Patient Journey
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Patient Journey



Interim report of phase 1 and phase 2 trials

Interim report of phase 1 and phase 2 trials



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Patient Journey Patient Journey Document Transcript

  • PAJRThe Patient JourneyRecord 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 11/29/2011The Patient Journey Record System (PaJR) is an innovative care pathway with an expanded Case Managementmodel utilizing lay Care Guides working with a case manager to support people with chronic illness in at risktrajectories 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 ofadmission risk based on frequent short phone conversations. Lay care guides, remotely monitors patients withchronic illness or frailty through daily or, as needed, health-related phone conversations about their healthand well-being in biopsychosocial and environmental contexts and health and social care. The systemtransmits the conversations for analysis, using software that organises the data and predicts next day healthand unplanned service utilisation. The system allows the care guides and clinical supervisors to quicklypinpoint health issues and respond accordingly, either by contacting the patient (to offer care instructionsand/or self-care education) or his or her GP or directing the patient to services). PaJR has an action researchbased adaptive learning, development and evaluation. Phase 1 compared hospital admissions and services intwo cohorts from KDOC. Phase 2 is a regional demonstration clinical trial in 2 sites – Nenagh, Tipperary andCastlebar, Mayo. PaJR is now monitoring 132 patients and 42 controls with 3 FTE care guides. To dateunplanned admission rates are approximately 3 times higher in the control group. We are midway throughPhase 2 and ongoing patient recruitment and on-going data collection is in progress. We have estimatedpotential savings from reduced admissions that could be shifted to more appropriate services in thecommunity and still potentially make savings for the HSE and shift care from unplanned hospital care toplanned hospital and more timely community care. Phase 3 is being planned with randomised controlled trialsof hospital readmission prevention and community based preventable admission avoidance. © PBOC Limited 2011 – Not for Distribution
  • ContentsPatient Journey Record (PaJR): Monitoring Chronically Ill Patients via Phone Calls to reducepotentially avoidable hospitalisations. ................................................................................................ 3Introduction ............................................................................................................................................ 3Literature ................................................................................................................................................ 4The conceptual framework for the PaJR project .................................................................................... 7 Complex adaptive systems theory and resource use ......................................................................... 7Description 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 ................................................................................................................ 12Results ................................................................................................................................................... 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 ................................................................................................ 17Conclusion ............................................................................................................................................. 18 Next Steps – Phase 3 ......................................................................................................................... 19Appendix 1 ............................................................................................................................................ 20Letters of Satisfaction and Support ...................................................................................................... 20KDOC Patient Satisfaction Survey Feedback Report ............................................................................. 23References ............................................................................................................................................ 26 © PBOC Limited 2011 – Not for Distribution
  • Patient Journey Record (PaJR): Monitoring Chronically Ill Patients viaPhone Calls to reduce potentially avoidable hospitalisations.IntroductionPaJR comprises an innovative care pathway with an expanded Case Management model utilizing layCare Guides working with a case manager to support people with chronic illness in at risk trajectoriesto avoid unplanned hospitalisations. Care Guides substitute for many tasks of case management,expanding the reach of case managers and enhancing their efficiencies with innovative predictivemodelling based on frequent short phone conversations.PaJR is a person-centred patient journey monitoring system to improve quality of life and reduceavoidable hospitalisations. It incorporates observations of daily living and machine learning ofsentiment 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 riskof readmission. It aims to detect health risks or deterioration earlier than currently happens bycloser monitoring, ongoing predictive modelling and faster information transfer to the GP, andrelevant health or social care providers.The PaJR study commenced in August 2009 with the development of a conceptual framework andoperational framework for a patient journey through chronic illness supported by informationtechnology.(1)PaJR then received 1 year funding as a translational research project funded by theNational Digital Research Centre.It is anticipated that a mature system for large numbers of patientswill be developed in year 2-3 by the Trinity College Dublin Campus Company PBOC Limited (CarmelMartin, Carl Vogel; Kevin Smith; Lucy Hederman, Enda and Brendan Madden and Trinity CollegeDublin) that formed as an outcome of the National Digital Research Centre funding.This is a report of the first 12months activity.Patient PopulationVulnerable Populations: Older Patients with Chronic Illness and Multi-morbidityProblem AddressedIf not monitored closely, chronically ill individuals may decompensate in any one of multipledomains in their personal health environment. Decompensation may lead to the need for expensiveinpatient care. Although ongoing monitoring of these individuals, especially older ones, may preventsome of these complications, relatively few health systems have the capacity to provide suchservices to date and involve expensive tale-health equipment in the home with the costs ofmaintaining the equipment.Cycle of hospitalisationsMany patients with chronic illnesses require frequent hospitalisations to deal with exacerbations orcomplications associated with their condition(s). Unrealised benefits of monitoring of chronically ill © PBOC Limited 2011 – Not for Distribution
  • individuals, especially older ones, can help to prevent many exacerbations and complications, thusreducing 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 careFigure 1 PaJR Concept“Chronic diseases such as cardiovascular diseases, cancers, respiratory diseases and diabetes are aheterogeneous group that share underlying lifestyle and societal causes which need to be addressedby political, fiscal and legal mechanisms as well as at the level of the individual”. UN GeneralAssembly, September 2010LiteratureAvoidable hospitalisations in older people with chronic conditions are the subject of considerableinterest to decision makers(2), because such admissions are deemed to be expensive, unhelpful topatients and reflect underperformance of health systems organisation.(3-6) There exist considerablevariations in terminology used to describe models and components of models designed to reduceavoidable 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 summarisestheir multi-factorial nature - ‘a preventable readmission as an unintended and undesired subsequentpost-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). Ingeneral, most medical readmissions for sub-acute or chronic conditions are potentiallypreventable.(12)(13)Who is at risk?Which patients are at risk and what interventions are successful in different settings andcommunities to reduce avoidable admissions?(14-22) Factors predicting avoidable hospitalisationsare 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 © PBOC Limited 2011 – Not for Distribution
  • risks of hospitalisations. Overlap among hospital and community programs(7) is demonstrated inprolonged post-discharge programs by the work of Courtney,(35) and others (34, 36). Communitybased risk prediction scores include self assessed health, support, psychosocial and environmentalissues and disease factors(37) while hospital predictions focus on diagnostic groups(9), diseaseseverity, length of stay and physiological variables.(37, 38) Some groups have pioneered use of bothhospital and GP databases(15, 39) . A well developed and internationally validated score in theProbability of Repeat Admission (Pra) Risk Score.(40)Tools to help identify people at high risk of future emergency admission include computer databasemodels and simple questionnaires.(15, 41)(42) However, ongoing research is needed to understandwhat are causal relationships among the descriptors, multiple associations and correlations found inthe 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 withregression 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. Casefinding has used threshold modelling from hospital data, such as repeated emergency admissions, asa marker of a high risk of future admissions. However, admission rates and bed use among high-riskpatients fall to the mean rate for older people(44) or have unpredictable rises with many admissionsfrom lower risk groups(40). Alternatives, such as identifying patients at high risk through aquestionnaire administered by a GP practice,(46) do not take account of trajectories in individualjourneys unless repeated regularly. (43)Predictive modelling of data to calculate the risk of future admission may be the best availabletechnique, but requires ongoing access data to update risk profiles as populations and serviceschange 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 canbe 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 predictablein the every day fluctuations of individual patient care, although there are patterns associated withcancer, organ failure and frailty. (49)(50)An older persons perception of his or her own health is animportant predictor of this trajectory.(51)(52)Multiple studies confirm the predictive validity of SRH in older populations concerning futurehealth(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 thestrongest biological predictor of death.(58-60)The SRH question "would you say your health ingeneral is excellent, very good, good, fair, or poor?" appears to reflect inner tacit awareness of one’shealth journey, that is most meaningful to each individual,(61). The one measure of self-rated healthpredicts adverse health events,(47) initial hospitalisation and repeated hospitalisation, especially inpeople with heart failure. (56)A wide range of body functions, activities and personal factors are associated with levels of SRHamong community-dwelling older people. Some of these, such as physical capacity, depressivesymptoms and habitual physical activity are of particular interest due to their potential for change © PBOC Limited 2011 – Not for Distribution
  • through health promoting interventions.(54, 57) Increased frailty and chronic diseases are closelycorrelated with worse self-rated physical and mental health, and are associated with greater healthdisparities and worse neighbourhoods. (62)Interventions need to address self-related health andself-efficacy (63), but much of this may be linked to neighbourhoods where social and environmentalissues need to be addressed.(62)Averting avoidable hospitalisationsInterventions to increase the general health of elderly people and avert preventable conditionsinclude vaccination, falls prevention, nutrition and physical activity programs.(29) Interventions inprimary 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-makingprotocols on admission.(64, 65)A number of interventions across health care levels include quick response services, geriatric dayhospitals, comprehensive geriatric assessment, advanced health directives and coordinated care.(64,66)Guided care, case management, nurse specialist clinics and tele-home care are specific strategieswhich have been found to be successful in reducing avoidable hospitalisations. (67-74)Self-monitoring and self-management is a key element of disease management preventinghospitalisations.(75) Tele-monitoring in chronic disease’s impact on hospital admissions and costsremain controversial, (31, 76, 77) but has been shown to improve quality of care.(54) Ongoingeffectiveness studies and data from functioning health systems may clarify the impact of differentcomponents/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 needsof people who are at high risk of admission or readmission.(15, 22, 30, 80) Pay for performancerelated to avoidable admissions is taking effect in the US and the UK, yet the feasibility andeffectiveness of pay for performance is not proven.(81-83) Evaluations of the impact of complexsystem 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 reduceavoidable hospitalisations remains unclear(88, 89) often because the interventions are complex andadaptive 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 andvariations lead by Kaiser Permanente, Evercare, Pfizer, PACE and other variations includingHealthways.These models show positive findings in particular US settings (92).(84). However, it isunclear 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 © PBOC Limited 2011 – Not for Distribution
  • 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 projectThe patient journey concept recognizes that hospital admissions take place in journeys throughstages of health and illness,(93) which are strongly influenced by the social and non-socialdeterminants of health. (94) The individual patient journey is shaped by their biological state anddisease process, and their health care, social and environmental milieu. The need for hospitalisationis strongly linked to feeling ill and whether one has supportive care at a personal level.(43) Self careand the work of managing the illness increasingly requires informational and practical support asillness become unstable.(95, 96) The general practitioner and the primary care team have alongitudinal journey with their patient through phases of health and illness, stages of care includinghealth 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 seekhelp in a timely and as needed basis. In addition PaJR creates more directed support andrecommendations through real-time monitoring and intervening where necessary.Complex adaptive systems theory and resource useThe patient journey represents a complex adaptive system that requires real-time monitoring toidentify deteriorations and improvements. Services are directed to address fluctuations in healthand health concerns when they are needed. Frailty is a concept of an aging human with diminishedfunctional reserve in the whole body system that includes the internal milieu of body organs andsystems and the external physical, personal and social environment. Deterioration in any of theseareas 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 createpositive 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 ofpatients can be responded to by the primary care professionals in anticipation of deterioration withmore immediacy and efficiency. © PBOC Limited 2011 – Not for Distribution
  • At a primary care systems level PaJR facilitates connections among team members. It sits betweendisease management and case management, being a holistic primary care service. It has thepotential to monitor and support patient through different care phases from chronic illness to end oflife and hospice or end of life care.Action research, machine learning and clinical learning continually improve the system. As numbergrow, predictions of deteriorations and interventions that work are continually refined and PaJRadapts 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. © PBOC Limited 2011 – Not for Distribution
  • 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 TypesThe semi-structured survey encompasses a range of open ended and closed questions based on keynarratives designed to pick up on deterioration in health of patients with chronic conditions.Thenarratives 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 thecall 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?” © PBOC Limited 2011 – Not for Distribution
  • Figure 3. Care Guides Data Entry System into the PaJR database.Intervention Types – triggered by PaJR AlertsInterventions are made in health care, social care, environmental and health promotion areas wherepossible when alerts are designated to a narrative type by machine learning. Social andenvironmental interventions depend greatly on the services available in the location in which PaJR isbased. 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). © PBOC Limited 2011 – Not for Distribution
  • 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-managementOther 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 SystemKey considerations in the PaJR system are the design and implementation of robust expertknowledge and data support systems that incorporate text analysis, machine learning and predictivemodelling developed by Dr Carl Vogel and his team. Care guides record a call by following an onlineclinically derived questionnaire. The analytic engine immediately decides the traffic light category ofthe 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 ruleor algorithm.(101) The analytics engine allows identification of features that emerge as predictive ofdeterioration. It has “perfect memory” and allows high accuracy, high volume at low cost. PaJRanalytics 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 statusbased on patient responses to caller questioning. We apply machine learning methods to predictpatient status in the near future. In this study, three target statuses are of interest: next urgentunplanned event (NUUE), next unplanned event (NUE) and next self-rated health (nextSRH). Patientbaseline records and daily online interview logs deliver rich information about patients currentstatus. We extract linguistic and meta-linguistic features together with current patient status, inorder to train prediction models. To predict the binary value of NUUE we use a decision tree basedon a highly refined set of features organised hierarchically as rules. © PBOC Limited 2011 – Not for Distribution
  • Efforts are made to minimise false negative predictions. Currently the system covers 23 out of 27NUUE cases in 1571 patient interviews with the cost of 453 false positive predictions. A false positiveprediction might trigger a phone call or visit, but its cost is much less than a false negativeprediction, where a true danger is overlooked. The patient status prediction system is constructed intwo phases, and it responds to requests in nearly real time. The two phases are: offline trainingmodule and online prediction module. The offline training module utilises the newest patientinterviews and re-trains decision models within a few hours, while the online prediction module runsover the latest successful model, and it takes only moments to deliver prediction results.Progress-to-DatePaJR has been piloted in 3 locations, a GP Out-of-Hours service, a community setting and hospitalbased settings. Phase 1 has been completed. The theory and concepts1 and high level results havebeen reported.(102, 103) We are now rolling out Phase 2 in several regional locations. Phase 3approach and protocols are being developed. For larger pragmatic demonstration, a randomizedcontrolled is planned and we are seeking further funding.Recruitment of patientsRecruitment processes for patients differs depending on the setting.Hospital based setting recruitment – e.g. Nenagh hospitalFor PaJR hospital based settings, patients meeting the criteria are recruited before discharge fromhospital 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 patientsresiding in nursing homes from taking part. GPs are informed in advance of the system and asked toparticipate in orderthat their patients would be selected if suitable. The call guide carries out thebaseline interview with the patient while in hospital and begins the daily phone calls on day 2 ofdischarge from hospital. The patient (or their caregiver) receives 5 phone calls during the first 5week days and a subsequent number of calls for a 28 day period. After the first 5 calls, the frequencyof calls for that patient is determined according to the number of flag alerts generated by thatpatient during calls, their reported self-rated health and also the number of calls the patient of theircare giver would like to receive.Community based setting recruitment – e.g. Castlebar, County MayoPaJR community based setting varies in terms of recruitment. GPs are contacted about PaJR andrecruited to take part. GPs select 10 of their patients suitable for PaJR to take part. The GP thencontacts the patient and informs them about PaJR, gets their verbal consent to take part and givesthe patient’s contact details to the PaJR team. The call guide then contacts the patient and carriesout 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 atotal of 28 days.Control PatientsTo show that the PaJR system effectively works to reduce avoidable hospital admissions, a cohort ofcontrol 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 baselineinterview with the call guide. The control patients are followed up on after 28 days and asked aboutany hospitalisations or visits to out-of-hours services since their initial interview. GPs are aware thatsome of their patients will be chosen as control patients. The control patients are followed up every28 days for a period of 6 months. They are then given the option to go into the PaJR system as an © PBOC Limited 2011 – Not for Distribution
  • intervention patient if they wish to do so.In total, PaJR is monitoring 170 patients with 3 full-timeoperatives in the current pilots.ResultsPhase 1 – KDOCOut-of-Hours Service, Naas, Kildare.In October 2010, the PaJR system pilot in Kildare Doctors on Call (KDOC) identified, from their calldatabase, all people over 18 who met PaJR criteria for chronic illness in a three month period whowere referred to A&E or transferred to hospital or had an out of hours home visit who were suitablefor 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 healthevents.(103) Care guides made outbound phone calls to 129 people 1 to 5 times per week for up to12 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 predictivemodelling and rule based alerts in key domains prompted illness, healthcare, medication, social andenvironmental interventions by care guides under the supervision of a clinical nurse. The alerts werepredominantly for prompt GP care, but a substantial number were for interventions with respect topharmacists, 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 repeatadmissions, have been monitored for 12 months plus. In an initial control cohort 1, there were nointerventions in 12 patients over 1 month with an admission rate of 43%2. In cohort 2, 46 patientsrecruited in the same manner from the same GP practices in the same season, have been monitoredwith the PaJR system with interventions and admissions tracked. Admissions per month steadilydecreased 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 triggeredas a result of the PaJR alerts.Table 1. Phase 1 Intervention and Control cohorts consecutively selected from Kildare and County West Wicklow GP Outof Hours data base.KDOC Kildare Cohort 1 Cohort 2 Cohort 2 Cohort 2out 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 daysof daysSRH 2.9 (fair-good) 1.8 (poor-fair) 1.8 (poor-fair) 1.8 (poor-fair)(self-ratedhealth)Care Giver 3 (25%) 6 (13%) 6 (13%) 6 (13%)presentHospital 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 3.5visits month per person month per person months per person months per person © PBOC Limited 2011 – Not for Distribution
  • Phase 2– Nenagh Hospital, Tipperary and County Mayo.Nenagh hospital, TipperaryReadmission avoidance program – post hospital discharge started June 6th, 2011 with 1 part-timeoperative using PaJR software system and workflow.County Mayo and Mayo General, Castlebar hospital, MayoAdmission avoidance program started in September, 2011.Overview of PaJR callsBetweenNovember 2010 and November 2011, the PaJR team has monitored 42 control patients and132 intervention patients in the three locations of Kildare and County West Wicklow, Tipperary andCounty Mayo. Their characteristics are described in Table 1.Table 2. Participants Baseline Characteristics based on Pra Score(40). Selection criteria were:all Doctor visitspast 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 InterventionNumber (175) 42 132Age (74) 73 75Self-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 threelocations of Kildare and County West Wicklow, Tipperary and County Mayo. Some key findingsinclude 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 dayTable 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 © PBOC Limited 2011 – Not for Distribution
  • These interventions were recorded by Care Guides and a process of validation with audio-taped callswill 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 TotalGP visits 106 638 744OPD Specialist 20 336 356Visit to a Casualty or Emergency 18 0 18DepartmentHospital admission 38 26* 64***Admissions which were planned such as for surgical procedures or investigations or did not include an overnight stay inhospital are included. **Hospital readmissions that took place within 24 hours of discharge were not included in numbers.Service UseOver 12 months 132 patients ( 320 person months) had 64 admissions in total of which 38 wereunplanned. On average, there were 5 admissions per month and 3 unplanned admissions permonth.Admissions which were planned such as for surgical procedures or investigations or did notinclude an overnight stay in hospital are included. Hospital readmissions that took place within 24hours of discharge were not included in these numbers.Respite care was reported in28/1500 calls; rehabilitationwas reported in 7/1500 calls wherequestion was asked.GP Home visits of an unplanned nature were reported in 42 calls and planned home visits werereported 181/1500 calls where question was asked.Similarly home visits were reportedfromPrimary care team (usually public health nurse) in 226/1500calls 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 wasreported by patients, caregivers and GPs. All KDOC GPs have now signed up for the Naas hospitaltrial. (See Appendix 1 for letters of satisfaction from the pilot sites and patient initial feedback)Control GroupControl group statistics are identified through monthly phone calls to the controls to ascertain theirservice 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 stayin hospital were not included. Nor were hospital readmissions that took place within 24 hours ofdischarge included in the readmission statistics. At least two patients felt that their care and supportwere suboptimal. See Table 4 © PBOC Limited 2011 – Not for Distribution
  • Q3 Since PaJRs 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 johns 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 admissionsTable 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 InterventionNumber (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 MayoThere are around 18,000 older citizens in County Mayo, of who 5% approximately 800 are at veryhigh risk of hospital admission at any one time. Each elderly person over 75 has almost 1 admissionper year, for more than 12 days, thus the 800 would have at least 1 hospital admission per year andlikely 2-3. (Public Health Information System, 2008) Reducing hospitalisations by 50% in this group isfeasible according to our pilot studies. The cost of each admission for an older person for 10 days is900 Euros direct costs and 1000 Euros if indirect costs such as ambulances and time spent in the A&E © PBOC Limited 2011 – Not for Distribution
  • are considered, which could be reduced from 800 to 400 would save 4million Euros potentially.Itwould take 8 Care Guides with full-time nurse supervisor working on the phones for 5 hours per dayto cover this population in the following manner: 2-3 minutes for ‘no problem’ calls (50%); 3-5minutes (25%) for complicated and5 minutes plus (25%) problem complex calls. The estimated costfor the service per year is 300,000 Euros.Other cost savings such as reductions in emergency department visits and delays in nursing homeadmissions and respite care would likely ensure. Other costs such as increased community servicesmight arise but would be small compared to the potential savings. Overall outcomes were areduction of admissions by 50% in pilot studies. Impact was similar on emergency departmentattendances. Impact on nursing home and other services has not been calculated, but there is likelyto be a delay or reduction in such use. It has become apparent that a considerable proportion ofroutine visits are for social purposes(104) could be replaced by more timely visits to avertdeteriorations, if their time was freed up and PaJR could provide alerts. It would take 8 Care Guideswith part-time nurse supervisor and a full time manager working on the phones for 5 hours per dayto cover this population in the following manner: 2-3 minutes for ‘no problem’ calls (50%); 3-5minutes (25%) for complicated and 5 minutes plus (25%) problem complex calls. The estimated costfor 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 MayoOther Findings from patient cohortsThe average age of the patients recruited to date is 69.7 years. The patient population of thecombined cohorts to date is composed of 34.5% males and 65.5% females. Emerging findings andtrends among cohort populations include:Nenagh hospital patient cohortAs this is a hospital based setting, patients are being recruited before discharge from hospital andare reporting poorer self-rated health to those patient cohorts in KDOC and Mayo. Patients inNenagh are on average a sicker cohort of patients with the majority reporting cardiovascular, heartfailure and gastrointestinal illnesses.Mayo patient cohortDue to the large number of elderly people living in relative isolation across Co. Mayo, mental healthissues including depression and alcohol related problems are the most commonly reported problemsto date among the patient cohort in Mayo. Patients recruited to the system in Mayo meet thecriteria of having one or more chronic conditions and are on average a more aged patient cohort © PBOC Limited 2011 – Not for Distribution
  • with the average age being 74 years. GPs in Mayo are referring to the study many of their patientswith manageable chronic conditions e.g. as they feel that need for social support which the programoffers will benefit their patients greatly. While some of the patients recruited are availing of locallyrun social support services like the telephone befriending services, the majority are not. PaJR isproviding 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 thelocal befriending service.KDOC patient cohortThis cohort of patients has a more varied patient population ranging from patients in their 30’s withgastrointestinal problems to patients of 80+ years with cardiovascular problems. The range ofchronic conditions varies most among this cohort of patients with more cases of diabetes,hypertension, multiple sclerosis, Chron’s disease and other gastrointestinal conditions.Emergent FindingsHow does PaJR work? Early detection of deterioration and guiding self-referral to GPs for medicalassessment is a key mechanism. Addressing barriers to help seeking such as health anxiety, poormobility, social anxiety (fear of being a nuisance) by enablement and encouraging the person to seekhelp themselves where possible on identification of deterioration. Learning is fostered, such thatpeople and care givers more readily identify when things are deteriorating and have strategiesreinforced by PaJR feedback. Social contact, practical support and informational support areimportant for the isolated patients and particularly caregivers.Machine learning and predictive modelling are continually improving the accuracy of when to makecalls, identifying more narrative and quantitative features to predict unplanned admissions and visitsto the emergency department.PaJR is a learning system that is adaptive to different populations of patients and will continue toimprove.ConclusionThis report is a work in progress. Data collection is ongoing and statistics are changing daily as wecollect 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 320person/months for15 months since starting the project. We are continually expanding our scope andthe numbers that lay care guides can manage.We have estimated potential savings from reduced admissions that could be shifted to moreappropriate 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 ofHours and acute services. It has potential to reduce costs in the acute care sector and shift savings tocommunity and social care. Feasibility and acceptability have been demonstrated. It is nowimportant to conduct a randomised control trial with large enough sample size to detectimprovements in operational environments beyond pilots. © PBOC Limited 2011 – Not for Distribution
  • Next Steps – Phase 3We are in the process of conducting a randomised control of community based patients in Mayo andNenagh. We plan to conduct a randomised controlled trial of hospital readmission prevention inNaas 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. © PBOC Limited 2011 – Not for Distribution
  • Appendix 1Letters of Satisfaction and Support © PBOC Limited 2011 – Not for Distribution
  • Primary Care Development Officer Mayo Primary, Community & Continuing Care HSE West St. Mary’s Headquarters Castlebar County Mayo  (094) 9042509/(094) 9042019  (094) 9025957Re: Patient Record Journey System – HRB Research ApplicationAs 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 toextending their study to the Mayo Area. We invited Carmel, Deirdre and Dr. John Kellett to do a presentationfor relevant staff and Managers here in Mayo and we were immediately impressed with the strong evidencebase and professionalism of the Team.The study has now commenced here in Mayo, following the recruitment of two part time telephone supportpeople, who are based at the offices of the Castlebar Social Services. This also provides an ideal partnershipsetting for delivery of this model in Mayo. To date, the service has been embraced by all of our Primary CareTeams and approximately 15 GP practices in the County are participating in the study and have referredpatients. We are confident that the study will have a significant impact on the lives of 100 to 150 people in theCounty and that there will be significant learning and transferability from the findings of this study, to otherPrimary Care Settings throughout the Country. We very much support the funding application and lookforward to the further development of this service in the future.Signed:_Laurence Gaughan Laurence Gaughan Primary Care Development Officer th 6 October 2011 © PBOC Limited 2011 – Not for Distribution
  • © PBOC Limited 2011 – Not for Distribution
  • 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. © PBOC Limited 2011 – Not for Distribution
  • 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. © PBOC Limited 2011 – Not for Distribution
  • 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. © PBOC Limited 2011 – Not for Distribution
  • References1. Martin CM, Biswas R, Joshi A, Sturmberg J. Patient Journey Record Systems (PaJR): Thedevelopment 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 andTechnologies. Hershey PA USA: IGI Global; 2010.2. Fleming ST. Primary care, avoidable hospitalization, and outcomes of care: a literaturereview 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 PublicPolicy 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 medicaladmissions 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 HealthStatus 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 andMidwifery, Therapy Professions Committee, Quality and Clinical Care Directorate, Executive HS.Community medical services for the older person. Report of the National Acute MedicineProgramme2011.7. Agency for Healthcare Research and Quality. Chronic Care and Disease ManagementImproves Health, Reduces Costs for Patients With Multiple Chronic Conditions in an IntegratedHealth System. In: Department of Health and Human Services, editor. United States2009. p. 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 ofSpain. 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 readmissionin Hong Kong--system, clinician, patient or social factor? BMC Health Serv Res. 2010;10:311. Epub2010/11/18.11. 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, apopulation-based analysis. BMC Health Serv Res. 2005;5(1):13. Epub 2005/02/08.12. Martin M, Hin PY, ONeill D. Acute medical take or subacute-on-chronic medical take? Ir MedJ. 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]; Availablefrom: van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospitalreadmissions 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: hownonurgent 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 preventablereadmissions 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 adultsadmitted to the emergency department: a systematic review. Scand J Trauma Resusc Emerg Med.2010;18:8. Epub 2010/02/12. © PBOC Limited 2011 – Not for Distribution
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