Learning bundled care opportunities from electronic medical records

Chen, You; Kho, Abel N.; Liebovitz, David; Ivory, Catherine H.; Osmundson, Sarah S.; Bian, Jiang; Malin, Bradley

doi:10.1016/j.jbi.2017.11.014

Cited by 19 publications

(20 citation statements)

References 55 publications

(66 reference statements)

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“…The category with the most papers listed was Diseases of the circulatory system followed by Neoplasms. Two papers [16,21] were not included in Table 6, since several hundred diseases and health problems were cited and classified using ICD-9. Of the remaining 36 case studies, ICD-10 was already used in 8 papers to code the diagnosis [12,14,21,22,33,34,38,40].…”

Section: Medical Diagnosismentioning

confidence: 99%

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Helm

Lin

Baumgartner

et al. 2020

IJERPH

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Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

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Section: Medical Diagnosismentioning

confidence: 99%

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Helm

Lin

Baumgartner

et al. 2020

IJERPH

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“…The integration of EMR data with ML algorithms also has huge ramifications on reimbursement processes, particularly in creating new classifications systems for bundled care models. 48,65 Moreover, all of the aforementioned impacts on risk stratification, personalized treatment algorithms, and clinical prognostication will allow for more accurate reimbursement models and financial optimization of clinical practice.…”

Section: Reimbursementmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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“…We extracted all provider interactions with the patient's EHR, including opening and modifying records (e.g., documentation [including procedure notes], observations, measurements, diagnoses, and orders), along with timestamps from the EHR system. [23][24][25][26][27] Data were extracted from the VUMC health care electronic systems for 3 perioperative days: 1 day before surgery, the day of surgery, and 1 day after surgery.…”

Section: Datasetsmentioning

confidence: 99%

“…To solve the challenges raised by claims data, many types of research leveraged EHR log data and data mining technologies to infer clinical workflows 23,24 and care team structures. [25][26][27] However,…”

Section: Introductionmentioning

confidence: 99%

“…these studies limited care team structure or workflow exploration to high levels of abstraction such as across the entire health care system. [23][24][25][26][27] While few have focused on modeling care team structures in specific settings, such as the NICU, there are some studies that relied on progress notes in the EHR to model handoff patterns between NICU providers. 28 Yet these studies only considered NICU nurses and neglected the roles of other providers (e.g., neonatologists, pediatric surgeons, or respiratory therapists).…”

Section: Introductionmentioning

confidence: 99%

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Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs

et al. 2019

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Background In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). Methods The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. Results The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03). Conclusion Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

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Learning bundled care opportunities from electronic medical records

Abstract: The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

Cited by 19 publications

References 55 publications

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs

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