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Strehlow M, Alvarez A, Blomkalns AL, Caretta-Wyer H, Gharahbaghian L, Imler D, Khan A, Lee M, Lobo V, Newberry JA, Riberia R, Sebok-Syer S, Shen S, Gisondi MA. Precision emergency medicine. Acad Emerg Med 2024. [PMID: 38940478 DOI: 10.1111/acem.14962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/13/2024] [Accepted: 05/23/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health. METHODS In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward. RESULTS Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty. CONCLUSIONS Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.
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Affiliation(s)
- Matthew Strehlow
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Al'ai Alvarez
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Andra L Blomkalns
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Holly Caretta-Wyer
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Laleh Gharahbaghian
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Daniel Imler
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Ayesha Khan
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Moon Lee
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Viveta Lobo
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jennifer A Newberry
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Ryan Riberia
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Stefanie Sebok-Syer
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Sam Shen
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Michael A Gisondi
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, USA
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2
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Samal L, Fu HN, Camara DS, Wang J, Bierman AS, Dorr DA. Health information technology to improve care for people with multiple chronic conditions. Health Serv Res 2021; 56 Suppl 1:1006-1036. [PMID: 34363220 PMCID: PMC8515226 DOI: 10.1111/1475-6773.13860] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. DATA SOURCES We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. STUDY DESIGN We identified studies of health IT interventions for PLWMCC across three domains as follows: self-management support, care coordination, and algorithms to support clinical decision making. DATA COLLECTION/EXTRACTION METHODS Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. PRINCIPAL FINDINGS The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi-experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient-centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. CONCLUSIONS Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.
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Affiliation(s)
- Lipika Samal
- Brigham and Women's HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Helen N. Fu
- Indiana University Richard M. Fairbanks School of Public HealthIndianapolisINUSA
- Regenstrief InstituteCenter for Biomedical InformaticsIndianapolisINUSA
| | - Djibril S. Camara
- Center for Disease Control and Prevention, Center for Surveillance, Epidemiology, and Laboratory Services (CSELS) Division of Scientific Education and Professional Development, Public Health Informatics Fellowship ProgramAtlantaGeorgiaUSA
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
| | - Jing Wang
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
- Florida State University College of NursingTallahasseeFloridaUSA
- Health and Aging Policy Fellows Program at Columbia UniversityNew YorkNYUSA
| | - Arlene S. Bierman
- Center for Evidence and Practice Improvement, Agency for Healthcare Research and QualityRockvilleMDUSA
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Golas SB, Nikolova-Simons M, Palacholla R, Op den Buijs J, Garberg G, Orenstein A, Kvedar J. Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial. NPJ Digit Med 2021; 4:97. [PMID: 34112921 PMCID: PMC8192898 DOI: 10.1038/s41746-021-00463-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 03/19/2021] [Indexed: 12/30/2022] Open
Abstract
This study explored the potential to improve clinical outcomes in patients at risk of moving to the top segment of the cost acuity pyramid. This randomized controlled trial evaluated the impact of a Stepped-Care approach (predictive analytics + tailored nurse-driven interventions) on healthcare utilization among 370 older adult patients enrolled in a homecare management program and using a Personal Emergency Response System. The Control group (CG) received care as usual, while the Intervention group (IG) received Stepped-Care during a 180-day intervention period. The primary outcome, decrease in emergency encounters, was not statistically significant (15%, p = 0.291). However, compared to the CG, the IG had significant reductions in total 90-day readmissions (68%, p = 0.007), patients with 90-day readmissions (76%, p = 0.011), total 180-day readmissions (53%, p = 0.020), and EMS encounters (49%, p = 0.006). Predictive analytics combined with tailored interventions could potentially improve clinical outcomes in older adults, supporting population health management in home or community settings.
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Affiliation(s)
- Sara Bersche Golas
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA.
| | | | - Ramya Palacholla
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Tufts University School of Medicine, Department of Public Health and Community Medicine, Boston, MA, USA
| | | | | | | | - Joseph Kvedar
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Nikolova-Simons M, Golas SB, den Buijs JO, Palacholla RS, Garberg G, Orenstein A, Kvedar J. A randomized trial examining the effect of predictive analytics and tailored interventions on the cost of care. NPJ Digit Med 2021; 4:92. [PMID: 34083743 PMCID: PMC8175712 DOI: 10.1038/s41746-021-00449-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.
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Affiliation(s)
| | - Sara Bersche Golas
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | | | - Ramya S Palacholla
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Tufts University School of Medicine, Boston, MA, USA
| | | | | | - Joseph Kvedar
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Op den Buijs J, Pijl M, Landgraf A. Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States. JMIR Med Inform 2021; 9:e25121. [PMID: 33682679 PMCID: PMC7985802 DOI: 10.2196/25121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/08/2021] [Accepted: 02/07/2021] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Predictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting. OBJECTIVE The objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider. METHODS Retrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States. RESULTS German PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model. CONCLUSIONS Despite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use.
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Secure training of decision trees with continuous attributes. PROCEEDINGS ON PRIVACY ENHANCING TECHNOLOGIES 2020. [DOI: 10.2478/popets-2021-0010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2
N) from the naive O(N
2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.
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7
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Pijl M, Op den Buijs J, Landgraf A. Evaluating the Impact of a Risk Assessment System With Tailored Interventions in Germany: Protocol for a Prospective Study With Matched Controls. JMIR Res Protoc 2020; 9:e17584. [PMID: 33001038 PMCID: PMC7563626 DOI: 10.2196/17584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 05/01/2020] [Accepted: 05/19/2020] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND With a worldwide increase in the elderly population, and an associated increase in health care utilization and costs, preventing avoidable emergency department visits and hospitalizations is becoming a global priority. A personal emergency response system (PERS), consisting of an alarm button and a means to establish a live connection to a response center, can help the elderly live at home longer independently. Individual risk assessment through predictive modeling can help indicate what PERS subscribers are at elevated risk of hospital transport so that early intervention becomes possible. OBJECTIVE The aim is to evaluate whether the combination of risk scores determined through predictive modeling and targeted interventions offered by a case manager can result in a reduction of hospital admissions and health care costs for a population of German PERS subscribers. The primary outcome of the study is the difference between the number of hospitalizations in the intervention and matched control groups. METHODS As part of the Sicher Zuhause program, an intervention group of 500 PERS subscribers will be tracked for 8 months. During this period, risk scores will be determined daily by a predictive model of hospital transport, and at-risk participants may receive phone calls from a case manager who assesses the health status of the participant and recommends interventions. The health care utilization of the intervention group will be compared to a group of matched controls, retrospectively drawn from a population of PERS subscribers who receive no interventions. RESULTS Differences in health care utilization and costs between the intervention group and the matched controls will be determined based on reimbursement records. In addition, qualitative data will be collected on the participants' satisfaction with the Sicher Zuhause program and utilization of the interventions offered as part of the program. CONCLUSIONS The study evaluation will offer insight into whether a combination of predictive analytics and case manager-driven interventions can help in avoiding hospital admissions and health care costs for PERS subscribers in Germany living at home independently. In the future, this may lead to improved quality of life and reduced medical costs for the population of the study. TRIAL REGISTRATION Deutsches Register Klinischer Studien (DRKS), DRKS00017328; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017328. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/17584.
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Affiliation(s)
- Marten Pijl
- Collaborative Care Solutions Department, Philips Research, Eindhoven, Netherlands
| | - Jorn Op den Buijs
- Collaborative Care Solutions Department, Philips Research, Eindhoven, Netherlands
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8
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Silva de Lima AL, Smits T, Darweesh SKL, Valenti G, Milosevic M, Pijl M, Baldus H, de Vries NM, Meinders MJ, Bloem BR. Home-based monitoring of falls using wearable sensors in Parkinson's disease. Mov Disord 2019; 35:109-115. [PMID: 31449705 PMCID: PMC7003816 DOI: 10.1002/mds.27830] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/02/2019] [Accepted: 07/15/2019] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. METHODS We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling. RESULTS The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0). CONCLUSION Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ana Lígia Silva de Lima
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Tine Smits
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Sirwan K L Darweesh
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Giulio Valenti
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Mladen Milosevic
- Philips Research North America, Acute Care Solutions Department, Cambridge, Massachusetts, USA
| | - Marten Pijl
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Heribert Baldus
- Philips Research, Department Personal Health, Eindhoven, the Netherlands
| | - Nienke M de Vries
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Marjan J Meinders
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.,Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, the Netherlands
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
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