Anticipatory care in potentially preventable hospitalizations

Making data sense of complex health journeys

Carmel M. Martin, Joachim P. Sturmberg, Keith Stockman, Narelle Hinkley, Donald Campbell

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Purpose: Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed. Application: PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia. Theory: A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as "particulars" in their unique trajectories. Evaluation: Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The -3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets-consistent reduction in acute bed days (20-25%) vs. target 10% and high levels of patient satisfaction. Discussion: Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical. Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.

Original languageEnglish
Article number376
Number of pages15
JournalFrontiers in Public Health
Volume6
Issue numberJAN
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes

Keywords

  • Anticipatory care
  • Complex adaptive systems
  • Data analytics
  • Data science
  • Frequent users
  • Health trajectory
  • Potentially preventable hospitalizations
  • Readmissions

Cite this

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title = "Anticipatory care in potentially preventable hospitalizations: Making data sense of complex health journeys",
abstract = "Purpose: Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed. Application: PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia. Theory: A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as {"}particulars{"} in their unique trajectories. Evaluation: Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The -3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets-consistent reduction in acute bed days (20-25{\%}) vs. target 10{\%} and high levels of patient satisfaction. Discussion: Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical. Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.",
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Anticipatory care in potentially preventable hospitalizations : Making data sense of complex health journeys. / Martin, Carmel M.; Sturmberg, Joachim P.; Stockman, Keith; Hinkley, Narelle; Campbell, Donald.

In: Frontiers in Public Health, Vol. 6, No. JAN, 376, 01.01.2019.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Anticipatory care in potentially preventable hospitalizations

T2 - Making data sense of complex health journeys

AU - Martin, Carmel M.

AU - Sturmberg, Joachim P.

AU - Stockman, Keith

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AU - Campbell, Donald

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KW - Data analytics

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KW - Potentially preventable hospitalizations

KW - Readmissions

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