The document describes the Patient Journey Record (PaJR), an online prediction system that uses machine learning to predict avoidable hospitalizations. It collects data from patient phone calls and uses decision trees to predict deterioration based on factors like self-rated health and medication adherence. The system achieved less than 1/3 false positives and under 1/6 false negatives in predicting urgent unplanned events. While the system helps prevent hospitalizations, challenges include limited training data and identifying predictive features from patient records.
1) The PaJR system uses telephone surveys and machine learning to predict avoidable hospitalizations for at-risk patients.
2) Most hospital admissions involve older patients with multiple chronic conditions, and are difficult to predict using traditional methods.
3) The PaJR approach uses non-clinical callers to regularly survey patients, asks questions predictive of deterioration, and intervenes early through alerts to prevent expensive hospitalizations.
4) PaJR has developed a machine learning model using decision trees that can predict unplanned health events and self-rated health from the survey responses with high accuracy.
Project HealthDesign: Rethinking the Power and Potential of Personal Health Records is a $10-million national program funded through the Robert Wood Johnson Foundation’s (RWJF) Pioneer Portfolio. In this second round of funding, Project HealthDesign will seek to test whether and how information about patterns of everyday living can be collected and interpreted such that patients can take action and clinicians can integrate new insights into clinical care processes.
Don Berwick offered 10 tips for improving the NHS in his speech:
1. Put patients at the center of care by customizing care to individuals and involving patients in their own care.
2. Stop restructuring the NHS to provide stability for improvements.
3. Strengthen local community health systems as the core unit for leadership, management, and care coordination.
4. Reinvest in general practice and primary care, which are the foundation of the healthcare system.
Health Informatics Society of Ireland - Patient Journey RecordEnda Madden
Health Informatics Society of Ireland - Patient Journey Record Presentation of the research behind the patient journey record readmissions management platform.
The Patient Journey Record System (PaJR) uses lay care guides to remotely monitor patients with chronic illnesses through daily phone calls. The calls analyze patients' health and social situations to predict health risks and the need for hospitalization. PaJR has completed Phase 1, which found lower hospitalization rates among monitored patients compared to controls. Phase 2 is ongoing in two sites and continues to recruit more patients and controls. Preliminary results again show lower admission rates for monitored patients. The system aims to detect health issues earlier through closer monitoring to reduce avoidable hospitalizations and shift care to the community.
The document profiles the backgrounds and areas of research expertise for several researchers:
- Dr. Carmel Martin is an Associate Professor of Family Medicine in Canada with research focused on primary health care reforms and chronic illness care. Her PhD explored experiences of chronic illness from patient and physician perspectives.
- Dr. Carl Vogel is a Fellow at Trinity College Dublin whose research uses cognitive science and artificial intelligence to study linguistic anomalies and language evolution. His work has applications in forensic, medical and other domains.
- Dr. Lucy Hederman is Director of the Centre for Health Informatics at Trinity College Dublin. Her research involves clinical decision support systems in primary care.
- Kevin A. Smith has over 30 years experience
The Propel Programme provides funding and business support to ambitious entrepreneurs located in border counties of Ireland. It offers up to €30,000 in financial support, specialized mentoring and training, hot desk facilities, and access to investors and networks. The year-long programme aims to help entrepreneurs establish successful global businesses and is open to applicants with export-focused business ideas. The deadline to apply is April 24, 2009.
1) The PaJR system uses telephone surveys and machine learning to predict avoidable hospitalizations for at-risk patients.
2) Most hospital admissions involve older patients with multiple chronic conditions, and are difficult to predict using traditional methods.
3) The PaJR approach uses non-clinical callers to regularly survey patients, asks questions predictive of deterioration, and intervenes early through alerts to prevent expensive hospitalizations.
4) PaJR has developed a machine learning model using decision trees that can predict unplanned health events and self-rated health from the survey responses with high accuracy.
Project HealthDesign: Rethinking the Power and Potential of Personal Health Records is a $10-million national program funded through the Robert Wood Johnson Foundation’s (RWJF) Pioneer Portfolio. In this second round of funding, Project HealthDesign will seek to test whether and how information about patterns of everyday living can be collected and interpreted such that patients can take action and clinicians can integrate new insights into clinical care processes.
Don Berwick offered 10 tips for improving the NHS in his speech:
1. Put patients at the center of care by customizing care to individuals and involving patients in their own care.
2. Stop restructuring the NHS to provide stability for improvements.
3. Strengthen local community health systems as the core unit for leadership, management, and care coordination.
4. Reinvest in general practice and primary care, which are the foundation of the healthcare system.
Health Informatics Society of Ireland - Patient Journey RecordEnda Madden
Health Informatics Society of Ireland - Patient Journey Record Presentation of the research behind the patient journey record readmissions management platform.
The Patient Journey Record System (PaJR) uses lay care guides to remotely monitor patients with chronic illnesses through daily phone calls. The calls analyze patients' health and social situations to predict health risks and the need for hospitalization. PaJR has completed Phase 1, which found lower hospitalization rates among monitored patients compared to controls. Phase 2 is ongoing in two sites and continues to recruit more patients and controls. Preliminary results again show lower admission rates for monitored patients. The system aims to detect health issues earlier through closer monitoring to reduce avoidable hospitalizations and shift care to the community.
The document profiles the backgrounds and areas of research expertise for several researchers:
- Dr. Carmel Martin is an Associate Professor of Family Medicine in Canada with research focused on primary health care reforms and chronic illness care. Her PhD explored experiences of chronic illness from patient and physician perspectives.
- Dr. Carl Vogel is a Fellow at Trinity College Dublin whose research uses cognitive science and artificial intelligence to study linguistic anomalies and language evolution. His work has applications in forensic, medical and other domains.
- Dr. Lucy Hederman is Director of the Centre for Health Informatics at Trinity College Dublin. Her research involves clinical decision support systems in primary care.
- Kevin A. Smith has over 30 years experience
The Propel Programme provides funding and business support to ambitious entrepreneurs located in border counties of Ireland. It offers up to €30,000 in financial support, specialized mentoring and training, hot desk facilities, and access to investors and networks. The year-long programme aims to help entrepreneurs establish successful global businesses and is open to applicants with export-focused business ideas. The deadline to apply is April 24, 2009.
High-Confidence Data Programming for Evaluating Suppression of Physiological ...Ivan Ruchkin
Presented by Sydney Pugh at the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2021.
Abstract: False alarms generated by physiological monitors can overwhelm clinical caretakers with a variety of alarms. The resulting alarm fatigue can be mitigated with alarm suppression. Before being deployed, such suppression mechanisms need to be evaluated through a costly observational study, which would determine and label the truly suppressible alarms. This paper proposes a lightweight method for evaluating alarm suppression without access to the true alarm labels. The method is based on the data programming paradigm, which combines noisy and cheap-to-obtain labeling heuristics into probabilistic labels. Based on these labels, the method estimates the sensitivity/specificity of a suppression mechanism and describes the likely outcomes of an observational study in the form of confidence bounds. We evaluate the proposed method in a case study of low SpO2 alarms using a dataset collected at Children's Hospital of Philadelphia and show that our method provides tight and accurate bounds that significantly outperform the naive comparative method.
An electronic early warning score system was proposed to address the shortcomings of a manual paper-based system. The electronic system would calculate scores based on recorded vital sign observations and trigger alerts and escalation pathways for deteriorating patients. The system could be deployed on mobile devices at bedsides or fixed iPads to facilitate real-time data entry and alerts. It leverages an existing electronic medical record platform already in use in New Zealand to provide an integrated, feasible solution.
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
This document discusses the virtualization of healthcare delivery through increased digitization and connectivity of data. It describes how advanced data processing and information fusion can turn insights into actions by integrating information from multiple sources. The future of healthcare is empowering individuals through connected technologies to live independently and with better health. Key challenges around improving outcomes and reducing costs through tools like clinical decision support, population health management, and remote monitoring are also addressed.
1) The iRegion Karlsruhe project aims to make infrastructures smarter by adding a digital awareness layer that integrates information to enable new technologies and organizational changes.
2) One example is the Stroke Angel project, which used a handheld device and data sharing to reduce clinical response times for stroke patients by 50% and double the treatment rate.
3) Looking ahead, the vision is for an iRegion Karlsruhe living lab that tests smart infrastructure solutions for healthcare, logistics, energy and more, and empowers individuals to actively manage their own information for highly personalized services and collective intelligence applications.
This document summarizes a presentation given by Dr. Jacob Perry at the University of Kentucky College of Medicine discussing why surgical residents may lack adequate operative experience. Dr. Perry reviews research showing that surgical skill is learned, not innate, and that current residents receive significantly less operative experience in essential cases than what is considered the minimum for competence. Factors that limit residents' operative experience include work hour restrictions, pressure for efficiency, and inadequate focus on teaching in the operating room. Improving resident education will require collecting better operative data, evaluating teaching skills, and prioritizing maximum resident benefit in the operating room.
The document discusses eliminating harm in healthcare by focusing on high reliability and continuous improvement. It provides examples of how high performing organizations, like nuclear submarine programs, achieve near-zero errors by engaging all staff in problem solving. The author argues that eliminating common harms like hospital-acquired infections can reduce costs while fulfilling the ethical duty to "first, do no harm." Specific initiatives at the University of Pennsylvania aimed to standardize practices and engage clinicians in redesigning processes to eliminate central line-associated bloodstream infections.
Presentation at "Impact Evaluation for Financial Inclusion" (January 2013)
CGAP and the UK Department for International Development (DFID) convened over 70 funders, practitioners, and researchers for a workshop on impact evaluation for financial inclusion in January 2013. Co-hosted by DFID in London, the workshop was an opportunity for participants to engage with leading researchers on the latest research methods of impact evaluation and to discuss other areas on the impact evaluation agenda.
scientific methods of genetical dieseases using pupillometryRahulChinnuYelakapal
The document proposes a clinical decision support system using chromatic pupillometry and machine learning to diagnose inherited retinal diseases in pediatric patients. Pupillometry data is collected from patients using a dedicated medical device and analyzed using two support vector machine classifiers, one for each eye, to classify features and diagnose conditions like retinitis pigmentosa. The system achieved 0.846 accuracy, 0.937 sensitivity and 0.786 specificity in diagnosing retinitis pigmentosa in pediatric subjects. This novel approach provides a non-invasive alternative to traditional invasive clinical tests for diagnosing genetic eye diseases in children.
CombiMatrix Corporation provides diagnostic testing services including prenatal testing using microarrays to screen for chromosomal abnormalities, with a focus on growing their prenatal microarray business which has seen record testing volumes and revenue. They discuss their competitive advantages in testing turnaround time and expertise, and outline their commercial strategy of direct sales and partnerships to drive testing volumes and secure reimbursement.
Impact Of a Clinical Decision Support Tool on Asthma Patients with Current As...Yiscah Bracha
The document summarizes research on the effect of computerized decision support (CDS) on the percentage of asthma patients with asthma action plans. The research found:
1) Implementation of a CDS tool at clinics led to increases in the percentage of pediatric patients with current asthma action plans, especially at clinics that previously lacked paper templates.
2) For adults, clinics that emphasized asthma action plans and had physicians start using the CDS tool saw increases, while clinics without paper templates saw physicians begin using the tool.
3) Statistical analysis showed the CDS tool had an initial positive effect at one pediatric clinic that oscillated over time, while having no significant effect at other clinics, possibly due to pre-existing tendencies of physicians to
This document discusses evidence farming as a new approach to evaluating mobile health (mHealth) applications. It outlines limitations of current evaluation methods like randomized controlled trials and data mining of electronic health records. It proposes an "evidence macrosystem" using open architectures to support evidence extraction through approaches like rooting for evidence, industrial evidence farming, personal evidence gardening, and crowdsourcing what matters to patients. The goal is a learning community that enables broad, rapid dissemination of evaluation methods and findings through shared modules and libraries within mHealth applications.
High-Confidence Data Programming for Evaluating Suppression of Physiological ...Ivan Ruchkin
Presented by Sydney Pugh at the IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2021.
Abstract: False alarms generated by physiological monitors can overwhelm clinical caretakers with a variety of alarms. The resulting alarm fatigue can be mitigated with alarm suppression. Before being deployed, such suppression mechanisms need to be evaluated through a costly observational study, which would determine and label the truly suppressible alarms. This paper proposes a lightweight method for evaluating alarm suppression without access to the true alarm labels. The method is based on the data programming paradigm, which combines noisy and cheap-to-obtain labeling heuristics into probabilistic labels. Based on these labels, the method estimates the sensitivity/specificity of a suppression mechanism and describes the likely outcomes of an observational study in the form of confidence bounds. We evaluate the proposed method in a case study of low SpO2 alarms using a dataset collected at Children's Hospital of Philadelphia and show that our method provides tight and accurate bounds that significantly outperform the naive comparative method.
An electronic early warning score system was proposed to address the shortcomings of a manual paper-based system. The electronic system would calculate scores based on recorded vital sign observations and trigger alerts and escalation pathways for deteriorating patients. The system could be deployed on mobile devices at bedsides or fixed iPads to facilitate real-time data entry and alerts. It leverages an existing electronic medical record platform already in use in New Zealand to provide an integrated, feasible solution.
June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
This document discusses the virtualization of healthcare delivery through increased digitization and connectivity of data. It describes how advanced data processing and information fusion can turn insights into actions by integrating information from multiple sources. The future of healthcare is empowering individuals through connected technologies to live independently and with better health. Key challenges around improving outcomes and reducing costs through tools like clinical decision support, population health management, and remote monitoring are also addressed.
1) The iRegion Karlsruhe project aims to make infrastructures smarter by adding a digital awareness layer that integrates information to enable new technologies and organizational changes.
2) One example is the Stroke Angel project, which used a handheld device and data sharing to reduce clinical response times for stroke patients by 50% and double the treatment rate.
3) Looking ahead, the vision is for an iRegion Karlsruhe living lab that tests smart infrastructure solutions for healthcare, logistics, energy and more, and empowers individuals to actively manage their own information for highly personalized services and collective intelligence applications.
This document summarizes a presentation given by Dr. Jacob Perry at the University of Kentucky College of Medicine discussing why surgical residents may lack adequate operative experience. Dr. Perry reviews research showing that surgical skill is learned, not innate, and that current residents receive significantly less operative experience in essential cases than what is considered the minimum for competence. Factors that limit residents' operative experience include work hour restrictions, pressure for efficiency, and inadequate focus on teaching in the operating room. Improving resident education will require collecting better operative data, evaluating teaching skills, and prioritizing maximum resident benefit in the operating room.
The document discusses eliminating harm in healthcare by focusing on high reliability and continuous improvement. It provides examples of how high performing organizations, like nuclear submarine programs, achieve near-zero errors by engaging all staff in problem solving. The author argues that eliminating common harms like hospital-acquired infections can reduce costs while fulfilling the ethical duty to "first, do no harm." Specific initiatives at the University of Pennsylvania aimed to standardize practices and engage clinicians in redesigning processes to eliminate central line-associated bloodstream infections.
Presentation at "Impact Evaluation for Financial Inclusion" (January 2013)
CGAP and the UK Department for International Development (DFID) convened over 70 funders, practitioners, and researchers for a workshop on impact evaluation for financial inclusion in January 2013. Co-hosted by DFID in London, the workshop was an opportunity for participants to engage with leading researchers on the latest research methods of impact evaluation and to discuss other areas on the impact evaluation agenda.
scientific methods of genetical dieseases using pupillometryRahulChinnuYelakapal
The document proposes a clinical decision support system using chromatic pupillometry and machine learning to diagnose inherited retinal diseases in pediatric patients. Pupillometry data is collected from patients using a dedicated medical device and analyzed using two support vector machine classifiers, one for each eye, to classify features and diagnose conditions like retinitis pigmentosa. The system achieved 0.846 accuracy, 0.937 sensitivity and 0.786 specificity in diagnosing retinitis pigmentosa in pediatric subjects. This novel approach provides a non-invasive alternative to traditional invasive clinical tests for diagnosing genetic eye diseases in children.
CombiMatrix Corporation provides diagnostic testing services including prenatal testing using microarrays to screen for chromosomal abnormalities, with a focus on growing their prenatal microarray business which has seen record testing volumes and revenue. They discuss their competitive advantages in testing turnaround time and expertise, and outline their commercial strategy of direct sales and partnerships to drive testing volumes and secure reimbursement.
Impact Of a Clinical Decision Support Tool on Asthma Patients with Current As...Yiscah Bracha
The document summarizes research on the effect of computerized decision support (CDS) on the percentage of asthma patients with asthma action plans. The research found:
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3) Statistical analysis showed the CDS tool had an initial positive effect at one pediatric clinic that oscillated over time, while having no significant effect at other clinics, possibly due to pre-existing tendencies of physicians to
This document discusses evidence farming as a new approach to evaluating mobile health (mHealth) applications. It outlines limitations of current evaluation methods like randomized controlled trials and data mining of electronic health records. It proposes an "evidence macrosystem" using open architectures to support evidence extraction through approaches like rooting for evidence, industrial evidence farming, personal evidence gardening, and crowdsourcing what matters to patients. The goal is a learning community that enables broad, rapid dissemination of evaluation methods and findings through shared modules and libraries within mHealth applications.
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PaJR Presentation at Health Informatics Society of Ireland - Oct 2011
1. Patient Journey Record (PaJR)
Online Prediction System
Jing Su, Lucy Hederman, Atieh Zarabzadeh, Dee Grady, Carmel Martin,
Kevin Smith, Carl Vogel, Enda Madden, Brendan Madden
Trinity College Dublin, UCC, PHC Research, GroupNos
HISI 2011 1
2. Avoidable hospitalisations
• PaJR is a telephone service that targets avoidable
hospitalisations
• Most hospital admissions
• are in older, sicker people with multiple diseases
and conditions
• are unpredictable in the short term with current
systems
HISI 2011 2
3. $/day
$5,000
$1,000
$100
Early Alertsof
deterioration helps
prevent the patient
$10 entering expensive
treatment
$1
Self care Complicated Complex Hospital
HISI 2011 3
6. The PaJR approach
• Use lay callers
• Ask the questions that predict
hospitalisation
• Not disease specific
• Intervene early
• Alert accurately …
• ~ 50% reduction in hospital
admissions in pilot sites
HISI 2011 6
7. PaJR Prediction Service
Michael Anon
• Predicts patient
deterioration based on
record of call.
– Self-rated health, taking
meds,…
– Brief text entries
• Uses a predictive
model learned from
examples of calls
leading to unplanned
events. HISI 2011 7
9. Simple predictive model:
a decision tree
A - decision node
A
yes no - leaf node
K=y (K means
B B
UnplannedEvent)
<n ≥n <p ≥p
C A sample decision tree
K=y K=n K=y on UnplannedEvent
yes no
Eg: A = “her sister”;
B = AvgWordLength; K=n K=y
C = takingMeds
HISI 2011 9
10. Machine learning
• ML Tools such as Weka or Timbl provide
algorithms to produce prediction models
from examples (“training data”).
• The examples must be presented to ML
tool as a collection of features.
• Expertise and skill is needed to identify /
derive / represent features of examples
that might predict the outcome.
HISI 2011 10
11. PaJR‟s Current ML
• Predicts unplanned events, urgent
unplanned events, self rated health.
• Uses decision trees.
• Weights false negatives 500 more costly
than false positives
– A missed deterioration is bad.
– An inappropraite alert to a carer to call a
patient is OK.
HISI 2011 11
12. Accuracy
• Predicting urgent
unplanned events True False
(UUE)
Negative 1091 4
• Training data (OK)
– 1621 phone calls
Positive 23 453
– 27 urgent unplanned
(UUE)
events
• False Negatives cost
500 times FPs • Fewer than 1/3 of the calls are
incorrectly prioritised
• Under 1/6 of the calls that should
be prioritised are not.
HISI 2011 12
13. Error Analysis
• False positive rate is worth further investigation:
– ML predicts an urgent unplanned event.
– No urgent unplanned event occurred.
– But is that because the PaJR caller intervened (with advice,
referral, comfort, …) and averted the event ML predicted?
• Analysing FP cases, we found evidence of some
intervention in a small number of cases.
– Further work needed.
• More significantly, UUEs were rarely (6/27) „anticipated‟
by lay callers (they didn‟t intervene), whereas ML
predicted 23 of them.
HISI 2011 13
14. Challenges
• Data
– ML requires lots of examples of each outcome.
Thanks to PaJR the number of unplanned events
among the users is declining.
• Features
– We have lots of data for each case but it takes time
and skill to identify features predictive of deterioration.
• Prediction Engine Pipeline
– The management of multiple cases, multiple models,
etc.
HISI 2011 14
15. Benefits of Machine Learning
• Compared with static rule-based alerts
– ML allows identification of features that emerge as predictive of
deterioration.
– ML uses evidence from data on real patients.
– ML can be easily transferred to new settings and new services
– ML adapts over time
• Compared with experienced callers without ML
– ML allows high accuracy, high volume at low cost.
– ML will identify features across callers, across time, etc.
– ML has perfect memory – callers go on leave, move on.
HISI 2011 15
Currently estimates for the bill for avoidable re-admissions in the US is $15 billion with a corresponding figure of £1.5 billion published by the UK Department of Health in 2010. From 2013 in the US, hospitals will be financially penalised for every re-admission of a Medicare patient within 30 days of their discharge. The number of patients over 65 years of age admitted to hospital in the US is 13 million per year. Currently, 20% of these patients are re-admitted within 30 days. Initial trials of the PaJR RAP Service have shown a 50% decrease in re-admissions. This would make the RAP Service very cost effective for US hospitals to deploy. Reducing avoidable hospitalisationsOne night in an Irish hospital costs €1000As a locum GP I’ve visited elderly patients with multiple chronic illnesses – I’ve had to admit patients as the degree of their deterioration is unclearIn 2010 the worldwide market (US, EMEA and APAC) for home health monitoring of “welfare diseases” such as diabetes, cardiac arrhythmia, sleep apnea, asthma and chronic obstructive pulmonary disease (COPD) was worth about €7.6 billion ($10 billion). The market for remote monitoring services is set to grow rapidly through 2014 based on current reports. Estimates for the cost savings achievable through remote monitoring of patients in the US by 2014 range from $2 billion and $6 billion.
The PaJR Service identifies patients at immediate risk of hospitalisation and alerts their GPsTimely interventions avert deteriorations.PaJR reduces admission rates by 50%.
Emergent feature – diarrhoea, falls; “cold”; Some of these may feed back to service and discharge planning.