½ÃÀ庸°í¼­
»óǰÄÚµå
1410065

¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå ¿¹Ãø(2023-2028³â)

Healthcare Fraud Analytics Market - Forecasts from 2023 to 2028

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: Knowledge Sourcing Intelligence | ÆäÀÌÁö Á¤º¸: ¿µ¹® 149 Pages | ¹è¼Û¾È³» : 1-2ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    



¡Ø º» »óǰÀº ¿µ¹® ÀÚ·á·Î Çѱ۰ú ¿µ¹® ¸ñÂ÷¿¡ ºÒÀÏÄ¡ÇÏ´Â ³»¿ëÀÌ ÀÖÀ» °æ¿ì ¿µ¹®À» ¿ì¼±ÇÕ´Ï´Ù. Á¤È®ÇÑ °ËÅ並 À§ÇØ ¿µ¹® ¸ñÂ÷¸¦ Âü°íÇØÁֽñ⠹ٶø´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀº 2021³â 16¾ï 2,600¸¸ ´Þ·¯·Î Æò°¡µÇ¾ú°í 2028³â¿¡´Â 59¾ï 8,900¸¸ ´Þ·¯¿¡ ´ÞÇÒ Àü¸ÁÀÌ¸ç º¹ÇÕ ¿¬°£ ¼ºÀå·ü(CAGR) 20.47%·Î ¼ºÀåÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå ±Ô¸ð°¡ È®´ëµÇ°í ÀÖ¾î, ÇコÄÉ¾î »ç¾÷¿¡¼­ÀÇ ºÎÁ¤ ÇàÀ§ÀÇ °ËÃâ°ú ¹æÁö¿¡ ÁÖ·ÂÇϰí ÀÖ½À´Ï´Ù. ºÎÁ¤ û±¸, ½ºÇªÇÎ, ºÒÇÊ¿äÇÑ Ä¡·á µîÀº ¸ðµÎ °Ç°­ °ü¸® ½Ã½ºÅÛ¿¡ ¸Å³â ¼ö½Ê¾ï ´Þ·¯ÀÇ ¼ÕÇØ¸¦ ÀÔ½À´Ï´Ù. ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½Ã½ºÅÛÀº ÷´Ü µ¥ÀÌÅÍ ºÐ¼® ±â¼ú°ú ¾Ë°í¸®ÁòÀ» »ç¿ëÇÏ¿© µ¿Çâ, ÀÌ»ó, Àǽɽº·¯¿î ÇàÀ§¸¦ ¹ß°ßÇϰí Àû±ØÀûÀÎ ºÎÁ¤ ÇàÀ§¸¦ ŽÁöÇÏ°í ¿¹¹æÇÒ ¼ö ÀÖ½À´Ï´Ù. ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ ¼ºÀåÀº ºÎÁ¤ ÇàÀ§¸¦ ÁÙÀ̰í, ÇコÄɾî Á¶Á÷À» ±ÝÀüÀû ¼Õ½Ç·ÎºÎÅÍ º¸È£Çϰí, ÇコÄÉ¾î ¾÷°èÀÇ ½Å·Ú¼º°ú ¹«°á¼ºÀ» À¯ÁöÇÏ´Â Å« °¡´É¼ºÀÌ ÀÖ½À´Ï´Ù. ½ÃÀå Á¡À¯À² Ãø¸é¿¡¼­ Àü¹® ºÐ¼® ¼Ö·ç¼Ç Á¦°ø¾÷ü, ±â¼ú ±â¾÷ ¹× ÀÇ·á Á¶Á÷ ÀÚü¸¦ Æ÷ÇÔÇÑ ¼ö¸¹Àº ¾÷°è °æÀï ¾÷üµéÀÌ ½ÃÀåÀÇ »ó´ç ºÎºÐÀ» ¾ò±â À§ÇØ ³ë·ÂÇϰí ÀÖ½À´Ï´Ù. ÇコÄɾî Á¶Á÷ÀÌ ºÎÁ¤À» °ËÃâ ¹× ¹æÁöÇϱâ À§ÇØ °í±Þ ºÐ¼® Åø°ú ±â¼ú¿¡ ÅõÀÚÇϰí Àֱ⠶§¹®¿¡ ½ÃÀåÀº ´õ¿í È®´ëµÉ °ÍÀ¸·Î º¸ÀÔ´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ºÐ¼® ½ÃÀå¿¡¼­ÀÇ ºñ¿ë¾ïÁ¦¿Í À繫¼Õ½Ç¹æÁöÀÇ Çʿ伺.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ¾÷°èÀÇ ÁÖ¿ä ÃËÁø¿äÀÎÀº ºñ¿ë ¾ïÁ¦¿Í À繫 ¼Õ½Ç ȸÇÇÀÇ Çʿ伺ÀÔ´Ï´Ù. NHCAA¿¡ µû¸£¸é °Ç°­ °ü¸® ºÒ¹ýÀº ¸Å³â ¼¼°è °Ç°­ °ü¸® ÁöÃâÀÇ 3%¿¡¼­ 10%¸¦ Â÷ÁöÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ÀÌ °æÁ¦Àû ºÎ´ãÀº ºÎÁ¤ ÇàÀ§¸¦ ŽÁöÇÏ°í ¿¹¹æÇϱâ À§ÇØ ºÎÁ¤ ºÐ¼® µµ±¸¸¦ µµÀÔÇÒ Çʿ伺À» °­Á¶ÇÕ´Ï´Ù. ¼³¹® Á¶»ç¿¡ µû¸£¸é ÀÌ·¯ÇÑ ¼Ö·ç¼ÇÀ» äÅÃÇϸé ÀÇ·á Á¶Á÷ÀÇ ºñ¿ëÀ» Å©°Ô ÁÙÀÏ ¼ö ÀÖ½À´Ï´Ù. °í±Þ ºÐ¼® ¼Ö·ç¼Ç¿¡ ´ëÇÑ ½ÃÀå ¼ö¿ä´Â ºñ¿ë Àý°¨°ú ±ÝÀüÀû ¼Õ½ÇÀ» ÇÇÇÏ´Â µ¥ ÁßÁ¡À» µÐ´Ù´Â ¹è°æÀÌ ÀÖ½À´Ï´Ù.

ºÎÁ¤ ¹æÁö¿¡ ´ëÇÑ ÀǽÄÀÇ °íÁ¶¿Í ÁßÁ¡ÀÌ ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå ±Ô¸ð¸¦ È®´ë

ÇコÄÉ¾î ºÎÁ¤ºÐ¼® ¾÷°è¿¡¼­´Â ºÎÁ¤¹æÁö¿¡ ´ëÇÑ ÀǽÄÀÌ ³ô¾ÆÁö°í Áß½ÃµÇ°Ô µÇ¾ú½À´Ï´Ù. °Ç°­ °ü¸® »ç±â ºÐ¼® ¾÷°è¿¡¼­´Â »ç±â ¹æÁö¸¦ Áß½ÃÇÔÀ¸·Î½á ½ÃÀå ¼ºÀå°ú Çõ½ÅÀ» ÃËÁøÇÕ´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ ÇコÄÉ¾î ºÎÁ¤¿¡ ´ëÇ×Çϱâ À§ÇÑ Á¤ºÎÀÇ ´ëó¿Í ±ÔÁ¦.

ÇコÄÉ¾î ºÎÁ¤À» ¿¹¹æÇϴµ¥ À־, Á¤ºÎÀÇ ´ëó¿Í ¹ý±ÔÁ¦°¡ ÇÏ´Â ¿ªÇÒÀº ¸Å¿ì Áß¿äÇÕ´Ï´Ù. ¼¼°è Á¤ºÎ´Â ºÎÁ¤ ÇàÀ§¿Í ½Î¿ì°í °Ç°­ °ü¸® ½Ã½ºÅÛÀÇ ¹«°á¼ºÀ» º¸È£Çϱâ À§ÇØ ´õ¿í °­·ÂÇÑ Á¶Ä¡¸¦ Á¦Á¤Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ Á¢±Ù¹ý¿¡´Â ºÎÁ¤´ëÃ¥ Àü¹® ÆÀÀÇ Ã¢¼³, ºÎÁ¤ °ËÁö ÇÁ·Î±×·¥¿¡ ´ëÇÑ ÀÚ±Ý Á¦°øÀÇ È®´ë, ºÎÁ¤ ÇàÀ§¸¦ ÀúÁöÇÏ°í ¹úÄ¢À» ºÎ°úÇϱâ À§ÇÑ ¹ý·üÀÇ Á¦Á¤ µîÀÌ Æ÷ÇԵ˴ϴÙ. ¶ÇÇÑ Á¤ºÎ´Â ¾÷°è °ü°èÀÚ¿Í Çù·ÂÇÏ¿© ¸ð¹ü »ç·Ê¸¦ ±¸ÃàÇϰí Á¤º¸¸¦ °øÀ¯Çϸç û±¸ ¹× û±¸ û±¸ÀÇ Åõ¸í¼ºÀ» ³ôÀÔ´Ï´Ù. °Ç°­ °ü¸® ºÎÁ¤ ÇàÀ§¿¡ ´ëÇ×ÇÔÀ¸·Î½á °æÁ¦Àû Ã¥ÀÓÀ» ¿Ï¼öÇϰí ȯÀÚ¸¦ º¸È£ÇÏ¸ç º¸´Ù ¾ÈÀüÇϰí È¿À²ÀûÀÎ °Ç°­ °ü¸® ½Ã½ºÅÛÀ» ÃËÁøÇÕ´Ï´Ù.

ºÏ¹Ì´Â ÀÇ·á ºÎÁ¤ ºÐ¼® ½ÃÀå ½ÃÀå ¸®´õÀÔ´Ï´Ù.

ºÏ¹Ì´Â ÀÇ·á ºÎÁ¤ ºÐ¼® ½ÃÀå Á¡À¯À²·Î ¾÷°è¸¦ ¼±µµÇϰí ÀÖ½À´Ï´Ù. ÀÌ ¹è°æÀº Áö¿ªÀÇ ¾ö°ÝÇÑ ±ÔÁ¦ ü°è, °í¾×ÀÇ °Ç°­ °ü¸® ÁöÃâ, ÀÇ·á »ç±â Áõ°¡ µî ´Ù¾çÇÑ ¿øÀÎÀÌ ÀÖ½À´Ï´Ù. °Ô´Ù°¡ ºÏ¹Ì¿¡´Â ºÎÁ¤ ¹æÁö¿Í ÄÄÇöóÀ̾𽺸¦ Áß½ÃÇÏ´Â ÀÇ·á Á¦µµ°¡ È®¸³µÇ¾î ÀÖ½À´Ï´Ù. ÀÌ Áö¿ªÀº ÷´Ü ºÐ¼® ±â¼úÀÇ µµÀÔ°ú ÇÔ²² ÀÇ·á »ç±â ¹æÁö¿¡ ÁßÁ¡À»µÎ°í ÀÖÀ¸¸ç, °Ç°­ °ü¸® »ç±â ºÐ¼®¿¡¼­ÀÌ Áö¿ª ½ÃÀå ¸®´õ·Î¼­ÀÇ ÁöÀ§¸¦ Áö¿øÇϰí ÀÖ½À´Ï´Ù.

°Ç°­ °ü¸® ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ ÀüÀÚ ÀÇ·á ±â·Ï(EHR) ¹× µðÁöÅÐ °Ç°­ ½Ã½ºÅÛ Ã¤Åà Áõ°¡.

ÀüÀÚ ÀÇ·á±â±â(EHR)¿Í µðÁöÅÐ °Ç°­ ½Ã½ºÅÛÀÇ È°¿ë È®´ë´Â ÀÇ·á ºÎÁ¤ ºÐ¼® ¾÷°è¿¡ Å« ¿µÇâÀ» ¹ÌÄ¡°í ÀÖ½À´Ï´Ù. ¹Ì±¹ ÀÇ·áÁ¤º¸±â¼úÁ¶Á¤±¹(Office of the National Coordinator for Health Information Technology)¿¡ µû¸£¸é 2021³â±îÁö ¹Ì±¹ ³» ºñ¿¬¹æ°è ±Þ¼º±â º´¿øÀÇ 96%°¡ ÀÎÁõ EHR ½Ã½ºÅÛÀ» ä¿ëÇϰí ÀÖ½À´Ï´Ù. ÀÌ¿Í °°ÀÌ ÇコÄÉ¾î µ¥ÀÌÅͰ¡ µðÁöÅÐÈ­µÊÀ¸·Î½á, ºÎÁ¤ ºÐ¼® ½Ã½ºÅÛÀÌ ºÎÁ¤ ÇàÀ§¸¦ °ËÁö ¹× ¹æÁöÇϱâ À§Çؼ­ ÀÌ¿ëÇÒ ¼ö ÀÖ´Â ¸¹Àº Á¤º¸¸¦ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù. EHR°ú µðÁöÅÐ °Ç°­ ½Ã½ºÅÛÀÇ ÅëÇÕÀº ½Ç½Ã°£ ¸ð´ÏÅ͸µ, µ¥ÀÌÅÍ ºÐ¼® ¹× ÆÐÅÏ ½Äº°À» °¡´ÉÇÏ°Ô Çϰí, °Ç°­ °ü¸® Á¶Á÷Àº »ç±â û±¸, ÄÚµù ½Ç¼ö ¹× ±âŸ »ç±â ÇàÀ§¸¦ ½Ç½Ã°£À¸·Î ¹ß°ßÇÒ ¼ö ÀÖ½À´Ï´Ù.

ÁÖ¿ä ¹ßÀü:

  • 2022³â 6¿ù Change Healthcare Á¦Ç° ¶óÀÎÀÎ Patient Engagement´Â Àüü ȯÀÚ °æÇèÀÇ ÅÍÄ¡ Æ÷ÀÎÆ®¸¦ ¿¬°áÇÏ°í ¾×¼¼½º¸¦ Áõ°¡½Ã۰í ȯÀÚ¿Í ÀÇ»ç °£ÀÇ ÀÇ»ç¼ÒÅëÀ» °³¼±ÇÕ´Ï´Ù. º¯°æ °Ç°­ °ü¸® ¼­ºñ½º´Â ·ç¸¶ °Ç°­ÀÌ ¾÷°è¸¦ °ßÀÎÇÏ´Â ¸ÞÄ¿´ÏÁò°ú °áÇÕÇÏ¿© °ø±ÞÀÚ¿¡°Ô ±â´ÉÀû, ÀÓ»óÀû, ±ÝÀüÀû ÀͽºÄ¿¼ÇÀ» Á¶Á¤ÇÒ ¼öÀÖ´Â ÈûÀ» Á¦°øÇϰí ȯÀÚ °æÇèÀ» ´õ¿í Çâ»ó½Ãŵ´Ï´Ù.

ȸ»ç Á¦Ç°

  • »ç±â °¨Áö ½Ã½ºÅÛ: IBMÀº »ç±â ÇàÀ§¸¦ ³ªÅ¸³¾ ¼ö ÀÖ´Â µ¿Çâ°ú ÀÌ»óÀ» °¨ÁöÇϱâ À§ÇØ ¸Ó½Å·¯´× ¹× ÀΰøÁö´ÉÀ» Ȱ¿ëÇÏ´Â °í±Þ ºÐ¼® µµ±¸¸¦ Á¦°øÇÕ´Ï´Ù. ¼ÛÀå, ¼ÛÀå ±â·Ï ¹× ȯÀÚ Á¤º¸¿Í °°Àº ´ë·®ÀÇ ÀÇ·á µ¥ÀÌÅ͸¦ ÀÌ·¯ÇÑ ½Ã½ºÅÛ¿¡¼­ ºÐ¼®ÇÏ¿© ºñÁ¤»óÀûÀÎ Çൿ°ú »ç±â °¡´É¼ºÀ» °¨ÁöÇÕ´Ï´Ù.
  • ½Ç½Ã°£ ¸ð´ÏÅ͸µ ¹× °æ°í: OptumÀº °Ç°­ °ü¸® Æ®·£Àè¼Ç ¹× µ¥ÀÌÅÍ ½ºÆ®¸²À» Áö¼ÓÀûÀ¸·Î ¸ð´ÏÅ͸µÇÏ´Â ½Ç½Ã°£ ¸ð´ÏÅ͸µ ½Ã½ºÅÛÀ» Á¦°øÇÕ´Ï´Ù. ÀÌ·¯ÇÑ ½Ã½ºÅÛÀº ±ÔÄ¢ ±â¹Ý ¾Ë°í¸®ÁòÀ» äÅÃÇÏ°í ºÒ¹ýÀûÀÎ ÇàÀ§¸¦ °¨ÁöÇϰí ÅëÁöÇÔÀ¸·Î½á ½Å¼ÓÇÑ °³ÀÔ°ú ¹æÁö¸¦ °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù.
  • º»ÀÎ È®ÀÎ : LexisNexis Risk Solutions´Â ÀÇ·á ±â°üÀÌ È¯ÀÚ, ÀÇ·á Á¦°ø¾÷ü ¹× ±âŸ ±â¾÷ÀÇ ½Å¿øÀ» È®ÀÎÇÒ ¼ö ÀÖµµ·Ï Áö¿øÇÏ´Â ½Å¿ø È®ÀÎ ±â¼úÀ» Á¦°øÇÕ´Ï´Ù. ½ºÇªÇΰú »ç±â ÇàÀ§¸¦ ÇÇÇϱâ À§ÇØ ÀÌ·¯ÇÑ ¼Ö·ç¼ÇÀº °­·ÂÇÑ º»ÀÎ È®ÀÎ ¾Ë°í¸®Áò°ú µ¥ÀÌÅͺ£À̽º¸¦ Ȱ¿ëÇÕ´Ï´Ù.
  • °ø±ÞÀÚ ³×Æ®¿öÅ© ºÐ¼® : OptamÀÇ »ç±â¼º ºÐ¼® ¼Ö·ç¼ÇÀº ³×Æ®¿öÅ© ºÐ¼® ±â¼úÀ» »ç¿ëÇÏ¿© ÀÇ·á Á¦°ø¾÷ü, ȯÀÚ ¹× ±âŸ Á¶Á÷ °£ÀÇ ¿¬°á ¹× »óÈ£ °ü°è¸¦ ¹àÈü´Ï´Ù. ÀÌ ¼³¹®Á¶»ç´Â Çù·Â, ¾Ç¼º û±¸ ¹æ¹ý ¶Ç´Â Á¶Á÷È­µÈ ³×Æ®¿öÅ©°¡ Âü¿©ÇÏ´Â ¾Ç¼º °èȹÀ» ã´Â µ¥ µµ¿òÀÌ µË´Ï´Ù.

¸ñÂ÷

Á¦1Àå ¼­·Ð

  • ½ÃÀå °³¿ä
  • ½ÃÀåÀÇ Á¤ÀÇ
  • Á¶»ç ¹üÀ§
  • ½ÃÀå ¼¼ºÐÈ­
  • ÅëÈ­
  • ÀüÁ¦Á¶°Ç
  • ±âÁسâ°ú ¿¹Ãø³âÀÇ Å¸ÀÓ¶óÀÎ

Á¦2Àå Á¶»ç ¹æ¹ý

  • Á¶»ç µ¥ÀÌÅÍ
  • Á¤º¸¿ø
  • Á¶»ç µðÀÚÀÎ

Á¦3Àå ÁÖ¿ä ¿ä¾à

  • Á¶»ç ÇÏÀ̶óÀÌÆ®

Á¦4Àå ½ÃÀå ¿ªÇÐ

  • ½ÃÀå ¼ºÀå ÃËÁø¿äÀÎ
  • ½ÃÀå ¼ºÀå ¾ïÁ¦¿äÀÎ
  • Porter's Five Forces ºÐ¼®
    • °ø±Þ±â¾÷ÀÇ Çù»ó·Â
    • ±¸¸ÅÀÚÀÇ Çù»ó·Â
    • ½Å±Ô Âü°¡¾÷üÀÇ À§Çù
    • ´ëüǰÀÇ À§Çù
    • ¾÷°è ³» °æÀï ±â¾÷°£ °æÀï °ü°è
  • ¾÷°è ¹ë·ùüÀÎ ºÐ¼®

Á¦5Àå ¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ÄÄÆ÷³ÍÆ®º°

  • ¼Ò°³
  • ¼ÒÇÁÆ®¿þ¾î
  • ¼­ºñ½º

Á¦6Àå ¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : Àü°³º°

  • ¼Ò°³
  • ¿ÂÇÁ·¹¹Ì½º
  • Ŭ¶ó¿ìµå ±â¹Ý

Á¦7Àå ¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ¿ëµµº°

  • ¼Ò°³
  • º¸Çè û±¸ÀÇ Àç°ËÅä
  • ÁöºÒÀÇ ¹«°á¼º
  • ¾ÆÀ̵§Æ¼Æ¼¿Í ¾×¼¼½º °ü¸®
  • ±âŸ

Á¦8Àå ¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ÃÖÁ¾ »ç¿ëÀÚº°

  • ¼Ò°³
  • ÇコÄɾî ÁöºÒÀÚ
  • ÇコÄɾî Á¦°ø¾÷ü
  • Á¤ºÎ±â°ü
  • ±âŸ

Á¦9Àå ¼¼°è ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå :Áö¿ªº°

  • ¼Ò°³
  • ºÏ¹Ì
    • ¹Ì±¹
    • ij³ª´Ù
    • ¸ß½ÃÄÚ
  • ³²¹Ì
    • ºê¶óÁú
    • ¾Æ¸£ÇîÆ¼³ª
    • ±âŸ
  • À¯·´
    • ¿µ±¹
    • µ¶ÀÏ
    • ÇÁ¶û½º
    • ÀÌÅ»¸®¾Æ
    • ½ºÆäÀÎ
    • ±âŸ
  • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
    • »ç¿ìµð¾Æ¶óºñ¾Æ
    • ¾Æ¶ø¿¡¹Ì¸®Æ®(UAE)
    • ±âŸ
  • ¾Æ½Ã¾ÆÅÂÆò¾ç
    • ÀϺ»
    • Áß±¹
    • Àεµ
    • Çѱ¹
    • Àεµ³×½Ã¾Æ
    • ´ë¸¸
    • ±âŸ

Á¦10Àå °æÀï ȯ°æ°ú ºÐ¼®

  • ÁÖ¿ä ±â¾÷°ú Àü·« ºÐ¼®
  • ½ÅÈï±â¾÷°ú ½ÃÀå¼öÀͼº
  • ÇÕº´, Àμö, ÇÕÀÇ ¹× Çù¾÷
  • º¥´õ °æÀï·Â ¸ÅÆ®¸¯½º

Á¦11Àå ±â¾÷ ÇÁ·ÎÆÄÀÏ

  • IBM Corporation
  • SAS Institute Inc.
  • Optum(a part of UnitedHealth Group)
  • FairWarning(acquired by Imprivata)
  • EXL Service Holdings, Inc.
  • Pondera Solutions(acquired by Thomson Reuters)
  • Cotiviti Holdings, Inc.
  • Change Healthcare
  • Wipro Limited
  • FICO(Fair Isaac Corporation)
BJH 24.02.01

The healthcare fraud analytics market is expected to grow at a CAGR of 20.47% from US$1.626 billion in 2021 to US$5.989 billion in 2028.

The healthcare fraud analytics market size is growing and focuses on detecting and preventing fraudulent actions in the healthcare business. Billing fraud, identity theft, and needless treatments all cost the healthcare system billions of dollars each year. Advanced data analytics techniques and algorithms are used in healthcare fraud analytics systems to uncover trends, abnormalities, and suspicious activity, allowing for proactive fraud detection and prevention. The healthcare fraud analytics market growth has enormous potential to reduce fraudulent activities, safeguard healthcare organizations from financial losses, and maintain the healthcare industry's confidence and integrity. In terms of market share, numerous industry competitors, such as specialized analytics solution providers, technology firms, and healthcare organizations themselves, are striving to grab a substantial chunk of the market. The market is likely to expand further as healthcare organizations invest in sophisticated analytics tools and technology to detect and prevent fraud in the sector.

Need for Cost Containment and Financial Loss Prevention in Healthcare Fraud Analytics Market.

A primary driver in the Healthcare Fraud Analytics industry is the requirement for cost conservation and financial loss avoidance. Healthcare fraud is projected to account for 3% to 10% of worldwide healthcare spending each year, according to NHCAA. This financial burden emphasizes the need to implement fraud analytics tools to detect and prevent fraudulent actions. According to research, employing such solutions can result in considerable cost reductions for healthcare organizations. The market's demand for advanced analytics solutions is being driven by a focus on cost conservation and financial loss avoidance.

Growing Awareness and Focus on Fraud Prevention Enhances the Healthcare Fraud Analytics Market Size.

In the Healthcare Fraud Analytics industry, there is a rising awareness of and emphasis on fraud prevention. In the Healthcare Fraud Analytics industry, the emphasis on fraud prevention drives market growth and innovation.

Government Initiatives and Regulations to Combat Healthcare Fraud in Healthcare Fraud Analytics Market.

The role of government actions and legislation in preventing healthcare fraud is crucial. Governments throughout the world are enacting stronger measures to combat fraud and defend the integrity of healthcare systems. These approaches include the creation of specialized anti-fraud teams, greater financing for fraud detection programs, and the passage of legislation to discourage and penalize fraudulent behaviour. Furthermore, governments work with industry players to create best practices, share information, and increase transparency in billing and claims procedures. Combating healthcare fraud provides financial responsibility, protects patients, and promotes a safer and more efficient healthcare system.

North America is a Market Leader in the Healthcare Fraud Analytics Market.

North America is the industry leader in healthcare fraud analytics market share. This can be linked to a variety of causes, including the region's rigorous regulatory framework, high healthcare spending, and rising occurrences of healthcare fraud. Furthermore, North America has a well-established healthcare system that places a premium on fraud prevention and compliance. The region's emphasis on preventing healthcare fraud, along with the deployment of advanced analytics technology, underpins its market leadership in Healthcare Fraud Analytics.

Rising Adoption of Electronic Health Records (EHRs) and Digital Health Systems in Healthcare Fraud Analytics Market.

The growing use of Electronic Health Records (EHRs) and digital health systems is having a significant influence on the Healthcare Fraud Analytics industry. By 2021, 96% of non-federal acute care hospitals in the United States have adopted certified EHR systems, according to the Office of the National Coordinator for Health Information Technology. This digitization of healthcare data gives a lot of information that fraud analytics systems may use to detect and prevent fraudulent activity. The integration of EHRs with digital health systems enables real-time monitoring, data analysis, and pattern identification, allowing healthcare organizations to discover fraudulent billing, coding errors, and other fraudulent practices in real time.

Key Developments:

  • In June 2022, Patient Engagement is a line of products from Change Healthcare that connects touchpoints throughout the patient experience, increasing access and improving communication between patients and physicians. Change Healthcare services laid out income cycle the board capacities, joined with Luma Health's industry-driving arrangements, empower suppliers to coordinate functional, clinical, and monetary excursions, bringing about a more improved understanding experience.

Company Products:

  • Fraud Detection Systems: IBM provides sophisticated analytics tools that leverage machine learning and artificial intelligence to detect trends and anomalies that may indicate fraudulent activity. Large amounts of healthcare data, such as claims, billing records, and patient information, are analyzed by these systems to detect unusual behavior and probable fraud.
  • Real-time Monitoring and Alerting: Optum offers real-time monitoring systems that continually monitor healthcare transactions and data streams. These systems employ rule-based algorithms to detect and notify of potentially fraudulent activity, allowing for quick intervention and prevention.
  • Identity Verification: LexisNexis Risk Solutions offers identity verification technologies to assist healthcare organizations in validating the identities of their patients, providers, and other entities. To avoid identity theft and fraudulent actions, these solutions make use of powerful identity verification algorithms and databases.
  • Provider Network Analysis: Optum's fraud analytics solutions use network analysis techniques to uncover linkages and interconnections among healthcare providers, patients, and other organizations. This study aids in the detection of fraudulent schemes involving cooperation, incorrect billing practices, or organized networks.

Segmentation

By Component

  • Software
  • Services

By Deployment

  • On-Premises
  • Cloud-Based

By Application

  • Insurance Claims Review
  • Payment Integrity
  • Identity & Access Management
  • Others

By End-User

  • Healthcare Payers
  • Healthcare Providers
  • Government Agencies
  • Others

By Geography

  • North America
  • United States
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • UAE
  • Others
  • Asia Pacific
  • Japan
  • China
  • India
  • South Korea
  • Indonesia
  • Taiwan
  • Others

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Overview
  • 1.2. Market Definition
  • 1.3. Scope of the Study
  • 1.4. Market Segmentation
  • 1.5. Currency
  • 1.6. Assumptions
  • 1.7. Base, and Forecast Years Timeline

2. RESEARCH METHODOLOGY

  • 2.1. Research Data
  • 2.2. Sources
  • 2.3. Research Design

3. EXECUTIVE SUMMARY

  • 3.1. Research Highlights

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porters Five Forces Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Power of Buyers
    • 4.3.3. Threat of New Entrants
    • 4.3.4. Threat of Substitutes
    • 4.3.5. Competitive Rivalry in the Industry
  • 4.4. Industry Value Chain Analysis

5. HEALTHCARE FRAUD ANALYTICS MARKET, BY COMPONENT

  • 5.1. Introduction
  • 5.2. Software
  • 5.3. Services

6. HEALTHCARE FRAUD ANALYTICS MARKET, BY DEPLOYMENT

  • 6.1. Introduction
  • 6.2. On-Premises
  • 6.3. Cloud-based

7. HEALTHCARE FRAUD ANALYTICS MARKET, BY APPLICATION

  • 7.1. Introduction
  • 7.2. Insurance Claims Review
  • 7.3. Payment Integrity
  • 7.4. Identity & Access Management
  • 7.5. Others

8. HEALTHCARE FRAUD ANALYTICS MARKET, BY END-USER

  • 8.1. Introduction
  • 8.2. Healthcare Payers
  • 8.3. Healthcare Providers
  • 8.4. Government Agencies
  • 8.5. Others

9. HEALTHCARE FRAUD ANALYTICS MARKET, BY GEOGRAPHY

  • 9.1. Introduction
  • 9.2. North America
    • 9.2.1. United States
    • 9.2.2. Canada
    • 9.2.3. Mexico
  • 9.3. South America
    • 9.3.1. Brazil
    • 9.3.2. Argentina
    • 9.3.3. Others
  • 9.4. Europe
    • 9.4.1. United Kingdom
    • 9.4.2. Germany
    • 9.4.3. France
    • 9.4.4. Italy
    • 9.4.5. Spain
    • 9.4.6. Others
  • 9.5. Middle East and Africa
    • 9.5.1. Saudi Arabia
    • 9.5.2. UAE
    • 9.5.3. Others
  • 9.6. Asia Pacific
    • 9.6.1. Japan
    • 9.6.2. China
    • 9.6.3. India
    • 9.6.4. South Korea
    • 9.6.5. Indonesia
    • 9.6.6. Taiwan
    • 9.6.7. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 10.1. Major Players and Strategy Analysis
  • 10.2. Emerging Players and Market Lucrativeness
  • 10.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 10.4. Vendor Competitiveness Matrix

11. COMPANY PROFILES

  • 11.1. IBM Corporation
  • 11.2. SAS Institute Inc.
  • 11.3. Optum (a part of UnitedHealth Group)
  • 11.4. FairWarning (acquired by Imprivata)
  • 11.5. EXL Service Holdings, Inc.
  • 11.6. Pondera Solutions (acquired by Thomson Reuters)
  • 11.7. Cotiviti Holdings, Inc.
  • 11.8. Change Healthcare
  • 11.9. Wipro Limited
  • 11.10. FICO (Fair Isaac Corporation)
ºñ±³¸®½ºÆ®
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
»óǰ ºñ±³Çϱâ
Àüü»èÁ¦