Documentation of goals of care discussions: Lessons from the Improving Goal Concordant Care Collaborative.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 166-166
Author(s):  
Kristen K. McNiff Landrum ◽  
Akhila Sunkepally Reddy ◽  
Tom Ross ◽  
Jack Kolosky ◽  

166 Background: Patients with advanced cancer do not reliably receive care consistent with their goals and values. Goal-concordant care requires effective, efficient and timely communications between patients and their providers, as well as systems to capture patient goals and ensure future accessibility. Currently, electronic health records (EHR) in most oncology settings do not adequately support structured documentation that is most relevant to goals of cancer care. Methods: The Alliance of Dedicated Cancer Centers (ADCC) initiated the Improving Goal Concordant Care (IGCC) Initiative in 2019. ADCC members are 10 U.S. freestanding, academic cancer hospitals, which are also the IGCC participants. In 2019 and 2020, we convened palliative care and oncology experts in the IGCC’s planning phase, via a series of structured consensus building sessions. We employed modified Delphi processes - including literature review, brainstorming, voting, and refinement - in conceptualization four core components considered essential to improving goal concordant care for cancer patients. One of these core components is EHR documentation of GOC discussions; an additional EHR workgroup created detailed recommendations. The three-year IGCC implementation phase launched in September 2020. Results: We achieved consensus on minimum desired fields for GOC documentation: intent of the current treatment, physician's estimated prognosis, prognosis disclosed/discussed with patient, patient prognostic awareness, patient goals, recommendations. Further, GOC documentation must be discrete and structured whenever possible to ease entry and facilitate retrieval/reporting. As GOC discussions evolve over time, documentation may be iterated over multiple encounters. GOC documentation is distinct from, but may rely on, advance directives or other ACP documentation, such as code status, POLST/MOLST, and healthcare agents. At IGCC collaborative launch, none of the 10 cancer hospitals had EHRs that were fully compliant. Progress toward development, training, and use of structured GOC documentation is ongoing. Conclusions: Establishing feasible and useful expectations for electronic documentation of GOC discussions by oncologists presented unique challenges for the IGCC. Ongoing and planned work in this area includes: Facilitating collaborative learning and promoting sharing of best practices; Measuring the presence of GOC documentation among priority patients, as well as other measures including patient and family reported outcomes; Assessing quality of the GOC documentation; Ongoing engagement of patient and family advisors, including regarding patient access to records containing GOC documentation; Potential application of natural language processing/artificial intelligence in prompting GOC documentation and facilitating retrieval.

2021 ◽  
Author(s):  
Anahita Davoudi ◽  
Hegler Tissot ◽  
Abigail Doucette ◽  
Peter E Gabriel ◽  
Ravi B. Parikh ◽  
...  

One core measure of healthcare quality set forth by the Institute of Medicine is whether care decisions match patient goals. High-quality "serious illness communication" about patient goals and prognosis is required to support patient-centered decision-making, however current methods are not sensitive enough to measure the quality of this communication or determine whether care delivered matches patient priorities. Natural language processing offers an efficient method for identification and evaluation of documented serious illness communication, which could serve as the basis for future quality metrics in oncology and other forms of serious illness. In this study, we trained NLP algorithms to identify and characterize serious illness communication with oncology patients.


2020 ◽  
pp. 183335832092099
Author(s):  
Witold H Polanski ◽  
Adrian Danker ◽  
Amir Zolal ◽  
David Senf-Mothes ◽  
Gabriele Schackert ◽  
...  

Background: Electronic health records (EHRs) may be controversial but they have the potential to improve patient care. We investigated whether the introduction of an electronic template-based admission form for the collection of information about the patient’s medical history and neurological and clinical state at admission in the neurosurgical unit might have an impact on the quality of documentation in a discharge record and the amount of time taken to produce this documentation. Method: A new digital template-based admission form (EHR) was developed and assessed with QNOTE, an assessment tool of medical notes with standardised criteria and the possibility to benchmark the quality of documentations. This was compared to 30 prior paper-based handwritten documentations (HWD) regarding the utilisation of these medical notes for dictation of medical discharge records. Results: Implementation of the EHR significantly improved the quality of patient admission documentation with a QNOTE mean grand score of 87 ± 22 ( p < 0.0001) compared to prior HWD with 44 ± 30. The mean documentation time for HWD was 8.1 min ± 4.1 min and the dictation time for discharge records was 10.6 min ± 3.5 min. After implementation of EHR, the documentation time increased slightly to 9.6 min ± 2.3 min (n.s.), while the time for dictation of discharge records was reduced to 5.1 min ± 1.2 min ( p < 0.0001). There was a clear correlation between a higher quality of documentation and a higher needed documentation time as well as higher quality of documentation and lower dictation times of discharge records. Conclusion: Implementation of the EHR improved the quality of patient admission documentation and reduced the dictation time of discharge records. Implications: It is crucial to involve stakeholders and users of EHRs in a timely manner during the stage of development and implementation phase to ensure optimal results and better usability.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Anastasia A. Funkner ◽  
Michil P. Egorov ◽  
Sergey A. Fokin ◽  
Gennady M. Orlov ◽  
Sergey V. Kovalchuk

AbstractA system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques.


2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.


Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Arif Hussain ◽  
Gabriele Via ◽  
Lawrence Melniker ◽  
Alberto Goffi ◽  
Guido Tavazzi ◽  
...  

AbstractCOVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.


2020 ◽  
Vol 4 (s1) ◽  
pp. 27-27
Author(s):  
Rosa Roman-Oyola ◽  
Anita Bundy ◽  
Eida Castro ◽  
Osiris Castrillo

OBJECTIVES/GOALS: Mothers with cancer who have young children experience life disruptions when treatment procedures limit mother-child interactions. This study proposes the development of an intervention combining the Coaching approach with the Model of Playfulness to improve Quality of Life (QoL) and wellbeing of these patients and their young children. METHODS/STUDY POPULATION: This embedded mixed method study will be guided by the two initial phases of the ORBIT Model for the development of behavioral interventions for patients with chronic diseases. Participants will be mothers in the post-acute treatment stage of cancer (n = 6) and their children who are between 2 years and a half and 6 years, 11 months. Phase 1A, Definition, builds on qualitative data from a concurrent study exploring the experiences of mothers with cancer playing with their young children. As part of this phase, we will develop a play-based coaching intervention. In Phase 1B, Refinement, we will employ in-depth semi-structured interviews and standardized tools to evaluate acceptability of the intervention and preliminary outcomes. This will serve to further refine the intervention. RESULTS/ANTICIPATED RESULTS: Phase 1A will yield a plan for the intervention and data to enhance its initial implementation. Phase 1B will yield data, from the perspective of the mothers, about acceptability of the intervention procedures (e.g., delivery strategy, place for the intervention, time devoted, and outcome measures). This will enable modifications to the intervention. Additionally, Phase IB will yield preliminary data from specific QoL and wellbeing measures. For the mother, data about anxiety and depression symptoms, stress levels, and parental self-efficacy; for the child, emotional and behavioral indicators; for both: playfulness. DISCUSSION/SIGNIFICANCE OF IMPACT: This study entails the development of an intervention to enhance QoL and wellbeing of mothers with cancer and their children. Play moments as the centerpiece of the intervention, represent an innovative approach. Findings will guide the design of future feasibility studies to advance the development of this outcome driven intervention.


2021 ◽  
Vol 12 (2) ◽  
pp. 155-165
Author(s):  
Ahmed Hashem ◽  
Yogesh Shastri ◽  
Malfi Al Otaibi ◽  
Elwin Buchel ◽  
Hussam Saleh ◽  
...  

Non-alcoholic fatty disease (NAFLD) is amongst the leading causes of chronic liver disease worldwide. The prevalence of NAFLD in the Middle East is 32%, similar to that observed worldwide. The clinicians in this region face several challenges in diagnosing and treating patients with NAFLD. Additionally, there are no national or regional guidelines to address the concerns faced with current treatment options. Silymarin, derived from milk thistle, provides a rational and clinically proven approach to hepatoprotection. This article focuses on addressing regional diagnostic challenges and provides clear guidance and potential solutions for the use of Silymarin in the treatment of NAFLD in the Middle East. Both clinical and preclinical studies have highlighted the efficiency of Silymarin in managing NAFLD by reducing liver disease progression and improving patient symptoms and quality of life, alongside being safe and well tolerated. An expert panel of professionals from the Middle East convened to establish a set of regional-specific diagnostics. A consensus was established to aid general physicians to address the diagnostic challenges in the region. In conclusion, Silymarin can be considered beneficial in treating NAFLD and should be initiated as early as possible and continued as long as necessary.


2019 ◽  
Vol 10 (3) ◽  
pp. 163-167
Author(s):  
Jon Rosenberg ◽  
Allie Massaro ◽  
James Siegler ◽  
Stacey Sloate ◽  
Matthew Mendlik ◽  
...  

Background: Palliative care improves quality of life in patients with malignancy; however, it may be underutilized in patients with high-grade gliomas (HGGs). We examined the practices regarding palliative care consultation (PCC) in treating patients with HGGs in the neurological intensive care unit (NICU) of an academic medical center. Methods: We conducted a retrospective cohort study of patients admitted to the NICU from 2011 to 2016 with a previously confirmed histopathological diagnosis of HGG. The primary outcome was the incidence of an inpatient PCC. We also evaluated the impact of PCC on patient care by examining its association with prespecified secondary outcomes of code status amendment to do not resuscitate (DNR), discharge disposition, 30-day mortality, and 30-day readmission rate, length of stay, and place of death. Results: Ninety (36% female) patients with HGGs were identified. Palliative care consultation was obtained in 16 (18%) patients. Palliative care consultation was associated with a greater odds of code status amendment to DNR (odds ratio [OR]: 18.15, 95% confidence interval [CI]: 5.01-65.73), which remained significant after adjustment for confounders (OR: 27.20, 95% CI: 5.49-134.84), a greater odds of discharge to hospice (OR: 24.93, 95% CI: 6.48-95.88), and 30-day mortality (OR: 6.40, 95% CI: 1.96-20.94). Conclusion: In this retrospective study of patients with HGGs admitted to a university-based NICU, PCC was seen in a minority of the sample. Palliative care consultation was associated with code status change to DNR and hospice utilization. Further study is required to determine whether these findings are generalizable and whether interventions that increase PCC utilization are associated with improved quality of life and resource allocation for patients with HGGs.


Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 17
Author(s):  
Andrea Giussani

In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being. Modern technological advances such as OpenAI’s GPT-2 (and recently GPT-3) permit automated systems to dramatically alter reality with synthetic outputs so that humans are not able to distinguish the real copy from its counteracts. An example is given by an article entirely written by GPT-2, but many other examples exist. In the field of computer vision, Nvidia’s Generative Adversarial Network, commonly known as StyleGAN (Karras et al. 2018), has become the de facto reference point for the production of a huge amount of fake human face portraits; additionally, recent algorithms were developed to create both musical scores and mathematical formulas. This presentation aims to stimulate participants on the state-of-the-art results in this field: we will cover both GANs and language modeling with recent applications. The novelty here is that we apply a transformer-based machine learning technique, namely RoBerta (Liu et al. 2019), to the detection of human-produced versus machine-produced text concerning fake news detection. RoBerta is a recent algorithm that is based on the well-known Bidirectional Encoder Representations from Transformers algorithm, known as BERT (Devlin et al. 2018); this is a bi-directional transformer used for natural language processing developed by Google and pre-trained over a huge amount of unlabeled textual data to learn embeddings. We will then use these representations as an input of our classifier to detect real vs. machine-produced text. The application is demonstrated in the presentation.


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