The Patient Journey Record System (PaJR) Interim Report

PAJR

The Patient Journey
Record System (PaJR)
Interim Report of Phase 1 and Phase 2 of the
Patient Journey Record System
C Martin, D Grady, K Smith, E Madden, L Hederman, C Vogel, B Madden, A Zarabzadeh and J Su
11/29/2011

The Patient Journey Record System (PaJR) is an innovative care pathway with an expanded Case Management
model utilizing lay Care Guides working with a case manager to support people with chronic illness in at risk
trajectories to avoid unplanned hospitalisations. Care Guides substitute for many tasks of case management,
expanding the reach, effectiveness and efficiency of case managers with innovative predictive modelling of
admission risk based on frequent short phone conversations. Lay care guides, remotely monitors patients with
chronic illness or frailty through daily or, as needed, health-related phone conversations about their health
and well-being in biopsychosocial and environmental contexts and health and social care. The system
transmits the conversations for analysis, using software that organises the data and predicts next day health
and unplanned service utilisation. The system allows the care guides and clinical supervisors to quickly
pinpoint health issues and respond accordingly, either by contacting the patient (to offer care instructions
and/or self-care education) or his or her GP or directing the patient to services). PaJR has an action research
based adaptive learning, development and evaluation. Phase 1 compared hospital admissions and services in
two cohorts from KDOC. Phase 2 is a regional demonstration clinical trial in 2 sites – Nenagh, Tipperary and
Castlebar, Mayo. PaJR is now monitoring 132 patients and 42 controls with 3 FTE care guides. To date
unplanned admission rates are approximately 3 times higher in the control group. We are midway through
Phase 2 and ongoing patient recruitment and on-going data collection is in progress. We have estimated
potential savings from reduced admissions that could be shifted to more appropriate services in the
community and still potentially make savings for the HSE and shift care from unplanned hospital care to
planned hospital and more timely community care. Phase 3 is being planned with randomised controlled trials
of hospital readmission prevention and community based preventable admission avoidance.

© PBOC Limited 2011 – Not for Distribution

Contents
Patient Journey Record (PaJR): Monitoring Chronically Ill Patients via Phone Calls to reduce
potentially avoidable hospitalisations. ................................................................................................ 3
Introduction ............................................................................................................................................ 3
Literature ................................................................................................................................................ 4
The conceptual framework for the PaJR project .................................................................................... 7
Complex adaptive systems theory and resource use ......................................................................... 7
Description of the PaJR System .............................................................................................................. 8
Survey Question Types........................................................................................................................ 9
Intervention Types – triggered by PaJR Alerts .................................................................................. 10
Patient Journey Record (PaJR) Online Prediction System................................................................. 11
Progress-to-Date ............................................................................................................................... 12
Recruitment of patients ................................................................................................................ 12
Results ................................................................................................................................................... 13
Phase 1 – KDOC Out-of-Hours Service, Naas, Kildare. ...................................................................... 13
Phase 2 – Nenagh Hospital, Tipperary and County Mayo. ............................................................... 14
Overview of PaJR calls....................................................................................................................... 14
Estimated potential Savings from Hospitalisations in County Mayo ................................................ 16
Other Findings from patient cohorts ................................................................................................ 17
Conclusion ............................................................................................................................................. 18
Next Steps – Phase 3 ......................................................................................................................... 19
Appendix 1 ............................................................................................................................................ 20
Letters of Satisfaction and Support ...................................................................................................... 20
KDOC Patient Satisfaction Survey Feedback Report ............................................................................. 23
References ............................................................................................................................................ 26


Patient Journey Record (PaJR): Monitoring Chronically Ill Patients via
Phone Calls to reduce potentially avoidable hospitalisations.

Introduction
PaJR comprises an innovative care pathway with an expanded Case Management model utilizing lay
Care Guides working with a case manager to support people with chronic illness in at risk trajectories
to avoid unplanned hospitalisations. Care Guides substitute for many tasks of case management,
expanding the reach of case managers and enhancing their efficiencies with innovative predictive
modelling based on frequent short phone conversations.

PaJR is a person-centred patient journey monitoring system to improve quality of life and reduce
avoidable hospitalisations. It incorporates observations of daily living and machine learning of
sentiment analysis within a primary care and care management environment. The PaJR system,
through lay care guides, remotely monitors patients with chronic illness or frailty and with high risk
of readmission. It aims to detect health risks or deterioration earlier than currently happens by
closer monitoring, ongoing predictive modelling and faster information transfer to the GP, and
relevant health or social care providers.

The PaJR study commenced in August 2009 with the development of a conceptual framework and
operational framework for a patient journey through chronic illness supported by information
technology.(1)PaJR then received 1 year funding as a translational research project funded by the
National Digital Research Centre.It is anticipated that a mature system for large numbers of patients
will be developed in year 2-3 by the Trinity College Dublin Campus Company PBOC Limited (Carmel
Martin, Carl Vogel; Kevin Smith; Lucy Hederman, Enda and Brendan Madden and Trinity College
Dublin) that formed as an outcome of the National Digital Research Centre funding.

This is a report of the first 12months activity.

Patient Population
Vulnerable Populations: Older Patients with Chronic Illness and Multi-morbidity

Problem Addressed
If not monitored closely, chronically ill individuals may decompensate in any one of multiple
domains in their personal health environment. Decompensation may lead to the need for expensive
inpatient care. Although ongoing monitoring of these individuals, especially older ones, may prevent
some of these complications, relatively few health systems have the capacity to provide such
services to date and involve expensive tale-health equipment in the home with the costs of
maintaining the equipment.

Cycle of hospitalisations

Many patients with chronic illnesses require frequent hospitalisations to deal with exacerbations or
complications associated with their condition(s). Unrealised benefits of monitoring of chronically ill


individuals, especially older ones, can help to prevent many exacerbations and complications, thus
reducing care costs and allowing them to remain at home.

The PaJR Mission
• Reduce Avoidable Hospitalisations Monitoring the Patient Journey
• Person-centred not disease
centred
• Social support model – appraisal,
informational &practical
• Earlier apt and responsive person
centred care interventions
• To reach people in all walks of life,
health literacy and home
circumstances – with phone
access.
• Primary Care Community
orientated.
• Supports integrated care

Figure 1 PaJR Concept

“Chronic diseases such as cardiovascular diseases, cancers, respiratory diseases and diabetes are a
heterogeneous group that share underlying lifestyle and societal causes which need to be addressed
by political, fiscal and legal mechanisms as well as at the level of the individual”. UN General
Assembly, September 2010

Literature
Avoidable hospitalisations in older people with chronic conditions are the subject of considerable
interest to decision makers(2), because such admissions are deemed to be expensive, unhelpful to
patients and reflect underperformance of health systems organisation.(3-6) There exist considerable
variations in terminology used to describe models and components of models designed to reduce
avoidable hospitalisations, their context of care and settings, even definitions of the terms “acute”,
“hospital” and “admissions” vary.(7, 8) A recent definition(9) of avoidable readmissions summarises
their multi-factorial nature - ‘a preventable readmission as an unintended and undesired subsequent
post-discharge hospitalization, where the probability is subject to the influence of multiple factors’.
Increasingly the literature sees underlying preconditions as a cause for readmissions(9-11). In
general, most medical readmissions for sub-acute or chronic conditions are potentially
preventable.(12)(13)

Who is at risk?

Which patients are at risk and what interventions are successful in different settings and
communities to reduce avoidable admissions?(14-22) Factors predicting avoidable hospitalisations
are the subject of recent narrative reviews, systematic reviews and meta-analyses in Ireland(4, 23,
24), USA(14, 17), Canada(22, 25), Australia(26-29), UK(30-32), Spain(33),(34) and elsewhere.(10)
Hospital studies look at 30 day readmissions, while community studies(15) look at population based


risks of hospitalisations. Overlap among hospital and community programs(7) is demonstrated in
prolonged post-discharge programs by the work of Courtney,(35) and others (34, 36). Community
based risk prediction scores include self assessed health, support, psychosocial and environmental
issues and disease factors(37) while hospital predictions focus on diagnostic groups(9), disease
severity, length of stay and physiological variables.(37, 38) Some groups have pioneered use of both
hospital and GP databases(15, 39) . A well developed and internationally validated score in the
Probability of Repeat Admission (Pra) Risk Score.(40)

Tools to help identify people at high risk of future emergency admission include computer database
models and simple questionnaires.(15, 41)(42) However, ongoing research is needed to understand
what are causal relationships among the descriptors, multiple associations and correlations found in
the studies conducted.(9) Problems with current risk assessment tools – are that they are cross-
sectional with static predictions while people’s at risk status is complex and non-linear with
regression to the mean over time.(43, 44) While clinical knowledge can predict current risks,
threshold models have more predictability.(45)

Threshold modelling is rule based and identifies those at high risk who meet a set of criteria. Case
finding has used threshold modelling from hospital data, such as repeated emergency admissions, as
a marker of a high risk of future admissions. However, admission rates and bed use among high-risk
patients fall to the mean rate for older people(44) or have unpredictable rises with many admissions
from lower risk groups(40). Alternatives, such as identifying patients at high risk through a
questionnaire administered by a GP practice,(46) do not take account of trajectories in individual
journeys unless repeated regularly. (43)

Predictive modelling of data to calculate the risk of future admission may be the best available
technique, but requires ongoing access data to update risk profiles as populations and services
change with regression to the mean and other unpredictable factors.(44, 47)(45)

Chronic illness and the life course modelling. Using a different lens multiple hospital admissions can
be seen to take place in trajectories of poor self-rated health (SRH) and limited social support.(48)
Such trajectories occur towards the end of life (49)(50). End of life trajectories are rarely predictable
in the every day fluctuations of individual patient care, although there are patterns associated with
cancer, organ failure and frailty. (49)(50)An older person's perception of his or her own health is an
important predictor of this trajectory.(51)(52)

Multiple studies confirm the predictive validity of SRH in older populations concerning future
health(53, 54), functional decline, disability, mortality, increased costs and hospitalisations.(55,
56)(57) Jylhä interprets SRH as a personal individual and subjective self-awareness that is the
strongest biological predictor of death.(58-60)The SRH question "would you say your health in
general is excellent, very good, good, fair, or poor?" appears to reflect inner tacit awareness of one’s
health journey, that is most meaningful to each individual,(61). The one measure of self-rated health
predicts adverse health events,(47) initial hospitalisation and repeated hospitalisation, especially in
people with heart failure. (56)

A wide range of body functions, activities and personal factors are associated with levels of SRH
among community-dwelling older people. Some of these, such as physical capacity, depressive
symptoms and habitual physical activity are of particular interest due to their potential for change


through health promoting interventions.(54, 57) Increased frailty and chronic diseases are closely
correlated with worse self-rated physical and mental health, and are associated with greater health
disparities and worse neighbourhoods. (62)Interventions need to address self-related health and
self-efficacy (63), but much of this may be linked to neighbourhoods where social and environmental
issues need to be addressed.(62)

Averting avoidable hospitalisations

Interventions to increase the general health of elderly people and avert preventable conditions
include vaccination, falls prevention, nutrition and physical activity programs.(29) Interventions in
primary care include case management, specialist geriatric care, acute care in primary settings, after-
hours primary care consultation, medication management, and health assessments.(27,
29)Interventions in secondary care are short stay or observation wards, routine discharge planning,
presence of specialist and GP staff in the emergency department, and the use of decision-making
protocols on admission.(64, 65)

A number of interventions across health care levels include quick response services, geriatric day
hospitals, comprehensive geriatric assessment, advanced health directives and coordinated care.(64,
66)Guided care, case management, nurse specialist clinics and tele-home care are specific strategies
which have been found to be successful in reducing avoidable hospitalisations. (67-74)Self-
monitoring and self-management is a key element of disease management preventing
hospitalisations.(75) Tele-monitoring in chronic disease’s impact on hospital admissions and costs
remain controversial, (31, 76, 77) but has been shown to improve quality of care.(54) Ongoing
effectiveness studies and data from functioning health systems may clarify the impact of different
components/types of tele-monitoring programs’ impact on hospital use and costs.(78, 79)

Case management or geriatric case management is a currently favoured solution to the varied needs
of people who are at high risk of admission or readmission.(15, 22, 30, 80) Pay for performance
related to avoidable admissions is taking effect in the US and the UK, yet the feasibility and
effectiveness of pay for performance is not proven.(81-83) Evaluations of the impact of complex
system wide health interventions to reduce admissions are difficult to draw conclusions from,(84,
85) as they require social and other inputs beyond medical care.(86, 87) In short the cost-
effectiveness of specific interventions or different program models in different settings to reduce
avoidable hospitalisations remains unclear(88, 89) often because the interventions are complex and
adaptive to prevailing circumstances and difficult to evaluate.(90, 91)(30, 79).

In the US, the major models are the Chronic Care Model, the expanded Chronic Care Model and
variations lead by Kaiser Permanente, Evercare, Pfizer, PACE and other variations including
Healthways.These models show positive findings in particular US settings (92).(84). However, it is
unclear which components work in which settings.

Conclusion

Hospitalisations for older people with chronic conditions are expensive and many can be
avoided.
Threshold Risk Scores, commonly used for predicting avoidable hospitalisations
areoperationalized through several risk assessments metrics. Predictive Risk modelling using


multiple sources of data, may be more effective than Threshold risk prediction for
identifying large cohorts at increased risk, but are limited in predicting trajectoriesin
individuals on an everyday basis.
Interventions to prevent potentially avoidable hospitalisations are complex interventions –
case management, discharge planning and follow up, monitoring, telehealth, increased
education and greater primary care access are important components of systemic
interventions found to be effective.
Although there is much variability and unpredictability multiple admissions may signify the
recognisable pattern of common end of life trajectories. Self-rated health is a robust
predictor of deteriorations and sentinel health events, as well as hospitalisations and
increased costs of care. Addressing poor self-rated health, which correlates with chronic
disease progression and frailty, requires supportive enablement and addressing of social and
environmental issues, as well as chronic disease management.

The conceptual framework for the PaJR project
The patient journey concept recognizes that hospital admissions take place in journeys through
stages of health and illness,(93) which are strongly influenced by the social and non-social
determinants of health. (94) The individual patient journey is shaped by their biological state and
disease process, and their health care, social and environmental milieu. The need for hospitalisation
is strongly linked to feeling ill and whether one has supportive care at a personal level.(43) Self care
and the work of managing the illness increasingly requires informational and practical support as
illness become unstable.(95, 96) The general practitioner and the primary care team have a
longitudinal journey with their patient through phases of health and illness, stages of care including
health promotion and prevention, risk management, diagnosis, treatment, and self-management.
(97, 98)

Enablement through support, coaching and feedback is a key concept.(99, 100)Key elements include:
addressing health anxiety and barriers to seeking help, and enabling people to self-manage and seek
help in a timely and as needed basis. In addition PaJR creates more directed support and
recommendations through real-time monitoring and intervening where necessary.

Complex adaptive systems theory and resource use
The patient journey represents a complex adaptive system that requires real-time monitoring to
identify deteriorations and improvements. Services are directed to address fluctuations in health
and health concerns when they are needed. Frailty is a concept of an aging human with diminished
functional reserve in the whole body system that includes the internal milieu of body organs and
systems and the external physical, personal and social environment. Deterioration in any of these
areas can quite easily have a follow-on effect potentially like a house of cards. Daily concerns, self-
rated health and other narratives are elicited to identify these deteriorations early and create
positive feedback through health promotion and other interventions to avert deterioration.

The everyday nature of PaJR monitoring by trained low cost care guides means that the needs of
patients can be responded to by the primary care professionals in anticipation of deterioration with
more immediacy and efficiency.


At a primary care systems level PaJR facilitates connections among team members. It sits between
disease management and case management, being a holistic primary care service. It has the
potential to monitor and support patient through different care phases from chronic illness to end of
life and hospice or end of life care.

Action research, machine learning and clinical learning continually improve the system. As number
grow, predictions of deteriorations and interventions that work are continually refined and PaJR
adapts the way it works.

Description of the PaJR System

Innovative care pathway with 1 Care/Case Manager supervising 10 or more Care Guides
working with a Primary Care Physician on call to address queries. Care Guides substitute for
many tasks of case management, expanding the reach of case managers and enhancing their
efficiencies with innovative predictive modelling based on frequent short phone
conversations.

The PaJR system uses lay care guides to remotely monitor patients with chronic conditions
on a daily basis or as required basis with semi-structured phone conversations. Each call is
made to the patient and/or their care giver on an agreed basis.

A week day structured questionnaire is completed online by the call operative based on the
answers given by the patient or their care giver once the phone conversation takes place.
These answers are analysed by machine learning processes and red or amber flag alerts are
then assigned to narratives on the questionnaire to alert the care team to follow up with an
intervention.

The week daily calls to the patient or care giver are performed in a very conversational
manner by the care guide.

PaJR phone conversations about the daily narratives living, concern person-centred general
health-related questions. Each phone conversation is audiotaped and stored on a database.

Call frequency and date of next call is determined by the number of flag alerts generated
for that patient by machine learning based on their calls to date along with their reported
self-rated health figure reported and the amount of calls the patient wants to receive
weekly.

The PaJR system transmits responses to a machine learning service that monitors
quantitative and qualitative features of the narrative and the language and voice.


PaJR machine learning service scans narratives of: Illness (incorporating: health
perceptions, mental health, pain, health promotion); Medication; Medical & Healthcare Use;
Social Support; Environmental Concerns and Health Promotion.

Figure 2. An over view of the Data Flow in the PaJR System

The PaJR system can predict patients’ future health and risk of unplanned events, with
machine learning using semantic analysis of conversation records in real time to the level of
90% accuracy, which improves clinical and simple rule based predictions.

PaJR triggers flags and alerts in real time. These are instantaneously reviewed by the care
guide using software that organises the data and highlights alerts. This allows prompt
responses to alerts.

Survey Question Types
The semi-structured survey encompasses a range of open ended and closed questions based on key
narratives designed to pick up on deterioration in health of patients with chronic conditions.The
narratives included in the survey are; illness, medication, medical and healthcare use, social support,
environmental concerns and health promotion. Each narrative has a set list of questions which the
call guide asks in during each call by including them into the conversation.

An example of an open ended question:
“Have you any concerns today?”

An example of a closed question:
“Can you give a number between 1 and 10 to describe your health today?”


Figure 3. Care Guides Data Entry System into the PaJR database.

Intervention Types – triggered by PaJR Alerts

Interventions are made in health care, social care, environmental and health promotion areas where
possible when alerts are designated to a narrative type by machine learning. Social and
environmental interventions depend greatly on the services available in the location in which PaJR is
based. The main types of interventions made include:

Health care

Recommending visits to GP or other health care professionals.
Recommending visits or phone calls to pharmacists if a query about prescription, dosage or
other medication related issue arises.
Contacting the patients GP or PHN in the case of a continuous red or amber flag being
generated for a given patient related to their healthcare or heath needs.
Arranging appointments with specialists, physiotherapists, speech and language therapists
and occupational therapists etc.

Social care

Organising visits to day care centres.
Setting up befriending services.
Arranging bereavement or other support counselling.

Environmental

Contacting St. Vincent de Paul regarding heating and other housing problems.
Applying for home insulating grants for patients living in older homes if eligible (Mayo only).


Health Promotion

Organising meals on wheels.
Providing motivational advice to patients trying to quit smoking or drinking.
Giving information and recommendations around the areas of diet, exercise, smoking and
alcohol intake.
Supporting chronic disease self-management

Other

Applying for panic alarms or panic buttons.
Contacting and putting patients in contact with AWARE and other organisations for
information on behalf of patients.
Referring people to organisations and resources to help them manage their chronic illness
and geriatric syndromes.

Patient Journey Record (PaJR) Online Prediction System

Key considerations in the PaJR system are the design and implementation of robust expert
knowledge and data support systems that incorporate text analysis, machine learning and predictive
modelling developed by Dr Carl Vogel and his team. Care guides record a call by following an online
clinically derived questionnaire. The analytic engine immediately decides the traffic light category of
the call.

Features considered by the analytic engine include:

Patient and Guide indicators
Self-rated health, predictions of risk for unplanned events and hospitalisation and other
measures
Measures of trajectory over recent calls
Words and phrases, type-token ratios, item length
Patterns of language use
Possibly features of call recordings
Speech quality, breathing, turn-taking…

These analytics can address complex or uncertain issues that cannot be solved with a specified rule
or algorithm.(101) The analytics engine allows identification of features that emerge as predictive of
deterioration. It has “perfect memory” and allows high accuracy, high volume at low cost. PaJR
analytics can anticipate deteriorations more quickly than manually by care centre staff.

A key feature of PaJR is its machine learning component which predicts deteriorating patient status
based on patient responses to caller questioning. We apply machine learning methods to predict
patient status in the near future. In this study, three target statuses are of interest: next urgent
unplanned event (NUUE), next unplanned event (NUE) and next self-rated health (nextSRH). Patient
baseline records and daily online interview logs deliver rich information about patients' current
status. We extract linguistic and meta-linguistic features together with current patient status, in
order to train prediction models. To predict the binary value of NUUE we use a decision tree based
on a highly refined set of features organised hierarchically as rules.


Efforts are made to minimise false negative predictions. Currently the system covers 23 out of 27
NUUE cases in 1571 patient interviews with the cost of 453 false positive predictions. A false positive
prediction might trigger a phone call or visit, but its cost is much less than a false negative
prediction, where a true danger is overlooked. The patient status prediction system is constructed in
two phases, and it responds to requests in nearly real time. The two phases are: offline training
module and online prediction module. The offline training module utilises the newest patient
interviews and re-trains decision models within a few hours, while the online prediction module runs
over the latest successful model, and it takes only moments to deliver prediction results.

Progress-to-Date
PaJR has been piloted in 3 locations, a GP Out-of-Hours service, a community setting and hospital
based settings. Phase 1 has been completed. The theory and concepts1 and high level results have
been reported.(102, 103) We are now rolling out Phase 2 in several regional locations. Phase 3
approach and protocols are being developed. For larger pragmatic demonstration, a randomized
controlled is planned and we are seeking further funding.

Recruitment of patients
Recruitment processes for patients differs depending on the setting.

Hospital based setting recruitment – e.g. Nenagh hospital

For PaJR hospital based settings, patients meeting the criteria are recruited before discharge from
hospital given the consent of their GP if they wish to take part. Patients are eligible for recruitment,
if they have one or more chronic conditions, over the age of 65 years. Criteria exclude patients
residing in nursing homes from taking part. GPs are informed in advance of the system and asked to
participate in orderthat their patients would be selected if suitable. The call guide carries out the
baseline interview with the patient while in hospital and begins the daily phone calls on day 2 of
discharge from hospital. The patient (or their caregiver) receives 5 phone calls during the first 5
week days and a subsequent number of calls for a 28 day period. After the first 5 calls, the frequency
of calls for that patient is determined according to the number of flag alerts generated by that
patient during calls, their reported self-rated health and also the number of calls the patient of their
care giver would like to receive.

Community based setting recruitment – e.g. Castlebar, County Mayo

PaJR community based setting varies in terms of recruitment. GPs are contacted about PaJR and
recruited to take part. GPs select 10 of their patients suitable for PaJR to take part. The GP then
contacts the patient and informs them about PaJR, gets their verbal consent to take part and gives
the patient’s contact details to the PaJR team. The call guide then contacts the patient and carries
out the initial interview over the phone with them. Week daily calls begin on the following week day.
Each patient receives 5 calls similar to the hospital based PaJR setting system and continues on for a
total of 28 days.

Control Patients

To show that the PaJR system effectively works to reduce avoidable hospital admissions, a cohort of
control patients were recruited to compare the number of avoidable unplanned hospitalisations.
Control patient cohorts meet the same criteria as intervention patients and carry out a baseline
interview with the call guide. The control patients are followed up on after 28 days and asked about
any hospitalisations or visits to out-of-hours services since their initial interview. GPs are aware that
some of their patients will be chosen as control patients. The control patients are followed up every
28 days for a period of 6 months. They are then given the option to go into the PaJR system as an


intervention patient if they wish to do so.In total, PaJR is monitoring 170 patients with 3 full-time
operatives in the current pilots.

Results

Phase 1 – KDOCOut-of-Hours Service, Naas, Kildare.
In October 2010, the PaJR system pilot in Kildare Doctors on Call (KDOC) identified, from their call
database, all people over 18 who met PaJR criteria for chronic illness in a three month period who
were referred to A&E or transferred to hospital or had an out of hours home visit who were suitable
for a phone monitoring system.

In Phase 1 of the study patients were telephoned by PaJR care guides and asked key questions,
eliciting narratives and reports on their symptoms, their health and SRH, social supports and health
events.(103) Care guides made outbound phone calls to 129 people 1 to 5 times per week for up to
12 months (315 person months), according to their stability. Analysis ofthese3000 work day ‘daily’
phone calls over 12 months was a follows: 2-3 minutes for ‘no problem’ calls (50%); 3-5 minutes
(25%) for complicated and 5 minutes plus (25%) for problem complex calls. Validated predictive
modelling and rule based alerts in key domains prompted illness, healthcare, medication, social and
environmental interventions by care guides under the supervision of a clinical nurse. The alerts were
predominantly for prompt GP care, but a substantial number were for interventions with respect to
pharmacists, public health nurses, social welfare and geriatric services.

Forty six patients identified through the GP Out-Of-Hours data base, as being at high risk for repeat
admissions, have been monitored for 12 months plus. In an initial control cohort 1, there were no
interventions in 12 patients over 1 month with an admission rate of 43%2. In cohort 2, 46 patients
recruited in the same manner from the same GP practices in the same season, have been monitored
with the PaJR system with interventions and admissions tracked. Admissions per month steadily
decreased until they have reached 4.3%. Increased and more targeted service use with GPs,
pharmacists and nurses and health promotion recommendations as the main mechanisms triggered
as a result of the PaJR alerts.
Table 1. Phase 1 Intervention and Control cohorts consecutively selected from Kildare and County West Wicklow GP Out
of Hours data base.

KDOC Kildare Cohort 1 Cohort 2 Cohort 2 Cohort 2
out of hours (monitoring only) (monitoring & (monitoring & (monitoring &
intervention) intervention) intervention)

Patients per no. 12 for 28 days 46 for 28 days 46 for 56 days 46 for 105 days
of days

SRH 2.9 (fair-good) 1.8 (poor-fair) 1.8 (poor-fair) 1.8 (poor-fair)
(self-rated
health)

Care Giver 3 (25%) 6 (13%) 6 (13%) 6 (13%)
present

Hospital 5 (42%) 8 (17%) 9 (10%) 10 (6.2%)
admissions unplanned unplanned unplanned unplanned
(+planned) (+1 planned) (+1 routine) (+1 routine)

A&E visits 4 (33%) 3 (6.5%) 5 (5.4%) 6 (3.7%)

Av no. of GP 1.25 GP visits per 5.6 visits per 11.43 visits per 2 11.8 visits for 3.5
visits month per person month per person months per person months per person


Phase 2– Nenagh Hospital, Tipperary and County Mayo.
Nenagh hospital, Tipperary
Readmission avoidance program – post hospital discharge started June 6th, 2011 with 1 part-time
operative using PaJR software system and workflow.

County Mayo and Mayo General, Castlebar hospital, Mayo
Admission avoidance program started in September, 2011.

Overview of PaJR calls
BetweenNovember 2010 and November 2011, the PaJR team has monitored 42 control patients and
132 intervention patients in the three locations of Kildare and County West Wicklow, Tipperary and
County Mayo. Their characteristics are described in Table 1.

Table 2. Participants Baseline Characteristics based on Pra Score(40). Selection criteria were:all Doctor visits
past 12/12≥ 7, Hospital admissions in the past12/12 ≥1 and One or more of major chronic diseases –i.e. CVS,
COPD, Diabetes, Gastrointestinaland GP agrees to participate in study.
Control Group Intervention

Number (175) 42 132

Age (74) 73 75

Self-rated health Poor-fair (1.68/5) Poor-fair (1.49/5)

Caregiver availability 43% 46%

At the time of reporting, 3973 patient outbound calls and conversations were recorded in the three
locations of Kildare and County West Wicklow, Tipperary and County Mayo. Some key findings
include the following,

35% of calls reported concerns on that day related to their health
25% of calls reported fair to very poor self-rated health,
17% calls reported moderate to severe pain on that day

Table 3 Recommendations made by Care Guides

Medication related – recommending contacting GP or pharmacist if concerns arise regarding
dosage/side effects 3% calls

Environmental issues e.g. heating problems, contacting community welfare officers, St. Vincent de
Paul etc. and safety concerns

Social issues e.g. Recommending day care centre visits ~5%calls

Health Promotion – related to eating, sleeping, exercises, self-care 15%

Healthcare issues e.g. Suggesting GP visits, setting up appointments with specialists ~ 11% calls

Contacting GPs in relation to concerns about medication/treatments for participants <3% calls


These interventions were recorded by Care Guides and a process of validation with audio-taped calls
will be undertaken. It is anticipated that recommendations about other issues such as spirituality,
social support, concerns about care access will emerge.

Table 4 Number of calls where service interventions were reported in intervention group of 320 person months.

Services in Intervention Group Unplanned Planned/other Total

GP visits 106 638 744

OPD Specialist 20 336 356

Visit to a Casualty or Emergency 18 0 18
Department

Hospital admission 38 26* 64**

*Admissions which were planned such as for surgical procedures or investigations or did not include an overnight stay in
hospital are included. **Hospital readmissions that took place within 24 hours of discharge were not included in numbers.

Service Use

Over 12 months 132 patients ( 320 person months) had 64 admissions in total of which 38 were
unplanned. On average, there were 5 admissions per month and 3 unplanned admissions per
month.Admissions which were planned such as for surgical procedures or investigations or did not
include an overnight stay in hospital are included. Hospital readmissions that took place within 24
hours of discharge were not included in these numbers.

Respite care was reported in28/1500 calls; rehabilitationwas reported in 7/1500 calls where
question was asked.

GP Home visits of an unplanned nature were reported in 42 calls and planned home visits were
reported 181/1500 calls where question was asked.

Similarly home visits were reportedfromPrimary care team (usually public health nurse) in 226/1500
calls and attendances at other services in community.

Speech and language therapists, community pharmacists, dieticians, community welfare officers,
dentists, chiropodists) were reported in 22/1500 calls.

Patient expressed high levels of satisfaction with participation. A high degree of satisfaction was
reported by patients, caregivers and GPs. All KDOC GPs have now signed up for the Naas hospital
trial. (See Appendix 1 for letters of satisfaction from the pilot sites and patient initial feedback)

Control Group

Control group statistics are identified through monthly phone calls to the controls to ascertain their
service use and issues.

Table 4 represents a summary of control admissions at 1 month after monitoring 11/35 patients.
Admissions which were planned such as for surgical procedures or did not include an overnight stay
in hospital were not included. Nor were hospital readmissions that took place within 24 hours of
discharge included in the readmission statistics. At least two patients felt that their care and support
were suboptimal. See Table 4


Q3 Since PaJR's last call have Q5 In past 4 weeks how many Q6 If there is anything else you
Control I.D. you had any healthcare service? stays (overnight) as a patient in would like to tell us.
hospital? unplanned

C01 yes no
C02 yes yes
C03 yes yes
C06 yes yes
C07 yes yes
C09 yes no
C10 yes no
C11 yes yes
C12 3 visits from the nurse no
C13 st john's hospital have visited 1 occasion planned
regarding his sleep apnoea
C17 no No very dissatisfied with no help for
his breathing
C18 Yes Once Once u/p Starting chemotherapy soon
C19 no no kidney investigated but no
results and still problems
C20
C21 no none
C22 no none
C28 no none
C31 no
C32 Yes in hospital u/p
C33 Yes u/p
C35 Yes Yes 2 admissions u/p
C36 No results yet No results yet No results yet
C37 ‘
C38
C40
C41
C41
C42
Total 11 admissions
Table 5 Control outcomes at 28 days of follow-up for 11 unplanned admissions for 35 patients.

Table 6Comparison of the intervention and control groups

Control Group Intervention

Number (175) 42 (results available 35 132 (results available 320
person/months) person/months)

Unplanned Hospital admissions/month 11/35 person months (31%) 38/320 person months (10%)

Estimated potential Savings from Hospitalisations in County Mayo

There are around 18,000 older citizens in County Mayo, of who 5% approximately 800 are at very
high risk of hospital admission at any one time. Each elderly person over 75 has almost 1 admission
per year, for more than 12 days, thus the 800 would have at least 1 hospital admission per year and
likely 2-3. (Public Health Information System, 2008) Reducing hospitalisations by 50% in this group is
feasible according to our pilot studies. The cost of each admission for an older person for 10 days is
900 Euros direct costs and 1000 Euros if indirect costs such as ambulances and time spent in the A&E


are considered, which could be reduced from 800 to 400 would save 4million Euros potentially.It
would take 8 Care Guides with full-time nurse supervisor working on the phones for 5 hours per day
to cover this population in the following manner: 2-3 minutes for ‘no problem’ calls (50%); 3-5
minutes (25%) for complicated and5 minutes plus (25%) problem complex calls. The estimated cost
for the service per year is 300,000 Euros.

Other cost savings such as reductions in emergency department visits and delays in nursing home
admissions and respite care would likely ensure. Other costs such as increased community services
might arise but would be small compared to the potential savings. Overall outcomes were a
reduction of admissions by 50% in pilot studies. Impact was similar on emergency department
attendances. Impact on nursing home and other services has not been calculated, but there is likely
to be a delay or reduction in such use. It has become apparent that a considerable proportion of
routine visits are for social purposes(104) could be replaced by more timely visits to avert
deteriorations, if their time was freed up and PaJR could provide alerts. It would take 8 Care Guides
with part-time nurse supervisor and a full time manager working on the phones for 5 hours per day
to cover this population in the following manner: 2-3 minutes for ‘no problem’ calls (50%); 3-5
minutes (25%) for complicated and 5 minutes plus (25%) problem complex calls. The estimated cost
for the service for one year for Co. Mayo is 350,000 Euros per year. (Table 5)
Table 7. Estimated costs and benefits related to PaJR intervention in 5% of high risk >65year olds in County Mayo

Other Findings from patient cohorts
The average age of the patients recruited to date is 69.7 years. The patient population of the
combined cohorts to date is composed of 34.5% males and 65.5% females. Emerging findings and
trends among cohort populations include:

Nenagh hospital patient cohort

As this is a hospital based setting, patients are being recruited before discharge from hospital and
are reporting poorer self-rated health to those patient cohorts in KDOC and Mayo. Patients in
Nenagh are on average a sicker cohort of patients with the majority reporting cardiovascular, heart
failure and gastrointestinal illnesses.

Mayo patient cohort

Due to the large number of elderly people living in relative isolation across Co. Mayo, mental health
issues including depression and alcohol related problems are the most commonly reported problems
to date among the patient cohort in Mayo. Patients recruited to the system in Mayo meet the
criteria of having one or more chronic conditions and are on average a more aged patient cohort


with the average age being 74 years. GPs in Mayo are referring to the study many of their patients
with manageable chronic conditions e.g. as they feel that need for social support which the program
offers will benefit their patients greatly. While some of the patients recruited are availing of locally
run social support services like the telephone befriending services, the majority are not. PaJR is
providing a link to these befriending telephone services for patients once they exit the PaJR system.
This will allow for follow on support for patients from the trained befriending callers working on the
local befriending service.

KDOC patient cohort

This cohort of patients has a more varied patient population ranging from patients in their 30’s with
gastrointestinal problems to patients of 80+ years with cardiovascular problems. The range of
chronic conditions varies most among this cohort of patients with more cases of diabetes,
hypertension, multiple sclerosis, Chron’s disease and other gastrointestinal conditions.

Emergent Findings

How does PaJR work? Early detection of deterioration and guiding self-referral to GPs for medical
assessment is a key mechanism. Addressing barriers to help seeking such as health anxiety, poor
mobility, social anxiety (fear of being a nuisance) by enablement and encouraging the person to seek
help themselves where possible on identification of deterioration. Learning is fostered, such that
people and care givers more readily identify when things are deteriorating and have strategies
reinforced by PaJR feedback. Social contact, practical support and informational support are
important for the isolated patients and particularly caregivers.

Machine learning and predictive modelling are continually improving the accuracy of when to make
calls, identifying more narrative and quantitative features to predict unplanned admissions and visits
to the emergency department.

PaJR is a learning system that is adaptive to different populations of patients and will continue to
improve.

Conclusion
This report is a work in progress. Data collection is ongoing and statistics are changing daily as we
collect more data and continually improve the system.

Further data collection, data validation and analysis is in progress.

To date it has been feasible to run the program in 3 locations, monitoring 132 patients over 320
person/months for15 months since starting the project. We are continually expanding our scope and
the numbers that lay care guides can manage.

We have estimated potential savings from reduced admissions that could be shifted to more
appropriate services in the community and still potentially make savings for the HSE.

PaJR is potentially a very useful system that sits well with the HSE primary care teams, GPs, Out of
Hours and acute services. It has potential to reduce costs in the acute care sector and shift savings to
community and social care. Feasibility and acceptability have been demonstrated. It is now
important to conduct a randomised control trial with large enough sample size to detect
improvements in operational environments beyond pilots.


Next Steps – Phase 3
We are in the process of conducting a randomised control of community based patients in Mayo and
Nenagh. We plan to conduct a randomised controlled trial of hospital readmission prevention in
Naas Hospital. (Application has been submitted and is under review in the Health Research Board)
We are piloting working with palliative care patients. We plan a trial in the US and Canada in 2012.


Appendix 1

Letters of Satisfaction and Support


Primary Care Development Officer
Mayo Primary, Community & Continuing Care
HSE West
St. Mary’s Headquarters
Castlebar
County Mayo

Laurence.Gaughan@hse.ie

 (094) 9042509/(094) 9042019
 (094) 9025957
Re: Patient Record Journey System – HRB Research Application

As Primary Care Development Officer here in Mayo, with the HSE, I had the pleasure of being contacted by Ms.
Deirdre Grady, Clinical Manager, and Dr. Carmel Martin, GP and Project Lead, earlier this year with a view to
extending their study to the Mayo Area. We invited Carmel, Deirdre and Dr. John Kellett to do a presentation
for relevant staff and Managers here in Mayo and we were immediately impressed with the strong evidence
base and professionalism of the Team.

The study has now commenced here in Mayo, following the recruitment of two part time telephone support
people, who are based at the offices of the Castlebar Social Services. This also provides an ideal partnership
setting for delivery of this model in Mayo. To date, the service has been embraced by all of our Primary Care
Teams and approximately 15 GP practices in the County are participating in the study and have referred
patients. We are confident that the study will have a significant impact on the lives of 100 to 150 people in the
County and that there will be significant learning and transferability from the findings of this study, to other
Primary Care Settings throughout the Country. We very much support the funding application and look
forward to the further development of this service in the future.

Signed:_Laurence Gaughan
Laurence Gaughan
Primary Care Development Officer

th
6 October 2011


KDOC Patient Satisfaction Survey Feedback Report
Q1. Are you happy with the Kdoc PaJR call service which you have been receiving calls
from?
Oh marvellous. Great service absolutely Ye are a great comfort to me. I am very very grateful of the service as well
in various ways. Like with Catherine getting me x-rays earlier and with the counselling ye set up and all that. It’s
been really great.

Very very happy, yea.

Oh God yea. Very beneficial.

I am of course. I’m very thankful to ye for calling like ye have been doing. Family tend to forget about you. I
suppose they have families of their own now so it’s nice to get the call.

I have a lot of children as you know and God I don’t see them. They wouldn’t even think to check on their mother
sometimes.

I am yes

I am, oh I am. I like the calls coming and it’s great to know you check how things are going with me.

Oh of course dear. 100%,

Q2 Do you enjoy the calls?
They’re great. I find them a great support to me. It’s fantastic to know there is someone there for me.

I really do because I can touch on things that I don’t want to bring up in front of other people. I don’t know you
and you probably don’t know me so there’s a confidentiality thing there. I would never say to my children the
things I really want to say or talk about. I just don’t want to worry them.

I do. I feel like I know ye at this stage.

I love yous ringing me now and I’m very thankful to yous.

Oh yea I do of course. Long may ye keep phoning.

I do yea because it’s someone to talk to. And ye girls that ring me, offer to help me out with things if ye can. You
know, like making phone calls to different people that I wouldn’t know of myself.

I do yes. At least I know there is someone thinking about me.

Oh yes yes. We’ve got a great service.

Absolutely, I really do.

Q3 Have you any ideas on how to improve or change the service?
No it’s grand the way it is.

Not off hand. I would have to think very hard before I could come up with anything to make improvements on
what ye do.

Ah no. I think yous are very good.

No it’s great. But if I think of something you know I’ll be sure to let you know!

Not really. I was a bit mixed up at the start because I thought you were phoning from the day care centre. But
once I figured it out, I was flying it.


No I think, I think it’s good. I’ve never felt under pressure talking to you. I don’t think you could improve it really.

Well now that’s a hard thing to say. I don’t know.

No, I don’t really. I know there are terrible tragedies happening out there but it’s nice to think someone is there to
talk to you and understand what I’m going through. That means an awful lot to me.

I can’t see much more ye can do. Ye are always thorough with yere questions. Whenever ye ring it’s never a rush
job. It shows me ye are concerned.

I think yous do it as good as yous can across the phone.

No, I have to say I’m happy with the way it is.

Q4 Have you got any other feedback about the service?
I couldn’t praise ye enough.

I find it marvellous what ye do. It’s been great for me.

No not at all. I’m very happy with it and thankful for what you do.

I couldn’t have done counselling. Even after my husband’s death I didn’t go for help. I feel it’s for people with
serious problems. But you have a great way of warming things out with me.

Ah no.

Q5 Are you a medical card holder?
Yes
Yes
Yes
Yes
Yes
Yes
No.. Can you get me one!
Yes.
I am now, yes.

Q6 Would you be willing to pay for a service similar to this?
Well, it would have to very reasonable or I couldn’t afford it.

No, I couldn’t afford it

I would, if it wasn’t too dear, I would of course

I would love to but I don’t think I could afford it on my pension

I would I suppose, if someone explained to me and talked to me about it, I would.

If I could afford it, I certainly would.

Well, how much would it cost? The pension isn’t going as far as it used to these days so I don’t think I could afford
much myself.

Yes, I would yes.

Of course, why wouldn’t I.


Q7 If so, what would you expect to pay for such a service?
If you tell me what it would cost, I can tell you straight away if I could afford it or not.

You’re talking to someone who has no money, so I couldn’t give you an answer to that.

I suppose you would have to judge it against what people pay for counselling. I wouldn’t pay that much now. I
think it’s as good as a counselling service for those who need it.

Well after the next budget, I don’t think I will be able to spare very much to pay for it.

That’s not a very easy one to answer. My income is my pension and I pay the rent out of that. That comes to
4,500 euro a year and that’s a lot for us.

Well I suppose, what would it be, the price of a doctor’s visit maybe. Yes, around that because I suppose the moral
support is as good as the medical support so why wouldn’t it be at least the price of a doctor’s visit anyway.


References
1. Martin CM, Biswas R, Joshi A, Sturmberg J. Patient Journey Record Systems (PaJR): The
development of a conceptual framework for a patient journey system. Part 1. In: Biswas R, Martin C,
editors. User-Driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and
Technologies. Hershey PA USA: IGI Global; 2010.

2. Fleming ST. Primary care, avoidable hospitalization, and outcomes of care: a literature
review and methodological approach. Med Care Res Rev. 1995;52(1):88-108. Epub 1995/03/01.
3. CARDI:Centre for Ageing Research and Development in Ireland. Stocktake of Ageing Public
Policy Initiatives in Ireland, North and South. 2008 [cited 2011 12/9/11].
4. Moloney ED, Bennett K, Silke B. Factors influencing the costs of emergency medical
admissions to an Irish teaching hospital. Eur J Health Econ. 2006;7(2):123-8. Epub 2006/03/07.
5. Crimmins EM, Hayward MD, Saito Y. Changing Mortality and Morbidity Rates and the Health
Status and Life Expectancy of the Older Population. Demography. 1994;31(1):159-76.
6. Royal College of Physicians of Ireland, Irish Association of Directors of Nursing and
Midwifery, Therapy Professions Committee, Quality and Clinical Care Directorate, Executive HS.
Community medical services for the older person. Report of the National Acute Medicine
Programme2011.
7. Agency for Healthcare Research and Quality. Chronic Care and Disease Management
Improves Health, Reduces Costs for Patients With Multiple Chronic Conditions in an Integrated
Health System. In: Department of Health and Human Services, editor. United States2009. p.
http://innovations.ahrq.gov/content.aspx?id=1696.
8. Jimenez-Puente A, Garcia-Alegria J, Gomez-Aracena J, Hidalgo-Rojas L, Lorenzo-Nogueiras L,
Perea-Milla-Lopez E, et al. Readmission rate as an indicator of hospital performance: the case of
Spain. Int J Technol Assess Health Care. 2004;20(3):385-91. Epub 2004/09/28.
9. Lindquist LA, Baker DW. Understanding preventable hospital readmissions: Masqueraders,
markers, and true causal factors. Journal of Hospital Medicine. 2011;6(2):51-3.
10. Yam CH, Wong EL, Chan FW, Leung MC, Wong FY, Cheung AW, et al. Avoidable readmission
in Hong Kong--system, clinician, patient or social factor? BMC Health Serv Res. 2010;10:311. Epub
2010/11/18.
11. Upshur RE, Moineddin R, Crighton E, Kiefer L, Mamdani M. Simplicity within complexity:
seasonality and predictability of hospital admissions in the province of Ontario 1988-2001, a
population-based analysis. BMC Health Serv Res. 2005;5(1):13. Epub 2005/02/08.
12. Martin M, Hin PY, O'Neill D. Acute medical take or subacute-on-chronic medical take? Ir Med
J. 2004;97(7):212-4. Epub 2004/10/20.
13. Commission. MPA. Payment policy for inpatient readmissions. In:Report to the Congress:
promoting greater efficiency in Medicare. . Washington (DC)2007 [cited 2011 Sept 19]; Available
from: www.medpac.gov/chapters/Jun07_Ch05.pdf
14. van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospital
readmissions deemed avoidable: a systematic review. Canadian Medical Association Journal.
2011;183(7):E391-E402.
15. Purdy S. Avoiding hospital admissions.What does the research evidence say? London UK:
The King’s Fund, 2010.
16. Durand AC, Gentile S, Devictor B, Palazzolo S, Vignally P, Gerbeaux P, et al. ED patients: how
nonurgent are they? Systematic review of the emergency medicine literature. Am J Emerg Med.
2011;29(3):333-45. Epub 2010/09/10.
17. Vest J, Gamm L, Oxford B, Gonzalez M, Slawson K. Determinants of preventable
readmissions in the United States: a systematic review. Implementation Science. 2010;5(1):88.
18. Brabrand M, Folkestad L, Clausen NG, Knudsen T, Hallas J. Risk scoring systems for adults
admitted to the emergency department: a systematic review. Scand J Trauma Resusc Emerg Med.
2010;18:8. Epub 2010/02/12.


19. Linertová R, García-Pérez L, Vázquez-Díaz JR, Lorenzo-Riera A, Sarría-Santamera A.
Interventions to reduce hospital readmissions in the elderly: in-hospital or home care. A systematic
review. Journal of Evaluation in Clinical Practice. 2010:no-no.
20. Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, et al. Structured telephone
support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database
Syst Rev. 2010(8):CD007228. Epub 2010/08/06.
21. van Walraven C, Oake N, Jennings A, Forster AJ. The association between continuity of care
and outcomes: a systematic and critical review. J Eval Clin Pract. 2010;16(5):947-56. Epub
2010/06/18.
22. Eklund K, Wilhelmson K. Outcomes of coordinated and integrated interventions targeting
frail elderly people: a systematic review of randomised controlled trials. Health Soc Care Community.
2009;24:24.
23. Kellett J. Hospital Medicine (Part 1): what is wrong with acute hospital care? Eur J Intern
Med. 2009;20(5):462-4. Epub 2009/08/29.
24. Smith SM, Allwright S, O'Dowd T. Does sharing care across the primary-specialty interface
improve outcomes in chronic disease? A systematic review. Am J Manag Care. 2008;14(4):213-24.
Epub 2008/04/12.
25. van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospital
readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391-402. Epub
2011/03/30.
26. Dennis SM, Zwar N, Griffiths R, Roland M, Hasan I, Powell Davies G, et al. Chronic disease
management in primary care: from evidence to policy. Med J Aust. 2008;188(8 Suppl):S53-6. Epub
2008/06/17.
27. Department of Health VG, Australia. Hospital Avoidance Reduction Program (HARP).
http://wwwhealthvicgovau/harp-cdm/indexhtm 2009.
28. Basu A, Brinson D. The effectiveness of interventions for reducing ambulatory sensitive
hospitalisations: a systematic review. . Cantebury, New Zealand: 2008 Contract No.: 6.
29. Beswick AD, Rees K, Dieppe P, Ayis S, Gooberman-Hill R, Horwood J, et al. Complex
interventions to improve physical function and maintain independent living in elderly people: a
systematic review and meta-analysis. Lancet. 2008;371(9614):725-35. Epub 2008/03/04.
30. McLean S, Nurmatov U, Liu JL, Pagliari C, Car J, Sheikh A. Telehealthcare for chronic
obstructive pulmonary disease. Cochrane Database Syst Rev. 2011(7):CD007718. Epub 2011/07/08.
31. Clarke M, Shah A, Sharma U. Systematic review of studies on telemonitoring of patients with
congestive heart failure: a meta-analysis. J Telemed Telecare. 2011;17(1):7-14. Epub 2010/11/26.
32. Shepperd S, Doll H, Angus RM, Clarke MJ, Iliffe S, Kalra L, et al. Avoiding hospital admission
through provision of hospital care at home: a systematic review and meta-analysis of individual
patient data. CMAJ. 2009;180(2):175-82. Epub 2009/01/21.
33. Gonseth J, Guallar-Castillon P, Banegas JR, Rodriguez-Artalejo F. The effectiveness of disease
management programmes in reducing hospital re-admission in older patients with heart failure: a
systematic review and meta-analysis of published reports. Eur Heart J. 2004;25(18):1570-95. Epub
2004/09/08.
34. Bittencourt RJ, Hortale VA. [Interventions to solve overcrowding in hospital emergency
services: a systematic review]. Cad Saude Publica. 2009;25(7):1439-54. Epub 2009/07/07.
Intervencoes para solucionar a superlotacao nos servicos de emergencia hospitalar: uma revisao
sistematica.
35. Courtney MD, Edwards HE, Chang AM, Parker AW, Finlayson K, Bradbury C, et al. Improved
functional ability and independence in activities of daily living for older adults at high risk of hospital
readmission: a randomized controlled trial. J Eval Clin Pract. 2011. Epub 2011/04/05.
36. Parry C, Min SJ, Chugh A, Chalmers S, Coleman EA. Further application of the care transitions
intervention: results of a randomized controlled trial conducted in a fee-for-service setting. Home
Health Care Serv Q. 2009;28(2-3):84-99. Epub 2010/02/26.


37. Allaudeen N, Vidyarthi A, Maselli J, Auerbach A. Redefining readmission risk factors for
general medicine patients. Journal of Hospital Medicine. 2011;6(2):54-60.
38. Wallmann R, Llorca J, Gomez-Acebo I, Ortega AC, Roldan FR, Dierssen-Sotos T. Prediction of
30-day cardiac-related-emergency-readmissions using simple administrative hospital data. Int J
Cardiol. 2011. Epub 2011/07/22.
39. Crane S, Tung E, Hanson G, Cha S, Chaudhry R, Takahashi P. Use of an electronic
administrative database to identify older community dwelling adults at high-risk for hospitalization
or emergency department visits: The elders risk assessment index. BMC Health Services Research.
2010;10(1):338.
40. Sidorov J, Shull R. "My patients are sicker:" using the Pra risk survey for case finding and
examining primary care site utilization patterns in a medicare-risk MCO. Am J Manag Care.
2002;8(6):569-75. Epub 2002/06/19.
41. Boult C, Pacala JT, Boult LB. Targeting elders for geriatric evaluation and management:
reliability, validity, and practicality of a questionnaire. Aging (Milano). 1995;7(3):159-64. Epub
1995/06/01.
42. Lyon D, Lancaster GA, Taylor S, Dowrick C, Chellaswamy H. Predicting the likelihood of
emergency admission to hospital of older people: development and validation of the Emergency
Admission Risk Likelihood Index (EARLI). Family Practice. 2007;24(2):158-67.
43. Martin C. Complex adaptive chronic care - typologies of patient journey: a case study.
Journal of Evaluation in Clinical Practice. 2011;17(3):1-5 online.
44. Roland M, Dusheiko M, Gravelle H, Parker S. Follow up of people aged 65 and over with a
history of emergency admissions: Analysis of routine admission data. BMJ. 2005;330:289 - 92.
45. King’s Fund. Predictive Risk Project: Literature review. 2005 *cited 2011 2nd September+;
Available from:
www.kingsfund.org.uk/current_projects/predicting_and_reducing_readmission_to_hospital/#conte
xt.
46. Lyon D, Lancaster GA, Taylor S, Dowrick C, Chellaswamy H. Predicting the likelihood of
emergency admission to hospital of older people: development and validation of the Emergency
Admission Risk Likelihood Index (EARLI). Fam Pract. 2007;24(2):158-67.
47. Diehr P, Williamson J, Patrick DL, Bild DE, Burke GL. Patterns of self-rated health in older
adults before and after sentinel health events. J Am Geriatr Soc. 2001;49(1):36-44. Epub
2001/02/24.
48. Weinberger M, Darnell JC, Tierney WM, Martz BL, Hiner SL, Barker J, et al. Self-rated Health
as a Predictor of Hospital Admission and Nursing Home Placement in Elderly Public Housing Tenants.
Am J Public Health. 1986;76:457-9.
49. Lunney JR, Lynn J, Foley DJ, Lipson S, Guralnik JM. Patterns of Functional Decline at the End
of Life. JAMA: The Journal of the American Medical Association. 2003;289(18):2387-92.
50. Lynn J, Adamson DM, Rand Corporation. Living well at the end of life : adapting health care
to serious chronic illness in old age. Santa Monica, CA: RAND; 2003. iii, 19 p. p.
51. Idler E, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community
studies. J Health Soc Behav. 1997;38:21 - 37.
52. Metz SM, Wyrwich KW, Babu AN, Kroenke K, Tierney WM, Wolinsky FD. Validity of patient-
reported health-related quality of life global ratings of change using structural equation modeling.
Qual Life Res. 2007;16(7):1193-202. Epub 2007/06/07.
53. Idler EL, Russell LB, Davis D. Survival, functional limitations, and self-rated health in the
NHANES I Epidemiologic Follow-up Study, 1992. First National Health and Nutrition Examination
Survey. Am J Epidemiol. 2000;152(9):874-83. Epub 2000/11/21.
54. Quirke S, Coombs M, McEldowney R. Suboptimal care of the acutely unwell ward patient: a
concept analysis. J Adv Nurs. 2011;67(8):1834-45. Epub 2011/05/07.


55. Bierman AS, Bubolz TA, Fisher ES, Wasson JH. How well does a single question about health
predict the financial health of Medicare managed care plans? Eff Clin Pract. 1999;2(2):56-62. Epub
1999/10/28.
56. Kennedy BS, Kasl SV, Vaccarino V. Repeated hospitalizations and self-rated health among the
elderly: a multivariate failure time analysis. Am J Epidemiol. 2001;153(3):232-41. Epub 2001/02/07.
57. DeSalvo KB, Jones TM, Peabody J, McDonald J, Fihn S, Fan V, et al. Health care expenditure
prediction with a single item, self-rated health measure. Med Care. 2009;47(4):440-7. Epub
2009/02/25.
58. Jylhä M. What is self-rated health and why does it predict mortality? Towards a unified
conceptual model. Social Science & Medicine. 2009;69(3):307-16.
59. Benning A, Dixon-Woods M, Nwulu U, Ghaleb M, Dawson J, Barber N, et al. Multiple
component patient safety intervention in English hospitals: controlled evaluation of second phase.
BMJ. 2011;342:d199. Epub 2011/02/05.
60. Jylhä M, Volpato S, Guralnik JM. Self-rated health showed a graded association with
frequently used biomarkers in a large population sample. Journal of Clinical Epidemiology.
2006;59(5):465-71.
61. Frith D, Brohi K. The acute coagulopathy of trauma shock: clinical relevance. Surgeon.
2010;8(3):159-63. Epub 2010/04/20.
62. Jean W, Ruth C, Jason L, Moses W. Relative Contributions of Geographic, Socioeconomic,
and Lifestyle Factors to Quality of Life, Frailty, and Mortality in Elderly. PLoS ONE. 2010;5(1):1-11.
63. Hackstaff L. Factors associated with frailty in chronically ill older adults. Soc Work Health
Care. 2009;48(8):798-811. Epub 2010/02/26.
64. Goldfield N. Strategies to decrease the rate of preventable readmission to hospital. CMAJ.
2010;182(6):538-9. Epub 2010/03/10.
65. Kellett J. Hospital medicine (Part 2): what would improve acute hospital care? Eur J Intern
Med. 2009;20(5):465-9. Epub 2009/08/29.
66. Goldfield N, Lamb V, Manton K, Vertrees J. Standardize concepts, not tools for quality
improvement. J Ambul Care Manage. 2007;30(2):116-9. Epub 2007/05/15.
67. Boult C, Wieland GD. Comprehensive primary care for older patients with multiple chronic
conditions: "Nobody rushes you through". Jama. 2010;304(17):1936-43. Epub 2010/11/04.
68. Sylvia ML, Griswold M, Dunbar L, Boyd CM, Park M, Boult C. Guided care: cost and utilization
outcomes in a pilot study. Dis Manag. 2008;11(1):29-36. Epub 2008/02/19.
69. Famadas JC, Frick KD, Haydar ZR, Nicewander D, Ballard D, Boult C. The effects of
interdisciplinary outpatient geriatrics on the use, costs and quality of health services in the fee-for-
service environment. Aging Clin Exp Res. 2008;20(6):556-61. Epub 2009/01/31.
70. Boongird C, Thamakaison S, Krairit O. Impact of a geriatric assessment clinic on
organizational interventions in primary health-care facilities at a university hospital. Geriatr Gerontol
Int. 2010. Epub 2010/12/15.
71. Arbaje AI, Maron DD, Yu Q, Wendel VI, Tanner E, Boult C, et al. The geriatric floating
interdisciplinary transition team. J Am Geriatr Soc. 2010;58(2):364-70. Epub 2010/04/08.
72. Vedel I, De Stampa M, Bergman H, Ankri J, Cassou B, Mauriat C, et al. A novel model of
integrated care for the elderly: COPA, Coordination of Professional Care for the Elderly. Aging Clin
Exp Res. 2009;21(6):414-23. Epub 2010/02/16.
73. Pearl G. Lee CCCB. The Co-Occurrence of Chronic Diseases and Geriatric Syndromes: The
Health and Retirement Study. Journal of the American Geriatrics Society. 2009;9999(9999).
74. Bayliss EA, Ellis JL, Steiner JF. Seniors' self-reported multimorbidity captured biopsychosocial
factors not incorporated into two other data-based morbidity measures. J Clin Epidemiol.
2009;62(5):550-7 e1. Epub 2008/09/02.
75. Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self-management of chronic disease
in primary care. Journal of the American Medical Association. 2002;288(19):2469 - 75.


76. Bolton CE, Waters CS, Peirce S, Elwyn G. Insufficient evidence of benefit: a systematic review
of home telemonitoring for COPD. Journal of Evaluation in Clinical Practice. 2010:no-no.
77. Novakovic C, Zucca F, Rauchhaus M. In response to: insufficient evidence of benefit: a
systematic review of home telemonitoring for COPD. Journal of Evaluation in Clinical Practice.
2011:no-no.
78. Lewis KE, Annandale JA, Warm DL, Rees SE, Hurlin C, Blyth H, et al. Does Home
Telemonitoring after Pulmonary Rehabilitation Reduce Healthcare Use in Optimized COPD? A Pilot
Randomized Trial. COPD: Journal of Chronic Obstructive Pulmonary Disease. 2010;7(1):44-50.
79. McCall N, Cromwell J, Smith K, Urato C. Evaluation Of Medicare Care Management For High
Cost Beneficiaries (CMHCB) Demonstration: The Health Buddy® Consortium (HBC) Centers for
Medicare & Medicaid Services, Office of Research, Development, and Information, 7500 Security
Boulevard, Baltimore, MD 21244-1850, 2011.

80. Oeseburg B, Wynia K, Middel B, Reijneveld SA. Effects of case management for frail older
people or those with chronic illness: a systematic review. Nurs Res. 2009;58(3):201-10. Epub
2009/05/19.
81. Fiorentini G, Iezzi E, Lippi Bruni M, Ugolini C. Incentives in primary care and their impact on
potentially avoidable hospital admissions. Eur J Health Econ. 2010. Epub 2010/04/29.
82. Ryan AM. Effects of the Premier Hospital Quality Incentive Demonstration on Medicare
patient mortality and cost. Health Serv Res. 2009;44(3):821-42. Epub 2009/08/14.
83. Leitman IM, Levin R, Lipp MJ, Sivaprasad L, Karalakulasingam CJ, Bernard DS, et al. Quality
and financial outcomes from gainsharing for inpatient admissions: a three-year experience. J Hosp
Med. 2010;5(9):501-7. Epub 2010/08/19.
84. Rula EY, Pope JE, Stone RE. A Review of Healthways' Medicare Health Support Program and
Final Results for Two Cohorts. Population Health Management. 2011;14(S1):S-3-S-10.
85. Reiffenstuhl G, Staudach A, Labacher K. [Analysis of perinatal mortality and its
consequences]. Zentralbl Gynakol. 1982;104(12):705-18. Epub 1982/01/01. Analyse der perinatalen
Mortalitat und Konsequenzen.
86. Trunet P, Le Gall JR, Lhoste F, Regnier B, Saillard Y, Carlet J, et al. The role of iatrogenic
disease in admissions to intensive care. Jama. 1980;244(23):2617-20. Epub 1980/12/12.
87. Goodwin N. The state of telehealth and telecare in the UK2010.
88. Sheaff R, Boaden R, Sargent P, Pickard S, Gravelle H, Parker S, et al. Impacts of case
management for frail elderly people: a qualitative study. J Health Serv Res Policy. 2009;14(2):88-95.
Epub 2009/03/21.
89. Gravelle H, Dusheiko M, Sheaff R, Sargent P, Boaden R, Pickar S, et al. Impact of case
management (Evercare) on frail elderly patients: controlled before and after analysis of quantitative
outcome data. BMJ. 2007;334:31 - 4.
90. Boustani MA, Munger S, Gulati R, Vogel M, Beck RA, Callahan CM. Selecting a change and
evaluating its impact on the performance of a complex adaptive health care delivery system. Clin
Interv Aging. 2010;5:141-8. Epub 2010/06/03.
91. Martin C, Sturmberg J. Complex adaptive chronic care. J Eval Clin Pract. 2009;15(3):571-7.
Epub 2009/06/16.
92. Ham C, Sing D. Improving Care for People with long-term conditions - A review of UK and
international frameworks. improvingchroniccare.org; 2006 [cited 2011 29/11/2011]; Available from:
http://www.improvingchroniccare.org/downloads/review_of_international_frameworks__chris_ha
mm.pdf.
93. Martin CM. Chronic disease and illness care: Adding principles of family medicine to address
ongoing health system redesign. Can Fam Physician. 2007;53(12):2086-91.
94. Martin CM, Kaufman T. Addressing health inequities: A case for implementing primary
health care. Can Fam Physician. 2008;54(11):1515-7.


95. Rogers A, Lee V, Kennedy A. Continuity and change? Exploring reactions to a guided self-
management intervention in a randomised controlled trial for IBS with reference to prior experience
of managing a long term condition. Trials. 2007;8:6. Epub 2007/02/24.
96. Martin CM, Peterson C. The social construction of chronicity--a key to understanding chronic
care transformations. J Eval Clin Pract. 2009;15(3):578-85. Epub 2009/06/16.
97. Boult C, Counsell SR, Leipzig RM, Berenson RA. The urgency of preparing primary care
physicians to care for older people with chronic illnesses. Health Aff (Millwood). 2010;29(5):811-8.
Epub 2010/05/05.
98. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic
illness: the chronic care model, Part 2. Jama. 2002;288(15):1909 - 14.
99. De Jong MJ, Chung ML, Wu JR, Riegel B, Rayens MK, Moser DK. Linkages between anxiety
and outcomes in heart failure. Heart Lung. 2011;40(5):393-404. Epub 2011/04/02.
100. Biddiss E, Brownsell S, Hawley MS. Predicting need for intervention in individuals with
congestive heart failure using a home-based telecare system. J Telemed Telecare. 2009;15(5):226-
31. Epub 2009/07/11.
101. Marsland S, Buchan I. Clinical quality needs complex adaptive systems and machine learning.
Stud Health Technol Inform. 2004;107(Pt 1):644-7. Epub 2004/09/14.
102. Martin CM, Biswas R, Sturmberg JP, Topps D, Ellaway R, Smith K. Patient Journey Record
Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions: A Developmental Framework.
In: Biswas R, Martin CM, editors. User-Driven Healthcare and Narrative Medicine: Utilizing
Collaborative Social Networks and Technologies. Hershey PA: IGI Global; 2011. p. 93-112.
103. Martin CM, Grady D, Deaconking S, McMahon C, Zarabzadeh A, O'Shea B. Complex adaptive
chronic care - typologies of patient journey: a case study. J Eval Clin Pract. 2011;17(3):520-4. Epub
2011/04/19.
104. Martin C, Grady D. Iterim Report of Phase 1 and Phase 2 of the Patient Journey Record
System. 2011.


The Patient Journey Record System (PaJR) Interim Report

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (7)

Similar to The Patient Journey Record System (PaJR) Interim Report

Similar to The Patient Journey Record System (PaJR) Interim Report (20)

More from Enda Madden

More from Enda Madden (10)

Recently uploaded

Recently uploaded (20)

The Patient Journey Record System (PaJR) Interim Report