Principles for Health System Capacity Planning: Insights for Healthcare Leaders

2017 ◽  
Vol 19 (4) ◽  
pp. 17-22 ◽  
Author(s):  
James Shaw ◽  
Ivy Wong ◽  
Bailey Griffin ◽  
Michael Robertson ◽  
R. Bhatia
Keyword(s):  
Health System ◽  
Capacity Planning ◽  
System Capacity ◽  
Healthcare Leaders

scholarly journals Prioritising surgical cases deferred by the COVID-19 pandemic: an ethics-inspired algorithmic framework for health leaders

BMJ Leader ◽  
2021 ◽  
pp. leader-2020-000343
Author(s):  
Amit Jain ◽  
Tinglong Dai ◽  
Christopher G Myers ◽  
Punya Jain ◽  
Shruti Aggarwal
Keyword(s):  
Decision Analysis ◽  
Health System ◽  
System Capacity ◽  
Surgical Case ◽  
The World ◽  
The Face ◽  
Algorithmic Framework ◽  
Limited Health ◽  
Healthcare Leaders

Elective surgical suspension during the COVID-19 pandemic resulted in a sizeable surgical case backlog throughout the world. As we ramp back up, how do we decide which cases take priority? Potential future waves (or a future pandemic) may lead to additional surgical shutdown and subsequent reopening. Deciding which cases to prioritise in the face of limited health system capacity has emerged as a new challenge for healthcare leaders. Here we present an ethically grounded and operationally efficient surgical prioritisation framework for healthcare leaders and practitioners, drawing insights from decision analysis and organisational sciences.

scholarly journals Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening

2020 ◽  
Vol 27 (7) ◽  
pp. 1026-1131 ◽  
Author(s):  
Sehj Kashyap ◽  
Saurabh Gombar ◽  
Steve Yadlowsky ◽  
Alison Callahan ◽  
Jason Fries ◽  
...  
Keyword(s):  
Health System ◽  
Health Systems ◽  
Capacity Planning ◽  
Age Distribution ◽  
Hospitalization Rate ◽  
System Capacity ◽  
Rt Pcr ◽  
Younger Patients ◽  
Hospitalization Rates ◽  
The Impact

Abstract Objective Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and Methods 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020. Results We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems. Conclusion Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.

scholarly journals Measure what matters: counts of hospitalized patients are a better metric for health system capacity planning for a reopening

2020 ◽  
Author(s):  
Sehj Kashyap ◽  
Saurabh Gombar ◽  
Steve Yadlowsky ◽  
Alison Callahan ◽  
Jason Fries ◽  
...  
Keyword(s):  
Health System ◽  
Health Systems ◽  
Capacity Planning ◽  
Age Distribution ◽  
Hospitalization Rate ◽  
Positive Case ◽  
System Capacity ◽  
Younger Patients ◽  
Hospitalization Rates ◽  
The Impact

AbstractResponding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order.We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems.Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.

scholarly journals Description of a Multi-faceted COVID-19 Pandemic Physician Workforce Plan at a Multi-site Academic Health System

2021 ◽  
Author(s):  
Sachin R. Pendharkar ◽  
Evan Minty ◽  
Caley B. Shukalek ◽  
Brendan Kerr ◽  
Paul MacMullan ◽  
...  
Keyword(s):  
Health System ◽  
Acute Care ◽  
Care Physician ◽  
System Capacity ◽  
Medical Emergency ◽  
Physician Workforce ◽  
Future Health ◽  
Academic Health ◽  
Response Planning ◽  
Implementation Period

Abstract Background The evolving COVID-19 pandemic has and continues to present a threat to health system capacity. Rapidly expanding an existing acute care physician workforce is critical to pandemic response planning in large urban academic health systems. Intervention The Medical Emergency-Pandemic Operations Command (MEOC)—a multi-specialty team of physicians, operational leaders, and support staff within an academic Department of Medicine in Calgary, Canada—partnered with its provincial health system to rapidly develop a comprehensive, scalable pandemic physician workforce plan for non-ventilated inpatients with COVID-19 across multiple hospitals. The MEOC Pandemic Plan comprised seven components, each with unique structure and processes. Methods In this manuscript, we describe MEOC’s Pandemic Plan that was designed and implemented from March to May 2020 and re-escalated in October 2020. We report on the plan’s structure and process, early implementation outcomes, and unforeseen challenges. Data sources included MEOC documents, health system, public health, and physician engagement implementation data. Key Results From March 5 to October 26, 2020, 427 patients were admitted to COVID-19 units in Calgary hospitals. In the initial implementation period (March–May 2020), MEOC communications reached over 2500 physicians, leading to 1446 physicians volunteering to provide care on COVID-19 units. Of these, 234 physicians signed up for hospital shifts, and 227 physicians received in-person personal protective equipment simulation training. Ninety-three physicians were deployed on COVID-19 units at four large acute care hospitals. The resurgence of cases in September 2020 has prompted re-escalation including re-activation of COVID-19 units. Conclusions MEOC leveraged an academic health system partnership to rapidly design, implement, and refine a comprehensive, scalable COVID-19 acute care physician workforce plan whose components are readily applicable across jurisdictions or healthcare crises. This description may guide other institutions responding to COVID-19 and future health emergencies.

scholarly journals HCFMUSP: Resilience in Response to the COVID-19 Pandemic

2021 ◽  
Vol 25 (spe) ◽  
Author(s):  
Marcelo Caldeira Pedroso ◽  
João Teixeira Pires ◽  
Ana Maria Malik ◽  
Antonio José Rodrigues Pereira
Keyword(s):  
Knowledge Management ◽  
Health System ◽  
Crisis Management ◽  
System Capacity ◽  
New Disease ◽  
Healthcare Operations ◽  
Teaching Case ◽  
Health System Governance ◽  
Emergency Actions ◽  
The Impact

ABSTRACT The teaching case describes a set of emergency actions taken by HCFMUSP to manage the needs brought by the COVID-19 pandemic in Brazil. The case objective considers the issues related to the impact of the pandemic mostly in healthcare operations, emphasizing how to: (a) adapt health system governance in response to a crisis (crisis management); (b) manage the health system capacity, which traditionally is not so resilient; (c) deal with a new disease (knowledge management). Thus, it should allow gathering elements for the management of future crises.

Who Counts Where? COVID-19 Surveillance in Federal Countries

2021 ◽  
Author(s):  
Philip Rocco ◽  
Jessica A. J. Rich ◽  
Katarzyna Klasa ◽  
Kenneth A. Dubin ◽  
Daniel Béland
Keyword(s):  
Public Health ◽  
Data Quality ◽  
Health System ◽  
Case Studies ◽  
Fiscal Decentralization ◽  
Public Health System ◽  
System Capacity ◽  
Democratic Institutions ◽  
Subnational Governments

Abstract Context: While the World Health Organization (WHO) has established guidance on COVID-19 surveillance, little is known about implementation of these guidelines in federations, which fragment authority across multiple levels of government. This study examines how subnational governments in federal democracies collect and report data on COVID-19 cases and mortality associated with COVID-19. Methods: We collected data from subnational government websites in 15 federal democracies to construct indices of COVID-19 data quality. Using bivariate and multivariate regression, we analyzed the relationship between these indices and indicators of state capacity, the decentralization of resources and authority, and the quality of democratic institutions. We supplement these quantitative analyses with qualitative case studies of subnational COVID-19 data in Brazil, Spain, and the United States. Findings: Subnational governments in federations vary in their collection of data on COVID-19 mortality, testing, hospitalization, and demographics. There are statistically significant associations (p<0.05) between subnational data quality and key indicators of public health system capacity, fiscal decentralization, and the quality of democratic institutions. Case studies illustrate the importance of both governmental and civil-society institutions that foster accountability. Conclusions: The quality of subnational COVID-19 surveillance data in federations depends in part on public health system capacity, fiscal decentralization, and the quality of democracy.

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