Searching for regulatory non-compliers. 

Regulatory teams are tasked with identifying a small number of problem businesses. They must sift through thousands of applications, often with only the option of a random audit, which is time-consuming, places burdens on compliant businesses and has a low conversion rate.

Predictive modelling uses data on old infractions to identify those at highest risk. The characteristics of filings from businesses who have broken rules in the past can be used to predict the types of business most likely to be non-compliant in the future. This can be used to create tools to select cases for audit; audits can be randomized over this higher-risk group, with selection criteria refined over time.

In assessing compliance with restaurant waste disposal regulations, NYC cross-referenced industry data on grease production with restaurant permit data and sewer back-up data from city agencies, allowing them to better-predict waste violations and to target enforcement (read more here).

Backlog of pest control cases are processed without information on their potential severity. 

Ordinarily, cases are investigated and dealt with in the order that they are received. Although pest control teams have insights as to which cases are most likely to lead to serious outbreaks, calls are not classified until an initial inspection.

Predicting the danger of outbreak posed by new cases before inspection, and reaching those cases first. Using data about the location, timing and nature of the complaint and comparing this to past outbreaks, cases can be ranked according to the risk that they turn into full outbreaks. These cases can be prioritized with quicker inspection and/or initial deployment.

In Chicago, after reviewing data on historic outbreaks, incidences of 311 requests related to garbage or broken water mains close to pest reports were used to identify severe outbreaks and trigger an immediate response (read more here).

Excessive force violations by police officers have huge negative repercussions in the community and for police careers.

Targeted early interventions could enable forces to prevent such interactions, rather than responsively dealing with the officers after an incident occurs.

Refine early warning system, identifying officers most likely to have negative interactions with the public. These systems flag recurring complaints against officers and notify supervisors when certain thresholds were reached, such as a certain number of use-of-force complaints over a given period of time, so those supervisors could implement targeted interventions.

In Charlotte, teams worked to enhance existing early warning tools by making better use of existing data. Combining information on officer demographics, training, payroll, on-the-job actions, internal affairs data, dispatch data, negative interaction reports and publically-available data, they were able to increase the accuracy of the CMPD system by 15-20% while reducing false positives by 55% (read more here.

EMS dispatchers are required to make dozens of daily deployment decisions, often with incomplete information.

Decisions can be delayed if crew readiness information is not available, or skewed if accurate traffic data not available.

Provide teams with traffic and hospital-turnaround adjusted estimates of crew readiness. Using accumulated data on dispatches,  delays in response times caused by dispatch decisions could be assessed. Identifying hospitals and traffic corridors that can cause delays in response time could allow teams to better identify optimal teams for deployment.

In Louisville, dispatchers are supported with regular reports from computer-aided dispatch software which detailed patterns in turnaround times for specific hospitals and for specific crews (read more here).

Ambulances’ standby locations are chosen based on dispatchers’ habits or their instincts as to where they could most easily get to emergencies.

Based on their personal experience, dispatchers and teams work with only a limited view of total EMS demand in the city.

Standby deployment based on city-wide analysis of accident patterns, traffic patterns and crew readiness. Building on the insights of dispatchers and ambulance crew with city-wide data on accidents, hospital transfers and traffic, ambulances can be deployed in a way that improves coverage and reduces emergency response time.

In New Orleans, a project is underway to map optimal standby locations for ambulance crews, given the patterns of emergency calls seen in the city.

Outreach tools, such as SMS texts, to Medicaid clients deliver low conversion rates. 

Those at risk of missing important medical appointments are texted reminders, but impact seems to be limited.

Optimize SMS outreach services with local testing of different forms. With relatively little disruption, different messages and timing of texts could be tested to find combinations that delivered maximum impact for different groups.

In New Orleans, experimental A/B testing was used to refine the content of text message outreach to Medicaid waiver program recipients, to maximize conversion rates (read more here).