Data is an increasingly important tool to improve city services. Among a city’s most challenging responsibilities is to prepare for, prevent, and respond to emergencies and crises. Several recent advances in technology have made it possible for cities to better manage their preparedness and response through improved public health and emergency medicine systems, critical examples of how data and analytics can be used to enhance city operations.
As public health increasingly enters the conversation about critical global challenges, new technologies have emerged to improve well-coordinated responses to emerging health crises. Globally, the case for much needed “epidemic intelligence” has become clear in recent years with the Ebola outbreak and even in recent months with the outbreak of the Zika virus, declared a public health emergency by the World Health Organization (WHO). For local authorities and decision makers, this type of intelligence includes real-time alert systems, rapid response surveillance, and mobile data collection and geolocation to improve the disease surveillance necessary to curb the spread of these types of major viruses.
Even in non-emergency situations, access to data can be an incredibly useful tool to help state and city officials understand the particular health challenges facing their communities. Health databases such as Healthy People 2020, from the Office of Disease Prevention and Health Promotion; Behavior Risk Factor Surveillance System (BRFSS), operated by the Centers for Disease Control; or County Health Rankings, a program of the Robert Wood Johnson Foundation and the University of Wisconsin, all help public health officials and city leaders to understand how their counties or cities compare against others with regard to key health indicators ranging from cancer rates to access to primary care. More localized databases include the Big Cities Health Inventory, organized by the National Association of County & City Health Officials with funding from the CDC and support from the Robert Wood Johnson Foundation and the de Beaumont Foundation, compiling data from 26 major American cities on indicators such as behavioral health, food safety, and HIV/AIDS rates. The availability of this type of data is critical to enable communities to perform needs assessments and determine specific areas of focus for evidence-based public health campaigns or interventions.
At the local level, many officials acknowledge the need to better monitor and understand health trends and threats in their communities, whether imminent or otherwise. FirstWatch is one example of a platform that enables city managers or public health departments to monitor multiple data sources (911, EMS, fire, law enforcement, and public health) simultaneously to track trends and predict potential public health issues. Given the sheer amount of information the platform relies on, a city may use FirstWatch for any issue requiring coordination from multiple offices, such as alerting authorities about a major sporting event where large crowds are expected.
More specifically related to public health, the platform can help in ways such as cluing leaders into an impending local flu outbreak by linking public health data with emergency room visits. Authorities in Reno, NV used FirstWatch’s tracking technology to bring agencies together to better prepare for expected drug related incidents and overdoses when a prescription drug ring was broken up by police, setting off a wave of preparations for medical professionals and first responders. In another example, authorities in Louisville, KY have leveraged digital health technology to address the city’s extremely high rates of asthma, among the worst in the nation. The AIR Louisville initiative brings together public and private partners to collect data from “smart inhalers” with sensors that map areas of inhaler use to measure air quality and enable authorities to focus on asthma-triggering areas and mitigate air quality in a more targeted fashion.
As public health, crime, and security are often closely linked in urban areas, it bears noting that improvements to real-time policing and security forces are an important component of fostering healthier, safer cities. With real-time policing, cities have begun creating data centers or dashboards that enable them to combine data sources (such as public health data) and to ensure a quicker response to violent events, better tracking of criminals, or even predictive policing to help prevent crime or violence.
Just as with public health, improvements to emergency medicine often revolve around connecting and sharing information. At a time when many leaders in the medical field have come to acknowledge the need for much improved trauma care in the United States, improvements to emergency medicine are welcome steps toward “zero preventable deaths,” a goal outlined in a recent report by the National Academies of Sciences, Engineering, and Medicine. Among these improvements include better use of two key components of first response: ambulances and dispatch data.
Enhancing the Power of the Ambulance
Ambulances are a critically important tool in emergency medicine, enabling first responders to arrive rapidly and well-equipped to the scene of an accident and successfully serve as an important bridge to critical hospital care. Ambulances have long been the sites of technology advancements, whether for communications between first responders and hospitals, vehicle safety improvements, or new technologies for fleet management and navigation. Recent improvements enable health teams, cities, and counties to respond to emergencies faster and more effectively.
In Louisville, KY, the Louisville Metro Emergency Medical Services (LMEMS) team analyzed the data in its Computer Aided Dispatch (CAD) system to monitor how emergency response staff spent time during emergency calls and to identify ways to minimize ambulance turnaround time between calls. In this case, the CAD system utilized real-time information on the teams’ whereabouts, allowing a supervisor to see which hospitals were routinely taking emergency crews longer and patterns in the frequency of demand in certain areas. By monitoring these patterns and adjusting accordingly, the emergency crews and ambulances were then able to be more efficiently dispatched depending on the neighborhood, hospital, or type of emergency.
In recent years, ambulances have frequently been equipped with wireless communication to enable first responders to share patient data with hospitals ahead of time through a “telemedicine system,” speeding up both the rate at which emergency department staff can receive patients and the turnaround time for the emergency response teams themselves. Although ambulances are typically responsible for submitting patient data directly to emergency rooms and hospitals, access to information about patient outcomes can be useful to emergency management teams as well, as it enables them to improve ambulance services and emergency health care delivery. London, UK’s London Ambulance Service, the busiest ambulance service in the world, has launched a Data Linkage project to focus on the “knowledge gap” between emergency response teams and hospitals by linking data to track diagnosis, health interventions, and mortality outcomes of patients brought to the hospital by emergency response teams. This knowledge gap is common for a number of reasons, from the logistical problems with creating new communication channels and the data security problems with protecting patient health records.
Leveraging Data Analytics for Emergency Medicine
In addition to the technologies that enable ambulances and hospitals to respond more quickly and effectively to emergencies, the data ambulances collect is also a useful source of information. 911 call data as well as patient data collected during the ambulance ride can be used in predictive analytics models, creating early-warning systems. This can allow hospitals to anticipate a higher number of visits, or help public health administrators better educate the public following noted trends. In Jersey City, the use of GIS-powered computer technology systems enabled the Jersey City Medical Center’s EMS team to reduce ambulance response times by nearly two minutes. Through the MARVLIS system, a visualized predictive model highlights areas on a map that are most likely to receive emergency calls on a given day or at a given time, thus allowing EMS teams to be strategically located and able to respond to emergencies more quickly.
Along with improving response times, data analytics can also help make health systems more efficient overall. In Memphis, TN, officials have been working to address the city’s high 911 call volume through an IBM Smarter Cities Challenge grant. Data analysis revealed areas with particularly high call volume and patterns of non-emergency concerns, prompting city officials to look for ways to introduce “paramedicine” or “telemedicine” that would allow individuals to speak with health care professionals without needing to be transported to the emergency room via ambulance, avoiding an exceedingly costly pattern.
Analyzing hospital records for “hot spotting” – when patients over-utilize emergency services rather than seeing a primary care physician for routine care, or are being inappropriately medicated because of a failure to coordinate among multiple healthcare providers – can also help improve efficiencies within emergency medicine. By identifying “hot spotting” patterns in health records, healthcare providers or health systems can intervene with specific patients to better treat their root health challenges and simultaneously improve the overall system’s efficiency and spending. The University of Illinois Hospital in Chicago, for example, has recently initiated a program to help frequent users of their emergency room services find and pay for rental housing. This housing subsidy program has been organized in an attempt to reduce the flow of homeless individuals who rely on emergency room care as a means to find safe temporary shelter, greatly driving up hospital costs and failing to resolve that individual’s need for permanent housing. Although the initiative is already supported by Chicago’s Center for Housing Health, the next step will be to find ways for city government to both support and institutionalize the initiative beyond the hospital’s limited initial support.
The leader in “hot spotting” initiatives is the Camden Coalition of Healthcare Providers (CCHP), an independent nonprofit in Camden, NJ committed to improving health outcomes and lowering healthcare costs in the Camden community. Much like the previous example of the University of Illinois Hospital, the CCHP also has a “housing first” pilot program to intervene with hospital “super-utilizers” in need of stable housing, supported by the state and county, among other supporters. For all its programs, the CCHP works extensively with real-time data from the city of Camden in order to identify patients in need of particular services and “hot spotting” trends, the bulk of which tends to take place in the emergency room. Beyond strictly health-related data, the CCHP is currently working on an integrated data system to bring together hospital, housing, and criminal justice related data together in one place, broadening the scope of their work.
Overall, a number of emerging technologies and data analytics have been integrated into the fields of public health and emergency management as a means to better understand the challenges at hand, mobilize the appropriate authorities prior to emergencies, and to significantly improve public information and emergency response time during crises.