We propose a distributed efficient log integrity audit framework, called DELIA, which employs the distributed ledger technique to protect audit information, and utilizes the idea of state channel to improve the throughput of distributed ledger. To generate stable state from the rapidly-updated logs in the domain, we propose a log state generation scheme, which not only generates state suitable for audit logs, but also enables mutual supervision within the domain. To overcome the high latency in existing state channel schemes, we propose a hierarchal multi-party state channel scheme, which makes the latency in our framework independent of the number of servers in the domain. We implement DELIA on Ethereum and evaluate its performance. The results show that our framework is efficient and secure in practice.