Opioid stewardship is most often a multidisciplinary effort, but at Lifespan health system in Rhode Island, an unusual team “member”—artificial intelligence—is being recognized for its contribution.

Employing physician education and machine learning led to significant reductions in prescribed opioid doses, decreases in benzodiazepine co-prescribing, and increased naloxone co-prescribing, as Ashley Rimay, PharmD, a controlled substance pharmacist at Lifespan, said during a virtual poster presentation at the ASHP 2020 Midyear Clinical Meeting and Exposition (Best Practice poster 1).

“The collaboration between the pharmacists, data scientists, physicians and leadership was essential to making this program a success,” she said.

Rimay and a team of hospital and pharmacy leaders, a pharmacy data scientist, a pharmacy informatics coordinator, a senior clinical pharmacist specialist, and the chief medical officer set out to develop an opioid stewardship program. They wanted to capitalize on the wealth of electronic health record (EHR) data at their disposal and did so by providing a machine-learning model with two years of EHR-based opioid prescribing data from their institution, which it has used to identify outlying prescribers.

Each day, the software generates a scatterplot image plotting out that day’s prescribing practices, positioning prescribers on the graph according to whether or not their opioid prescribing falls in line with appropriate opioid prescribing.

Once an outlier is identified, the controlled substance pharmacist selects 15 opioid prescriptions at random and audits them for compliance with controlled substance laws. The findings are then passed on to physician leadership for peer clinical evaluation, provider education as well as possible follow-up audits, the researcher said.

In addition to providing outlier physicians with information on appropriate opioid prescribing, Rimay and her team educated them on the consequences of inappropriate opioid prescribing and opioid diversion, presented them with license reprimands issued by Rhode Island’s Medical Board, and detailed the state’s controlled substance law on prescribing for acute and chronic pain.

Between January and December 2019, the machine-learning software identified 25 outlying prescribers, Rimay reported. Additionally, more than 240 attending physicians and 900 residents participated in the system-wide opioid education initiatives.

In 2019, after the opioid stewardship program was rolled out, the health system saw a 14.4% decrease in average morphine equivalent daily doses (MEDD) among those provided with opioid education, including both outliers and the general physician population. In contrast, average MEDD increased by 0.39% during the same period among a group of Lifespan physicians not provided with opioid prescribing education.

During the same time, among physicians who received opioid prescribing education, there was a 9.7% decrease in benzodiazepine co-prescribing and a 15.7% increase in naloxone co-prescribing—both of which are in line with practices recommended by the CDC for those receiving opioids for chronic pain. In contrast, there was only a 4.35% drop in benzodiazepine co-prescribing and a 5% increase in naloxone co-prescribing among those who did not receive education on appropriate opioid prescribing, Rimay said.

“Health systems should foster collaboration between pharmacists, data scientists, physicians and leadership to develop a controlled substance prescription stewardship program,” the researcher concluded.

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Many central banks have undertaken initiatives to develop big data platforms to facilitate storage and processing of large data sets, but progress has varied.

The BIS (Bank of International Settlements) has published a new report on the application of big data by central banks.

The report is based on a survey organised by the BIS’ Irving Fischer Committee, updating a similar survey conducted five years earlier.

The report highlights the growth of big data sources as reflective of the impact of digitisation, new capabilities to process “traditional” information such as text, and the creation of large databases as a by-product of complex operations taking place in modern societies.

“Additionally, vast amounts of data have emerged in the administrative, commercial and financial areas, an evolution spurred by the important data collection strategies undertaken after the Great Financial Crisis of 2007-09,” the report says.

Central banks are said to now have a “comprehensive view” of big data, with 80 percent of respondents reporting its increased use to support policy efforts, compared to just one third in 2015.

The report notes that big data applications supporting central banks’ operational work had initially been limited, contrasting with the rapid pace of innovation seen in the private sector.

Central banks have reported using big data in monetary policy, financial stability, research and the production of official statistics.

“Newly available databases and techniques are increasingly mobilised to support economic analyses and nowcasting/forecasting exercises, construct real-time market signals and develop sentiment indicators derived from semi-structured data,” the report says.

“This has proved particularly useful in times of heightened uncertainty or economic upheaval, as observed during the Covid-19 pandemic.”

A majority of central banks have also reported using big data for micro-level supervision (suptech) and regulation (regtech), with an increasing focus on consumer protection, such as to assess misconduct, detect fraudulent transactions and combat money laundering.

The report underscores the need for adequate IT infrastructure and human capital, highlighting that many central banks have undertaken initiatives to develop big data platforms to facilitate the storage and processing of large and complex data sets.

“But progress has varied, reflecting the high cost of such investments and the need to trade off various factors when pursuing these initiatives,” it says, noting the difficulties involved with hiring and training staff amid a limited supply of data scientists.

The report also highlights challenges related to legal issues, ethics, privacy, data quality, and the fairness and accuracy of algorithms trained on pre-classified and/or unrepresentative data sets.

“Moreover, a key issue is to ensure that predictions based on big data are not only accurate but also ‘interpretable’ and representative, as to carry out evidence-based policy central banks need to identify specific explanatory causes or factors.”

The report says cooperation could facilitate central banks’ use of big data, and that international financial institutions can help foster such cooperation.

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