A new approach to clinical research: Integrating clinical care, quality reporting, and research using a wound care network-based learning healthcare system

2017 ◽  
Vol 25 (3) ◽  
pp. 354-365 ◽  
Author(s):  
Thomas E. Serena ◽  
Caroline E. Fife ◽  
Kristen A. Eckert ◽  
Raphael A. Yaakov ◽  
Marissa J. Carter
Keyword(s):  
Clinical Research ◽  
Healthcare System ◽  
Wound Care ◽  
Clinical Care ◽  
Care Quality ◽  
New Approach ◽  
Care Network ◽  
Quality Reporting ◽  
Learning Healthcare System

The Healthcare System

2019 ◽  
pp. 3-6
Author(s):  
Mark E. Frisse ◽  
Karl E. Misulis
Keyword(s):  
Healthcare System ◽  
Financial Distress ◽  
Clinical Care ◽  
Critical Role ◽  
The United States ◽  
Care Quality ◽  
Clinical Informatics ◽  
System P ◽  
Per Capita ◽  
United States Healthcare

The United States healthcare system ranks the highest in per capita expense but ranks far lower with respect to patient access and health outcomes. An aging and increasingly ill population, family financial distress, changing cultural expectations, and unsustainable healthcare prices will necessitate a radically broader view of clinical care and system change. Clinical informatics will play a critical role in essential transformation efforts aimed at improving care quality in financially sustainable ways.

scholarly journals A HOSPICE COLLABORATIVE NETWORK TO IMPROVE SERIOUS ILLNESS CARE IN A LARGE HEALTHCARE SYSTEM

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S132-S132
Author(s):  
Richard E Leiter ◽  
Charles T Pu ◽  
Emanuele Mazzola ◽  
Rachelle E Bernacki
Keyword(s):  
Healthcare System ◽  
Hospice Care ◽  
Clinical Care ◽  
The United States ◽  
Care Quality ◽  
Collaborative Network ◽  
Initial Attempt ◽  
Modified Delphi ◽  
Seriously Ill Patients ◽  
Percentile Scores

Abstract The quality of hospice care in the United States varies significantly, yet healthcare systems lack methods to comprehensively evaluate and stimulate quality improvement in organizations that serve their patients. Partners HealthCare, an integrated healthcare system located in Eastern Massachusetts, sought to create a high-quality hospice collaborative network based on objective and quantitative criteria obtained from public reporting as well as the hospice itself. Through a modified Delphi procedure, clinicians, administrators, and data scientists developed a set of criteria and a scoring system focused on three areas: organizational information, clinical care quality indicators, and training and satisfaction. All Medicare-certified hospices in good-standing in Massachusetts were eligible to participate in a request for information (RFI) process. We blinded all hospice data prior to scoring and invited hospices scoring above the 15th percentile to join the collaborative for a 2-year initial term. Of 72 eligible hospices, the majority (53%) responded to the RFI, of which 60% submitted completed surveys. Hospices could receive up to 23.75 points with scores ranging from 2.25 to 19.5. The median score was 13.62 (IQR: 10.5-16.75). For the 19 hospices scoring above the 15th percentile, scores ranged from 10.0-19.5 (median: 14, IQR: 11.1-16.9). There was no association between quality score and continuous (Spearman’s correlation 0.24, p=0.27) or dichotomous (Wilcoxon rank sum test p=0.13) measures of hospice size. The hospice collaborative network is one healthcare system’s initial attempt to effectively leverage its influence and relationships to improve hospice quality for the benefit of its seriously ill patients and their families.

Impact of curated data on electronic quality measure capture rates within CancerLinQ.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 307-307
Author(s):  
Katrina Caridad Rios ◽  
Arpitha Thakkalapally ◽  
Jacob Koskimaki ◽  
Mark Riffon ◽  
Robert S. Miller ◽  
...  
Keyword(s):  
Language Processing ◽  
Clinical Care ◽  
Office Visit ◽  
Quality Measures ◽  
Care Quality ◽  
Structured Data ◽  
Quality Measure ◽  
Patient Journey ◽  
Quality Reporting ◽  
Data Elements

307 Background: Accurate calculation of key quality measures is critical for informing high-quality, value-based cancer care that is consistent with clinical guidelines. The American Society of Clinical Oncology (ASCO)’s CancerLinQ enables oncology organizations around the US to view near-real time quality measure dashboards sourced from structured electronic medical record (EMR) data; however, use of structured data in key fields is highly variable. Unstructured content, such as progress notes, contains important clinical information on treatment and disease status, which can then undergo curation. This process involves trained data abstractors searching for key data elements through a combination of manual review and natural language processing (NLP) to extract structured data from unstructured content. We hypothesize inclusion of curated data substantially augments structured data alone by more accurately representing the patient journey, thus improving validity of quality measures across EMRs. Methods: A total of 96,399 records across 57,232 patients from 4 EMRs vendors were analyzed from 2018-2019 across structured EMR and curated data. Each record represents 1 of 7 key data elements used to calculate the Staging Documented within One Month of First Office Visit quality measure. Structured documentation of these data elements determines if a patient is concordant with the measure, meaning they were staged within 31 days of their first visit after diagnosis, or non-concordant, meaning they were not staged within the appropriate window. Results: More than a quarter of records from patients concordant or non-concordant with the measure (28.85%) had key data elements sourced from curation. In total, 33% of all records among concordant patients were sourced from curation. Relying on structured data alone would show only 67% concordance versus 97.5% concordance among curated records. This demonstrates that appropriate care may often be delivered but documentation may be missing in a significant fraction of structured EMR data, thus limiting accurate reporting capabilities. Conclusions: NLP-assisted curation can meaningfully supplement structured EMR data by providing a more accurate picture of care rendered, which can have substantial impacts on clinical care, quality reporting, and business operations. [Table: see text]

scholarly journals Developing the infrastructure to support the optimisation of antibiotic prescribing using the learning healthcare system to improve healthcare services in the provision of primary care in England

2020 ◽  
Vol 27 (1) ◽  
pp. e100147
Author(s):  
Victoria Palin ◽  
Edward Tempest ◽  
Chirag Mistry ◽  
Tjeerd P van Staa
Keyword(s):  
Primary Care ◽  
Healthcare System ◽  
Clinical Care ◽  
Healthcare Services ◽  
Antibiotic Prescribing ◽  
Successful Implementation ◽  
Care Providers ◽  
Level Data ◽  
Learning Healthcare System ◽  
Periodic Feedback

IntroductionThe learning healthcare system (LHS) underpinned by data analysis and feedback to clinical care providers is thought to improve quality of care. The work aimed to implement an LHS for antibiotic prescribing in primary care in England.MethodDeidentified patient-level data from general practices were processed and analysed at regular intervals (fortnightly increments). A dashboard application was developed and implemented displaying analytical graphics to give periodic feedback to clinicians, tailored to each clinical site. Benchmarking parameters were established by the analysis of two large national primary care datasets allowing peer-to-peer comparisons. To date, the dashboard is available to 70 English practices.ConclusionsSuccessful implementation and uptake of the secure technical LHS infrastructure for the analysis and feedback to clinicians of their antibiotic prescribing demonstrate a great appetite for this type of frequent prescribing review in primary care, combining advanced data analytics with tailored feedback.

scholarly journals Reconsidering ‘minimal risk’ to expand the repertoire of trials with waiver of informed consent for research

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e048534
Author(s):  
Paul A Monach ◽  
Westyn Branch-Elliman
Keyword(s):  
Clinical Research ◽  
Healthcare System ◽  
Clinical Decision Making ◽  
Clinical Equipoise ◽  
Minimal Risk ◽  
Pragmatic Trials ◽  
Federal Drug Administration ◽  
Regulatory Burden ◽  
Learning Healthcare System ◽  
Evidence Generation

BackgroundProgress in therapeutic research is slowed by the regulatory burden of clinical trials, which provide the best evidence for guiding treatment. There is a long delay from evidence generation to adoption, highlighting the need for designs that link evidence generation to implementation.ObjectiveTo identify clinical trial designs that confer minimal risk above that inherent in clinical care, to obviate the need for cumbersome consenting processes to enrol patients in prospective clinical research studies. These designs extend the scope of the Learning Healthcare System, a framework for leveraging retrospective ‘big data’ to advance clinical research, to include data collected from prospective controlled trials.SummaryPragmatic trials may use simplified eligibility criteria, unblinded interventions and objective outcome measures that can all be monitored through the electronic health records (EHR), to reduce costs and speed study conduct. Most pragmatic trials continue to suffer from substantial regulatory burden. Written consent to participate in research can be waived only if the research produces minimal risk above what is encountered in everyday life. However, the ‘consent’ processes for prescribing Federal Drug Administration-approved medications in clinical medicine are informal, even when they involve decisions of uncertain benefit and higher levels of risk. We propose that trial designs that mimic clinical decision-making in areas of uncertainty (clinical equipoise) and in which no data are generated outside of usual care (ideally by EHR embedding) confer minimal additional risk. Trial designs meeting this standard could, therefore, be conducted with minimal documentation of consent, even when interventions contain different risks. To align with risk encountered in clinical practice, allocation to treatment arms should change (adaptive randomisation) as data are collected and analysed. Embedding of informatics tools into the EHR has the additional benefit that, as adaptive randomisation progresses, evidence-generation transitions into implementation via decision-support tools—the ultimate realisation of the Learning Healthcare System.

scholarly journals Patient Experience in Home Respiratory Therapies: Where We Are and Where to Go

2019 ◽  
Vol 8 (4) ◽  
pp. 555 ◽  
Author(s):  
Cátia Caneiras ◽  
Cristina Jácome ◽  
Sagrario Mayoralas-Alises ◽  
José Ramon Calvo ◽  
João Almeida Fonseca ◽  
...  
Keyword(s):  
Clinical Research ◽  
Patient Experience ◽  
Clinical Care ◽  
Research Priorities ◽  
Specific Patient ◽  
Quality Healthcare ◽  
Body Of Knowledge ◽  
Number Of Patients ◽  
Patient Reported ◽  
System Sustainability

The increasing number of patients receiving home respiratory therapy (HRT) is imposing a major impact on routine clinical care and healthcare system sustainability. The current challenge is to continue to guarantee access to HRT while maintaining the quality of care. The patient experience is a cornerstone of high-quality healthcare and an emergent area of clinical research. This review approaches the assessment of the patient experience in the context of HRT while highlighting the European contribution to this body of knowledge. This review demonstrates that research in this area is still limited, with no example of a prescription model that incorporates the patient experience as an outcome and no specific patient-reported experience measures (PREMs) available. This work also shows that Europe is leading the research on HRT provision. The development of a specific PREM and the integration of PREMs into the assessment of prescription models should be clinical research priorities in the next several years.

scholarly journals Development and implementation of standardized study performance metrics for a VA healthcare system clinical research consortium

2021 ◽  
pp. 106505
Author(s):  
Marcus R. Johnson ◽  
Merritt Raitt ◽  
Aliya Asghar ◽  
Debra L. Condon ◽  
Danielle Beck ◽  
...  
Keyword(s):  
Clinical Research ◽  
Healthcare System ◽  
Performance Metrics ◽  
Research Consortium ◽  
Study Performance
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