California’s Unemployment Rate Remains Unchanged in March 2023 at 4.4{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}

California’s Unemployment Rate Remains Unchanged in March 2023 at 4.4{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}

ca urate 202303

IMPORTANT NOTE: The employment data for the month of March 2023 is taken from the survey week including March 12th. Data for the month of April is scheduled for release on May 19, 2023.

Employers added 8,700 nonfarm payroll jobs

April 22, 2023 – SACRAMENTO – California’s unemployment rate stayed constant from February 2023 and remained at 4.4 percent1 for March 2023 as the state’s employers added 8,700 nonfarm payroll jobs2 to the economy, according to data released today by the California Employment Development Department (EDD) from two surveys. February 2023’s initial estimate of a 4.3 percent unemployment rate was revised up by a tenth of a percentage point due to an increase in unemployment.

California payroll jobs totaled 19,371,100 in March 2023, up 32,700 from February 2023 and up 151,200 from March of last year.

California’s Labor Market, by the Numbers…

  • Since the current economic expansion began in April 2020, California has gained 3,049,800 jobs, which works out to a monthly average of 84,717 jobs gained over that time.
  • Six of California’s 11 industry sectors gained jobs in March with Private Education and Health Services (+7,000) leading the way with notable gains in Offices of Dentists, Home Health Care Services, and Hospitals.
  • Government (+6,900) enjoyed the second-strongest month-over gain as jobs were added in all three of its subsectors with the strongest gains coming in Local Government Education.
  • Construction (-8,200) suffered the largest reduction in month-over employment with much of its losses coming in the Specialty Trade Contractors and Construction of Buildings subsectors. Additional losses came in the Highway, Street, and Bridge Construction and Residential Building Construction, among others. Extreme weather and flooding likely played a role in the sector’s month-over decline as atmospheric rivers hit California during the survey week.

1. The unemployment rate comes from a separate federal survey of 5,100 California households.

2. The nonfarm payroll job numbers come from a federal survey of 80,000 California businesses.

Data Trends about Jobs in the Economy

Total Nonfarm Payroll Jobs

Total Nonfarm Payroll Jobs (Comes from a monthly survey of approximately 80,000 California businesses that estimates jobs in the economy – seasonally adjusted)

  • Month-over – Total nonfarm jobs in California’s 11 major industries totaled 17,967,800 in March – a net gain of 8,700 from February. This followed a downward revised (-10,500) month-over gain of 21,800 jobs in February.
  • Year-over – Total nonfarm jobs increased by 432,100 (a 2.5 percent increase) from March 2022 to March 2023 compared to the U.S. annual gain 4,145,000 jobs (a 2.7 percent increase).

ca-industries-202303.png

Total Farm jobs – The number of jobs in the agriculture industry increased from February by 10,500 a total of 434,900 jobs in March. The agricultural industry had 17,000 more farm jobs in March 2023 than it did in March a year ago.

Data Trends about Workers in the Economy

Employment and Unemployment in California (Based on a monthly federal survey of 5,100 California households which focuses on workers in the economy)

  • Employed – The number of Californians employed in March was 18,511,400, an increase of 14,700 persons from February’s total of 18,496,700 and also up 128,600 from the employment total in March
  • Unemployed – The number of unemployed Californians was 859,600 in March, an increase of 17,900 over the month and also up 22,500 in comparison to March 2022.
ca-laborforce-202303.png

Unemployment Insurance Claims (Not Seasonally Adjusted)

The following data is from a sample week that includes the 19th of each month: In related data that figures into the state’s unemployment rate, there were 414,119 people certifying for Unemployment Insurance benefits during the March 2023 sample week. That compares to 380,768 people in February and 398,638 people in March 2022. Concurrently, 46,792 initial claims were processed in the March 2023 sample week, which was a month-over increase of 6,209 claims from February and also a year-over increase of 7,607 claims from March 2022.

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MONTHLY LABOR FORCE DATA FOR COUNTIES
March 2023 (Preliminary); 2022 Benchmark
NOT SEASONALLY ADJUSTED
COUNTY LABOR FORCE EMPLOYMENT UNEMPLOYMENT RATE
STATE TOTAL 19,498,300 18,560,500 937,800 4.8{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
ALAMEDA 832,900 801,500 31,400 3.8{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
ALPINE 620 590 30 5.3{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
AMADOR 14,580 13,740 840 5.8{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
BUTTE 94,100 88,800 5,300 5.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
CALAVERAS 22,510 21,470 1,040 4.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
COLUSA 10,700 8,650 2,050 19.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
CONTRA COSTA 555,900 533,900 22,100 4.0{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
DEL NORTE 9,390 8,830 550 5.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
EL DORADO 94,700 90,900 3,900 4.1{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
FRESNO 467,000 427,000 40,100 8.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
GLENN 13,040 12,120 920 7.1{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
HUMBOLDT 60,500 57,600 2,800 4.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
IMPERIAL 71,400 60,300 11,100 15.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
INYO 8,380 8,060 320 3.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
KERN 392,300 354,700 37,600 9.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
KINGS 58,500 52,800 5,700 9.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
LAKE 29,000 27,170 1,830 6.3{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
LASSEN 8,990 8,430 560 6.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
LOS ANGELES 5,034,100 4,783,000 251,100 5.0{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MADERA 65,700 60,000 5,700 8.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MARIN 132,000 127,900 4,100 3.1{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MARIPOSA 7,330 6,830 490 6.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MENDOCINO 37,620 35,620 1,990 5.3{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MERCED 118,100 104,800 13,200 11.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MODOC 3,190 2,900 290 9.0{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MONO 8,900 8,550 350 3.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
MONTEREY 225,300 203,600 21,700 9.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
NAPA 72,200 69,600 2,600 3.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
NEVADA 48,760 46,730 2,040 4.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
ORANGE 1,609,900 1,555,400 54,500 3.4{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
PLACER 196,600 189,300 7,300 3.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
PLUMAS 7,220 6,410 810 11.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
RIVERSIDE 1,158,900 1,106,000 53,000 4.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SACRAMENTO 738,800 705,300 33,500 4.5{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN BENITO 33,300 31,100 2,200 6.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN BERNARDINO 1,014,700 968,900 45,800 4.5{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN DIEGO 1,620,200 1,560,800 59,400 3.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN FRANCISCO 581,300 564,100 17,200 3.0{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN JOAQUIN 351,200 327,900 23,300 6.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN LUIS OBISPO 140,700 135,900 4,800 3.4{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SAN MATEO 459,900 447,100 12,700 2.8{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SANTA BARBARA 226,000 215,800 10,200 4.5{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SANTA CLARA 1,059,800 1,026,200 33,500 3.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SANTA CRUZ 135,600 126,200 9,400 6.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SHASTA 74,500 70,100 4,400 5.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SIERRA 1,290 1,210 80 6.5{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SISKIYOU 16,690 15,380 1,320 7.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SOLANO 203,800 194,000 9,800 4.8{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SONOMA 251,700 242,700 9,000 3.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
STANISLAUS 245,800 228,800 17,000 6.9{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
SUTTER 47,500 43,000 4,500 9.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
TEHAMA 25,880 24,150 1,730 6.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
TRINITY 4,470 4,150 320 7.2{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
TULARE 212,400 188,400 23,900 11.3{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
TUOLUMNE 20,040 18,920 1,120 5.6{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
VENTURA 418,900 402,000 16,900 4.0{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
YOLO 110,900 105,200 5,700 5.1{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}
YUBA 32,600 30,100 2,500 7.7{08cd930984ace14b54ef017cfb82c397b10f0f7d5e03e6413ad93bb8e636217f}

Source: CA. EDD

Lack of trust in AI-led electronic health systems remains

Lack of trust in AI-led electronic health systems remains

Lack of trust in AI-led electronic health systems remains

Research, innovations and leaders want to support working with synthetic intelligence (AI) in electronic wellbeing file (EHR) techniques to stimulate prevalent adoption and just take the advancing tech into mainstream health care.

Conventional, guide and antiquated digital wellness programs are going through a digital transformation. Synthetic intelligence (AI) is increasingly getting into healthcare, transforming present on-line data by removing the cumbersome, advanced and puzzling nature of retaining and sustaining digital well being data (EHRs), at times acknowledged as digital professional medical information (EMRs).

AI-led EHR systems have the likely to contribute to democratising entry to educated, precise and timely care. Nevertheless, innovators need to explore the tech’s capabilities to offer equitable health care and fully grasp how it can meet individuals’ and communities’ certain requires.

Tech to completely transform EHR capabilities

Well being technologies pioneers are finding out how AI-led discoveries can support supply this healthcare conventional. They are responding by checking out how technological characteristics in EHR methods can build a additional higher-doing digital wellness ecosystem.

“AI has the likely to change present EHRs from becoming passive data storage methods that organise overall health facts into active information-generating devices that area actionable clinical insights from well being info,” claims Steven Lin, Executive Director of the Stanford Healthcare AI Used Investigate Crew (HEA3RT) at Stanford University College of Medicine.

Utilizing AI, clinicians can make formerly hidden styles and insights in patent knowledge obvious. In change, unlocking this data can direct to substantial enhancements in a patient’s cure and broader populace care. “EHRs, at first built to retailer and allow for the retrieval of patients’ overall health facts, are shifted into a considerably a lot more practical realm by AI,” suggests Peter Fish, CEO of Mendelian.

As the advancing tech grows in the confront-to-confront medical setting, practitioners improve how they keep and entry well being records. “Designed thoughtfully, an AI-driven EHR can turn into the physician’s most powerful instrument and even a reliable partner,” says Lin.

Even so, problems exist all-around utilising AI’s abilities in EHR, stopping the tech from moving into mainstream health care. “Designed poorly, it can obfuscate and interfere with individual care and worsen the epidemic of medical professional burnout,” Lin provides.

Predictive algorithms form personalisation

AI-led EHR techniques are accelerating to provide personalised health care. AI in EHR aims to give practitioners a proactive device for personalised healthcare administration of chronic and vulnerable sufferers.

“As drugs moves from the a single-dimension-suits-all approach into stratified and in the long run personalised medicine, it will become more and additional advanced to supply the treatment sufferers ought to have at scale,” suggests Fish.

Predictive algorithms built-in into EHRs can guidance clinical decision-creating. AI-embedded tools can forecast whether or not a individual will have a specific proportion likelihood of becoming hospitalised for a individual problem and will endorse proper and responsive intervention.

The tech captures computational pattern-matching abilities which exceed practitioners’ personal at this amount, Fish says. Predictive algorithms can also enhance the experience of using EHRs for physicians by personalising menus, buttons, layouts and shortcuts to the physician’s sample of use.

New developments and emerging research

“I’m enthusiastic about AI-pushed risk prediction in principal care and population health,” claims Lin. Determining individuals at significant chance of preventable results this kind of as heart attacks, strokes, unexpected emergency division visits and hospitalisations and intervening utilizing proof-dependent recommendations can preserve lives and revenue, Lin claims.

“I’m also energized about the subsequent generation of AI-assisted clinical selection-creating applications that can enable medical professionals make the very best treatment options for ‘patients like theirs’,” Lin adds. Phenotyping clients and utilizing authentic-earth evidence to personalise care are examples of how we can anticipate AI to produce amid ongoing analysis.

Mendelian, a uncommon disease diagnostics healthcare business, has introduced MendelScan, a medical determination assistance technique. It is created to allow for and deploy exceptional disorder circumstance-getting algorithms on EHRs asynchronously, as it aims to type a population-level proactive treatment procedure.

Translating tech into treatment for people’s personalised needs

“It would be disingenuous to say that AI in EHRs is obtaining a measurable impact on the treatment or personalisation of treatment on precise patients correct now,” says Lin. In spite of “pockets of success”, Lin provides that health care does not show anything at all “broad or significant yet” in the AI-led EHR area. “There is authentic possible, but the proof points are not there,” Lin provides.

Substantial challenges exist in the sphere, precisely in maximising AI’s abilities in EHR programs and providing alternatives to increase their likely and strengthen total world-wide health care.

Lots of thriving stories of AI in EHRs have still to be employed at scale. There are much more stories of embarrassing failures, these kinds of as Epic’s AI sepsis product, than there are successes, Lin shares. Poor predictions and demonstrations of AI have popular and probably extensive-standing consequences. “[It] appreciably impacts medical professional and individual trust in AI, which is minimal all round,” suggests Lin.

“Another obstacle is the basic unwillingness and resistance of large EHR suppliers to get the job done with 3rd-bash AI builders, stifling innovation and development,” carries on Lin. Aligning legacy health care devices and processes with innovations featuring new know-how is an ongoing impediment. Therefore, partnerships concerning third-bash AI developers and massive EHR suppliers stay largely unexplored.

“The continued absence of interoperability among EHRs remains a significant barrier,” Lin adds. Disparate devices that do not converse with 1 one more are a limitation that stifles progression for practitioners seeking for AI to boost their present details-gathering procedures.

“We see a great deal of complexity around facts sharing that can be solved technically,” states Fish. With cautious planning, the EHR info is incomplete and in some cases inaccurate, and driving substantial-scale adoption proves problematic. “It may perhaps choose decades for nationwide commissioners to choose on the subsequent techniques,” relays Fish.

Long run AI potential in EHR units

“AI holds enormous quantities of assure for medicine,” Fish facts. Nonetheless, considerable real-entire world pilots and usefulness reports are essential to crank out the strong proof demanded to entice final decision-makers and gatekeepers. Currently, we require leaders to winner and assist AI in EHR methods to empower their have faith in and common adoption.

A important gap in our knowledge is how greatest to integrate AI versions into human-pushed medical workflows. “We require to devote substantially a lot more into the implementation science of AI,” says Lin.

Knowing the authentic-everyday living abilities of AI-led tech is a person of lots of needs. Making certain it is seamless and interesting is vital as well. “It does not issue how very good the AI is, if the engineering causes way too a lot friction on present human-pushed workflows, it will not be adopted,” Lin highlights.