WO2016130879A1

WO2016130879A1 - System and method to objectively measure quality assurance in anatomic pathology

Info

Publication number: WO2016130879A1
Application number: PCT/US2016/017668
Authority: WO
Inventors: Rodney S. Markin; Mark L. PRIEBE
Original assignee: Prairie Ventures, Llc
Priority date: 2015-02-13
Filing date: 2016-02-12
Publication date: 2016-08-18
Also published as: CA2976443A1; EP3257016A1; EP3257016A4

Abstract

Techniques are described that facilitate generation of a practice proficiency score related to the quality of a pathology department processes (e.g., quality of/accuracy of pathologist diagnoses, etc.). In one or more implementations, a method may comprise generating an assessment metric based upon a comparison of the first image attribute with a second image attribute. The first image attribute comprises an attribute associated with a digital image representing a specimen. The method also includes receiving a diagnostic confidence metric corresponding to the specimen. The method also includes receiving a diagnostic accuracy metric corresponding to the specimen. The method also includes receiving a turn around time metric corresponding to the specimen. The method also includes receiving a clerical accuracy metric corresponding to the specimen. The method also includes causing a processor to generate a score representing a proficiency corresponding to the specimen. The score is based upon the assessment metric, the diagnostic confidence metric, the diagnostic accuracy metric, the torn around- time metric, and the clerical accuracy metric.

Description

SYSTEM AND METHOD TO OBJECTIVELY MEASURE QUALITY ASSURANCE IN ANATOMIC PATHOLOGY

BACKGROUND

fOO^'OI j Anatomical (or anatomic) pathology is a medical specialty concerned with the diagnosis of disease based upon macroscopic, microscopic, biochemical, immunologic_, and molecular examinati n of organs and tissues,

SUMMARY

100031 Techniques are described that facilitate generation of a practice proficiency score related to the quality of a pathology department processes (e.g., quality of/accuracy of pathologist diagnoses, etc.). In one or more implementations, a method may comprise determining a first image attribute of a digital image of a specimen based upon at least one image characteristic- of the digital image, the digital image captured by an image capture device and generating an assessment metric based upon a compariso of the first image attribute with a second image attribute provided by a client device. The method also includes receiving a diagnostic confidence metric correspondin to the specimen. The diagnostic confidence ^'metric comprises an indication of whether a second .review of the specimen occurred. The method also includes receiving a diagnostic accuracy metric corresponding to the specimen. The diagnostic accuracy metric comprises a accuracy indication of a diagnosis of the anatomic pathology specimen. The method also includes receiving a turn around time metric corresponding t the specimen. The turn around time metric, comprises a time ranging from a case accession time to a ease sign out time. The method also includes receiving a clerieal -accuracy metric corresponding to the specimen. The clerical accuracy metric comprises clerical accuracy parameter pertaining -to the specimen. The method also .includes causing a processor to generate a score representing a proficiency corresponding to the specimen. The score is based upon the assessment metric, the diagnostic confidence metric, the diagnostic accuracy metric, the turn around time metric, and the clerical accurac metric. |0 O3| This Summary is provided to Induce a selection of concepts in a simplified forra that are further described below in the Detailed Description. This Summary Is not intended to identify key features or essentia! features of toe claimed subject ma ter, nor Is it intended to be used as an aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION Of THE DRAWINGS

\QW4\ The detailed description is described with reference to the accompanying figures, in the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears, The use of the same reference numbers in different instances n the description and the figures may indicate similar or identical items.

|000Sj FIG, I A is an Illustration of an environment in an example implementation that: Includes a server operable to generate a practice proficiency score in accordance with an example implementation of the present disclosure.

|{HH½f FIG, I B is an example graphical representation of an alert displayed within an unobscored portion o a display of a client device in accordance with an example implementation of the present disclosure,

|0i¾ ?| FIG. !C is an example graphical representation of an alert displayed within a display of a client de ice in accordance with an example implementation of the present disclosure, where the alert includes a URL link for accessing one or more reports graphically representing, a practice proficiency score.

f0Q68| FIG. 2 is an example graphical representation -of the practice proficiency score in accordance with an example implementation of the present disclosure,.

|0089) FIG. 3 Is another example graphical representation of the practise proficiency score in accordance with an example im lementation of the present disclosure,

|00!0| FIG. 4 Is another example graphical representation of the practice proficiency score in accordance with an example implementation of the present disclosure,

ftCt^'ii I FIG, 3 Is another example graphical representation of the practice proficiency score in accordance with an example implementation of the present disclosure,

|00i2| FIG. 6 is a flow diagram illustrating an example method for generating a practice proficiency score based upon multiple metrics corresponding to a specimen in accordance with an example implementation of the presen disclosure. DETAILED DESCRIPTION

|00I3) The term quality assurance refers to the documented evidence that a rocess o system performs according to predetermined specifications and quality attributes. The practice of anatomic pathology involves performing a subjective interpretation of microscopic tissue characteristics and objective data_* and to select specific language to convey a diagnosis. Th objective data, contained in the characteristics of the ceils, organisation of tissues, and relationship, to the organ on the whole, are preserved for the initial examination ors histologic glass slides or in a digitized image format. Many factors contribute to the lack of objectivity and diagnostic inaccuracy, including the training and skill of the pathologis quality of the slide, level of pathologist confidence and methods to access the quality of pathology services.

1001 ) One of the possible contributing factors h the use of an intra-deparirnentai consultation or second opinion method to assess the quality of a pathology service. The College of American Pathologists (CAP) has established guidelines for both intra- departmental and extra-departmental quality consultations. These guidelines appear to have failed to encourage quality assurance activities that result in optimal diagnostic accuracy, consistency in terminology, and timely care for patients. Over the past two decades., several studies have published the rates of diagnostic discrepancy. A discrepancy is defined as: when one pathologist renders a diagnosis and another pathologist looks at the same materia! and renders a different opinion/diagnosis.

fOOt.Sj Another likely contributing factor to the subjectivity is the absence of an objective measure to assess the quality of the pre-analytie. analytic and post-analytic processes of the: pathology service. Existing standardized scoring quality assurance programs are; External Quality Assurance (eQA) and Proficiency Testing (FT). These existing quality assurance programs are limited in their scope and suffer from lack of granularity and completeness of qual ity review,,

\ 16\ Thus, quality assurance measurements need to be applied across the pre~anaiyiical₅ analytical and post-analytieal diagnostic process to ensure the patient receives the best diagnosis possible. Accordingly, a system is disclosed that generates a practice proficiency score related to the quality of a pathology department process and associated diagnosis ref¾ted artifacts. The system receives information identifying a case submission; receives information, digital images, and data associated with the case from the laboratory/pathology information system; receives whole-slide imaging files of digital microscop technologies: receives information related to the quantitative and qualitativ analysis performed by an image analysis application; receives quality assurance review of the final diagnosis for accuracy as provided by external reviewers; and causes calculation of an objective measure of pathology service proficiency. The objective measure enables comparable comparisons in generating benchmarking tools that e identify professional strengths ami areas of improvement when compared to a matching peer group and practice, |e0i?l Certain embodiments as disclosed herein provide for systems and methods to objectively measure quality assurance in anatomic pathology. However, although various, implementations of the presen disclosure are described herein, and presented by way of example specific to anatomic pathology, the disclosure has other embodiments and applicability to other disciplines as to not limit the scope or breadth of the current disclosure, in one embodiment, a system and method of quality assurance that generates a unique Practice Proficiency Score (PPS) thai is used to benchmark quality performance in pathology and for medical practice continuous improvement. The program ^'comprises five. (5) metrics that may have a role in reducing diagnostic error.

J80l8f FIG. I A illustrates an environment 100 in an example implementation tha is operable to facilitate generation of an objective measure of pathology service proficiency corresponding to a case ^'in accordance with the present disclosure. The ease may comprise one or more metrics relating to a specimen. For example, the specimen may comprise an anatomic pathology specimen and/or a clinical laboratory specimen, The anatomic pathology specimen may include, hut is not limited to. specimen used to determine the presence of cancer or dermal disorders. The illustrated environment 100 includes a server 102 and one or more client devices 104 that communicates with the server 102 via one or more networks 106-

(0019} The server 102 may be configured in a variety of ways. For example, the server 2 may be configured as ne or more server computers that are capable of commurs seating Ove ^'a wired o wireless network 106. The client device 104 may also be configured^' in a variety of ways. For example, the client device 104 may be configured as a computing device independent of the server 102, For instance_* the client device 104 may comprise a desktop computing device, a server computing de ice, a laptop computing device, a tablet, a mobile electronic device, and so forth, that is capable of communicating over a wireless network. Additionally, although one client device 104 is Illustrated, it is understood that the server 102 may provide the functionality described herein to multiple mobile electronic devices 104, The client deyfce(s) 104 can he utilized to provide the system 100 one or more of the quality metrics described below,

100201 The network 1 6 may -assume a wide variety of configurations. For example, the network 106 may comprise any of a plurality of communications standards, protocols and technologies, including, but n st limited to: a 3G communications network, a 4G communications network, , a Global System for Mobile Communications {GS } environment an Enhanced Data GSM Environment (EDGE) network, a high-speed downlink packet access (HSDPA) network, a wideband code division multiple access (W» CDMA) network, a code division multiple access (CDMA) network, a time division multiple access (TDM A} network, Bluetooth. Wireless Fidelity (Wi-Fi) (e.g., IEEE 802, 1 l . IEEE .§02.1 l b. IEEE S02, 1 1 g and/or IEEE 802.1 1 n), voice over Internet Protocol (VoIP), Wi- AX. a protocol for email (e.g., internet message access protocol (IMAP) and/or post office protocol (POP)) environment, -an instant messagin (e,g„ extensible messaging and presence protocol (XMPP) environment Session Initiation Protocol for Instant Messaging and Presence Leveragin Extensions (SIMPLE), and/or .Instant Messaging and Presence Service (IMPS), and/or Short Message Service (SMS)), or any other suitable communication protocol, that facilitates eommunicaiion between the server 102 and the client device 104,

f 002 I f in FIG. I A, the server 102 and the client device 104 are illustrated as including a respective processor 1 16 or 1 1 8; a respective memory 1 0 or 122; and a respective communication module 1 4 or 126. in the following discussion, elements -of the server 102 are described with reference to FIG. I A, Respective elements and/or reference numbers related to the client device 104 are shown in parentheses, Where appropriate, elements of the client device 1 4 are described separately,

0 221 The processor 1 16 ( 1 I S) provides processing functionality for the server 102 ^'(client device 104) and may Include an number of processors, micro-controllers, or other processing systems, and resident or external memory for storing dais and other information accessed or generated by the server 102 (client device 104), The processor 1 16 ( 1 18) may execute one or mom software programs which Implement techniques described herein. The processor 1 16 ( 1 ! 8) is not limited by the materials from which it is formed or the processing mechanisms employed therein and, as such, may he implemented via semkonduetor{s) and/or transistors (e.g. , electronic integrated circuits (iCs})< and so forth.

f(M)23| The memory 120 ( 122) is an example of tangible computer-readable media thai provides storage functionality to store various date associated with the operaiioft of the server 102 (client device 10 }, such as the software program and code segments mentioned^' above, or other data to instruct the processor ! 16 ( I I 8) and other elements of the server 102 (client device 104) o perform the steps described herein. Although a single memory 1 0 (122) is shown, a wide variety of types and combinations of memory may be employed.. The memory 120 (122) may be integral with the processor ! ^"16 (1 18), stand-alone memory, o a combinatio of both. The memor may include, fo example, removable and nonremovable memory elements such as RAM, ROM, Flash (e.g.. SO C*rd„ mmi~8D card, mi^'cro-SO Card}, magnetic, optical, USB memory devices, and so forth,

100241 The communication module 124 (126) provides functionality to enable the ^'server 102 (client device 104)_. to communicate with one or more networks (depicted in FIG. 1A as network 106), In various implementations, the communication module 124 ( 126) may^¬ b representative of a variety of communication components a id functionality including, but not limited to: one or more antennas; a browser; a transmitter and/or receiver (e.g., radi frequency circuitry); wireless radio; data ports; software interfaces and drivers; networking interfaces; data processing components; and so forth,

fit 2S the one or more networks 106 may fee representative of a variety of different communication pathways .and network connections which may be employed, individually or in combinstions, to communicate among the components of the environment 100. Thus, the one or more networks 106 may be representative of communication pathways achieved using a single network or multiple networks. Further, the one or more networks 106 are representative of a variety of different types of networks and connections that are contemplated, including, but not limited to; the Internet; an intranet; a satellite network; a cellular network; a mobile data network: wired and/or wireless connections; and so forth. |O026| Examples of wireless networks include, but are not limited to: networks configured for communications according to: one or more standard of the institute of Electrical and Electronics Engineers (IEEE), such as 802, 1 1 or 802.16 {Wi-Max} standards: Wi-Fi. standards promulgated by the Wi-Fi Alliance; Bluetooth standard promul ated by the Bluetooth Special interest Group; and so on. Wired communications are also contemplated such as through universal serial feus (USB), Ethernet,. serial connections, and so forth. fO02?| As shown i FIG. LA, the client device 104 includes a touch-sensitive display 132, which ears be implemented using a li uid crystal display, an organic light emitting diode display, or the like. In some embodiments, the touch-sensitive display 1 32 may include a touch panel 134,. The touch panel 134 may be, but is not limited to; a eapacidve touch panel a resistive touch panel, an infrared touch panel combinations thereof, and the like. Thus, the display 132 ma be configured to receive input from a user and display information to the user of the client device 104, For example, the display 132 displays visual output to the user. The visual output may Include graphics, text, icons, video, interactive fields configured to receive input from a user, and any combination thereof (collectively termed "graphics'"}. In some embodiments, some or all of the visual output may correspond to user-interface objects, further details of which are described below, f0§28{ The display { 32 is communicatively coupled to a display controller 136 that is configured to receive and/or transmit electrical signals to the touch-sensitive display 132, In an implementation, the touch panel 134 Includes a sensor, an array of sensors, or the like, configured to accept input from a user based upon haptic and/or tactile contact. The touch panel 134, in combination with the display controller 136 (along with any associated modules and/or sets of computer-readable instructions in memory 122), detects a point of contact (or points of contact), as well as any movement or breaking of the contact, on the touch panel 134 and converts the detected contact (e.g., a finger of the user, a stylus, etc.) into electrical signals representing interactions wit user-interface objects (e.g., buttons, custom views, icons, web pages, images, web page links, etc.} that are displayed through the display 2, The client device 104 ma further include one or more input/output (I/O) devices 1 38 (e.g., a keypad, buttons, a wireless input device, a thumbwheel input device, a traekstiek mput device, and so on). The I/O devices 1 38 may include one or more audio I/O devices, such as a microphone, speakers, and so on. Thus, I/O device 1 8 may include a keyboard configured to receive user In u In an implementation, the keyboard may be integrated with the client device 104, or the keyboard may be a peripheral device that is configured to Interface with the device 104 {e.g., via a USB port, etc.),

|0829] The client device 104 is illustrated as Including a user interface 142, which is storable in memory 122 and executable by the processor 1 18, The user Interface 1 2 is representative of functionality to control the display of information and data to the user of the client device 104 via the display 132, In some Implementations, the display 132 may not be integrated into the mobile electronic device and may instead be connected externally using universal serial bus {USB}, Ethernet, serial connections, and so forth. The user interface 142 may provide functionality to allow the user to interact with one or more applications 144 o the client device- 104 by providing Inputs via the touch panel 4 and/o the I/O devices 138. For example, the user interface 142 may cause an application programming interface (API) to be generated to ftrrnish functionality to ars application 1 4 to configure the application for display by the display 132 or in combination with anothe display. In embodiments, the API may further furnish functionalit to configure the application 144 to allow the user to interact with an application by pro viding inputs via the touch panel 134 and/or the I/O devices 138.

100301 Applications 144 may comprise software, which is storahle in memory 122 and executable by the processor I IS, to perform a specific operation or group of operations to furnish specified functional sty to the client device 104.

[003 if As shown, the computing device 102 and/or the client device 104 may be in communication with one or more image capture devices 146. The image capture devices 146 comprise devices (e.g., cameras) for capturing images and/or videos. For instance, the image capture devices 146^' may be configured to capture anatomic and/or clinical laboratory specimens and provide image data representing the captured imagery to the computing device 102 and/or the client device 104. I one or more implementations, the image capture device 1 6 is configured to generate image including one or more spectral characteristics of -the anatomic and/or clinical ^"laboratory specimens, in one or more implementations, the Image capture device 146 is configured to generate image including one or more hue characteristics of the anatomic and/or clinical laboratory specimens. |0Θ32] As shown in FIG, 1 A, the server 102 includes a quality measurement module 148 Chat is storahie in the memory 120 and executable by the processor 1 16, The uality measurement module 148 is representative of functionality - to generate a quantitative metric based upon one or .more selected quality metrics corresponding to an anatomic pathology tissue sample. For example, as described in greater detail below, the server 02 receives the quality metrics from one or more client devices 104, In response* the quality measurement module 1 8 generates a quantitative metric corresponding to the quality metrics,

|G833| In one or more implementations, the system 100 ma comprise a Health Insurance Portability and Accountabil ty Act (H1PPA) compliant cloud-based computer architecture for managing and measuring externa! quality assurance _.review, initially, a pathology practice (e.g., a pathology professional, a group of pathology professionals) identifies and selects a case (e.g., an anatomic pathology tissue sample) for quality: assurance review based on defined criteria and published recommendations of The Association of Directors of Anatomic and Surgical Pathology (AD ASF). These recommendations take, into consideration the structure, responsibilities, and needs of academic anatomic pathology laboratories that have an active residency or fellowship. These recommendations can be modified according to specific institutional circumstances and needs.

|0034f Depersding on departmental resources, a number of methods may serve as qualit assurance case reviews including; review of a randomly selected percent of eases (eg., I %_> 2%, 5%, 10% depending on the size of practice and available staff time to conduct reviews). 100351 In one embodiment, a minimum of twent eases per pathologist can be submitted to ensure statistical significance is maintained for quantitative analysis and comparison benchmarking. Objective data relative to the case (e.g., data representing imagery of the anatomic pathology sample captured by the image capture devices 146, data representing turn-around-time related to the anatomic pathology sample, data representing diagnosis related to the anatomic pathology sample) is aggregated, collated, and converted in digital format as an e-ease for upload via a secure network (e.g., network 106) to the server 102 via a client device 104.

§036f Once the upload of the data corresponding to the case Is completed, a quality assurance analysis is performed b the server 102. For example, the server 102 automatically applies a digital slide analysis to the image data representing the anatomic pathology sample to assess image attributes and the completeness of the objective data submission as described in greater detail below, if the quality of the digital slide is insufficient, a visual qualitative assessment is performed to provide m objective measure or score for quality,. The associated scores- are: 1 ^::: Good; 2 —Adequate; and 3 ^« Unacceptable. If the digital slide (e,g,, image data of the anatomic pathology specimen) is rejected, the anatomic pathology practice can prepare a new glass slide, reimage the sample within the glass slide, and resubmit the data representing the updated imagery via the client device 1 04.

)003?| In another implementation, as shown in FIG, I A, th sever 102 is communicatively connected to a staining device 149, The-: staining device 149 comprises a device that can apply a stain to the specimen. For example, the staining device 149 can apply a variety of staining compounds (e.g., stain) to Che specimen. For instance_* the staining compounds cm comprise biological stains such as antibodies or chemical stains including dyes and pigments. The staining device 149 includes a staining head 15-1 that can be positioned proximate to the specimen. Once the staining head 151 is positioned proximate to the specimen, the staining head 15 1 can disperse the stain. In some Implementations, the image capture device 146 is integral with the staining device 149, In other implementations, the staining device 149 and the image capture device 149 are discrete devices.

|W38| As shown i FIG, I A, the server 102 includes an image analysis module 150, which is storable in the memory 1 0 and executable by the processor 1 16. The image analysis module 150 is representative of functionality tor Identifying tumor tissueCs) based upon the provided image data. In an implementation of the present disclosure, if the digital slide is of insufficient quality, the module 150 transmits a request to the client device 104 over the network 106, The request can automatically cause the user Interface 1 2 of the client device 104 to display a graphical interface requesting submission of a new digital slide. If the quality of the slide image Is sufficient for quality review, then the digital slide can be analyzed and Interpreted by the reviewing pathologist,

10039) As described above, the image analysis module I SO provides functionality to Identify one or more pixels as corresponding to a- tumor tissue. For Instance, the processor 1 1.6 can compare pixels of the digital image representin the specimen to one o more pixels (e.g.,. adjacent pixels) of the digital image to compare one or more characteristics of the pixels. In an implementation, the processor i !6 iterates through the digital image comparing pixel characteristics. Based upon one or more pixel characteristics, the processor 1 16 can identify (e.g., determine) a region of interest that may correspond to tumo tissue. The image analysis module 150 retains information to cause the processor 1 16 to identity regions of int rest within the digital image of the specimen, In implementations, the processor 1 16 is configured to identify regions of interest based upon an identifying characteristic (e.g., the pixels having a hue characteristic corresponding to purple). Thus, the processor 1 16 iterates through the digital image to identify regions of interest where pixeis within that region of interest have characteristics corresponding to the identifying characteristic. For instance_* the hue (color) characteristics of the pixels within the regio of interest may differ from the hue characteristics of pixels no within the region of interest, For instance, the hue (color) characteristics of the pixeis within the region of interest are purple (due to staining of th ^'specimen), and the hue characteristics of the pixeis outside the regio of interest differ substantially (e.g., the hue characteristics correspond to pink, etc). Thus, the pixels having characteristics that at least substantially match the identifying characteristic are deemed to he within the region of interest. As described above, the pixels within the region of interest may be indicative of tumor tissu within the specimen.

|00 0| Upon determining, a region of interest within the digital image, the image analysis module I SO provides functionality to determine an image quality of the digital image of the specimen. In one or more implementations, one or more baseline characteristics may be stored in the database 152. In an implementation, the baseline characteristics may comprise baseline digital Images having regions of interest. The baseline digital Images comprise digital images having baseline pixel characteristics (e.g., the baseline pixel characteristics of the region of interest), In another implementation, the baseline characteristics may comprise baseline data corresponding to pixel characteristics. For instance, the baseline characteristics may comprise hue characteristics indicating an acceptable image quality (e.g., the pixeis indicative of tumor tissue are sufficiently purple). For example, the baseline digital images may represent specimens having tumors therein, and the regions of interest correspond to the tumors. {0041) The image ana s module 150 provides functionality to compare a characteristic of at least one pixel within the identified region of interest the baseline characteristics to determine whether the pixels within the identified region are above a threshold- Pixels within identified region having characteristics at or exceeding the threshold may comprise pixels indicative of sufficient image quality, in an implementation, the threshold may comprise a value indicative of a^' suitable hue characteristic. For example, the processor 1 16 determines whether a hue characteristic of a pixel within the identified region is equal to or exceeds the baseline characteristics. Thus, the processor 1 16 cm compare a hue characteristic of the digital image provided by the client device 104 to a similar hue characteristic of the baseline digital image to determine the image quality of the digital image, f n response, the processor I \6 determines whether the image quality of the digital image is sufficient, in some instances, (he processor 1 16 generates a score corresponding to the difference In the hue characteristics betwee the pixel within the identified region and the baseline characteristics (e.g., the hue characteristics of the digital Image is within ten percent ( 0%) of the hue characteristics of the baseline characteristics). Digital images of insufficient quality may comprise pixels having hue characteristics that are less than the hue characteristics of the baseline image (e.g., the pixels are "less^*" purple with respect to the baseline characteristics), which may he indicative of insufficient stain applied to the specimen,

| 42j In instances_; where the server 102 deems the digital image to be of insufficient quality (e.g., pixels within the identified region of interest have characteristics below the quality threshold), the server 102 can cause the staining device 149 to re-apply stain to the specimen. For instance, the server 102 may cause the staining device 149 to re-apply the stain in real-time or near real-time (e.g.. causing re-application of the stain upon determining an Image- of Insufficient qualit ),. Once the stain has been re-applied to the specimen, the server 102 causes, the image capture device 146 to capture another digital image of the specimen in real-time or near real-time. Thus, the server 102 can transmit a signal causing the staining dev ice 149 to re-apply the stain and causing t he image capture device 1 6 to- capture another digital image of the specimen- whe the server 102 determines that the .original digital image is not of sufficient quality, in some instances, the server 102 can cause re-orientation of the staining device and/or the image capture device 146 (e.g._* the server 102 is operabiy coupled to at least one of a mechanical component of the image capture device 146 or an eleetr -mechani sl component of the image capture device 146). For instance, the server 102 can issu commands over the network 106 to cause the image capture device 146 to adjust an angle of i cidence with respect to the specimen (e.g._* cause ilk image capture device 146 to re-orient itself with respect to the specimen to alter the angle of incidence}. In some Implementations, the server 102 can cause the image capture device 1 6 to adjust a magnification characteristic of the image capture device 1 6. For instance, if the image quality of the digital image is determined to he insufficient, the server 102 can issue one or more commands to cause the image capture device 146. to adjust a magnification characteristic (e.g., a higher magnification characteristic, a lower magnification characteristic-, etc.) compared to the magnification characteristic of the original digital image. This process may continue until the server 102 determines that the digital Image Is of sufficient quality.

10043} Once a digital image of sufficient quality has been provided, the Image analysts, module 150 applies qualitative and quantitative Image analysis (Q21A.) and enhancement to measure the quality of the digital slide (e.g., the digital image of the anatomic pathology specimen, the image data representing the anatomic pathology specimen). For example, the colors of stains can be enhanced, by the module i 50 or even changed to provide more color contrast in eounterstained samples, in an implementation, the quality analysis module 150 comprises a suitable image analysis module (e.g., a Tissue ark® image analysis tool or the like) that generates a quantitative visualization of tumor probability for regions within the marked-up boundary. Regions of high tumor probability may be labeled red and regions of low tumor probability may be labeled pale blue/transparent. ^'This quantitative visualisation allows highlighting areas of non-tumour such as stroma, inflammation, and necrosis that are included within the mucrodissee iem boundary. After the quality is determined and image analysis of the digital image (e.g., image data) is performed, the digital slide can then be analyzed and interpreted by a reviewer (e.g., b a sub-specialty expert pathologist) who views the digital slide at the user interface at a client device 104 communicatively connected to the server 102.

I_.S0 4I Furthermore, the reviewing expert pathologist analyzes and interprets the digits! slide for -diagnostic accuracy compared to the .original subjective interpretation rendered b the submitting pathologist. An expert Interpretation is recorded from the reviewing suhspseialist via client device 104.am! stored as a reviewer quality data management data, input (e.g., a et ic as described In greater detail feelow) within a database 152 of the server 102 for farther processing by the module- 148. For instance, the reviewing pathologist transmits an assessmen of an imag attribute of the specimen.. The attribute (e.g.,assessment .metric), for example, may comprise indications of cancer, disease, or the like. The assessment may comprise a diagnostic accuracy review measured as one of the standard and/or acceptable values. For instance, the assessment (e.g., feedback, etc.) may comprise; (I) Concordant: Preferred diagnosis is substantially identical with the target diagnosis; (2) Concordant with Comments: Would like to add -a comment or provide some- constructive, feedback to the ease; (3) Minor Discordant: Disagreement not clinically relevant; {4} Discordant: Disagreement, clinical ly .-relevant:, but does not change the original diagnosis^* and 0} Major Discordance: Disagreement that may result in a change in the Initial diagnostic report and impact patient -care.

£0845f In another implementation, the server 102 includes an image determination module I S3 that Is stored in the memory 120 and executable by the. processor- 1 16. The image determination module 53 represents functionality for determining an Image attribute of the digital image. The database 152 retains multiple digital images (e.g., a library of digital images) representing already completed cases, . for example, these digital Images may comprise images of a specimen where a final ^'determination (e.g., prev iously analyzed) has been made regarding the specimen (e.g.. cancer, disease, etc.). The database I 32 can retain associated information with each digital image indicting the final determination (e.g.. reference digital images), or example, the reference digital images include metadata associated therewith such that the metadata provides the final determination. The image determination module 1 S3 can cause the processor 1 16 to determine an image attribute of the current digital image based upon a comparison with one or snore reference digital images. For instance, as described above, the processor 1 16 identifies; a regio of Interest. The processor 1 16 can compare the characteristics of the pixel within the identified region with characteristics of corresponding pixels within a region of interest in the reference digital images. For example, the captured digital image comprises an image representing a specimen having, a tumor therein. The processor 1 16 identifies a subset of pixels as representing the tumor (e.g., the region of interest) based upon the characteristics of die pixels representing the tumor with respect to the characteristics of the pixels representing the remaining portion of the specimen. The processor 1 16 then compares at least one pixel characteristic of a pixel within the identi fied region with a pixel characteristic of a pixel of at least one reference digita image, in some implementations, the processor 1 16 compares the pixel characteristic with a pixel charac teristic of a pixel within an identified region of a group of reference digital i mages. For example, the processor 1 16 c sts pares the pixel characteristic with a pixel characteristic of a pixel within an Identified region of each reference digital image, in some implementations, the processor 1 16 (e.g., server 102) can cause the image capture device 146 to adjust a magnification characteristic such that the image capture device 146 generates a digital image corresponding to the region of interest with the magnification characteristic. For example, once the region oil merest is identified within a first digital image, the processor 1 16 causes the image capture device 146 to capture and generate a second digital image of the specimen having a different magnification characteristic with respect to the first digital Image, for Instance, the second digital image may have a higher magnification characteristic of the region of interest compared with the first digital image.

\M46 In some implementations, a spectral analysis may be applied to the digital image, for instance, the processor I i 6 may select one or more pixels within the identified region and apply a Fast Fourier transform to these pixels to generate a spectral representation of the pixels. In the implementation described above, the processor 1 16 can generate a spectral representation corresponding to the tumor.

|0Q4?| Based upon this comparison, the processor 1 16 determines an image attribute of the identified region. For example, based upon the comparison, the processor ϊ 16 determines whether the pixel within the identified region comprises cancer, disease, or the like. The processor 1 16 can then compare the determined image attribute with the image attribute provided by the client device 104 and provide a metric associated therewith. In an Implementation, the processor 1 16 generates an assessment metric based upon the comparison of the processor determined image attribute and m image attribute provided by the client device 104, For instance, the image attribute provided by the client device 104 may comprise data representing a decision by a pathologist regarding the specimen (e.g., cancer, disease, etc). Thus, the processor 1 16 compares the processor 1 16 generated image attribute with an attribute of the specimen provided by the client d v e 04, Fo example, If the detennlncd image attribute matches the provided image attribute, the processor 1 16 generates an assessment metric indicating that she determined image attribute and tbe provided image attribute are concordant. If the determined image attribute does not match the provided image attribute, the processor 1 16 generates an assessment metric indicating discordant or major discordance,

10048) Diagnostic Confidence indicates whether the case had a second review prior to sign-out which may have been a formal consult or an informal review. Results of the consult or review can be found in the case notes. In implementations, the server 102 receives data from the client device 104 corresponding to- Diagnostic Confidence. For instance, the server 102 can be provided data corresponding to the instant case that includes information of whether, prior to sign-out, the ease had a second review. Based upon this data, the server 102 generates a metric representing a Diagnostic Confidence. For instance, the metric may represent a score of one (Ί ) Indicating a second revie of the case did occur or zero (0) indicating a second review of the case did not occur. In some instances, based upon previous submissions of the pathologist, the- server 104 can apply confidence Interval rankin to the Diagnostic Confidence metric. In these implementations, the database 152 maintains previous submissions provided by the pathologists such that the processor 1 can compare currently submitted data against previously submitted data and previously generated metrics and/or scores. For instance, in the event this patho!ogisthas submitted cases having a second revie but the second review was discordant, the processor 1 \ 6 can •generate a metric that is lower that metric where the pathologist has submitted cases having a second revie -and the second review was- concordant with the initial diagnosis. Thus, the processor 1 16. can generate a confidence interval related to these metrics and apply the confidence interval to any newly generated Diagnostic Confidence metrics.

[00 9J Clerical Accuracy, may include, but is not limited, to accurate tracking of the specimen from collection to analysis ensuring -no mix-up of the specimen occurs. Thus_* the client device 104 can provide the server i 02 with data representing a lo that represents tracking of the specimen from collection to analysis. Additionally, clerical accuracy may also Include accurately recording the diagnosis from the specimen into the appropriate file. Based upon this information, the server 102 Is configured to generate- a Clerical Accuracy arameter (e.g., metric) indicating whether The Clerical Accuracy parameter (e.g., parameter representing the clerical accuracy) is recorded in the database 1 52 as; (1) Concordant: Preferred diagnosis is ._.substantially identical with the target diagnosis; (2) Concordant with Comments: Would Hfce to add a comment or provide constructive feedback to the clerical.eomporient of the case; (3) Minor Discordant; Clerical inaccuracy not-clmicaily relevant; (4) Discordant: Clerical inaccuracy, clinically relevant- out does not change the diagnosis; or (5) Major Discordant; Clerical inaccuracy that ma result in. a change in the initial^' diagnostic report, For instance, the processor 1 16 generates a Clerical Accuracy metric eased upon data within the log and/or information provided by the client device 104. !n some implementations, the processor 1 16 identifies whether the specimen was accurately recorded at each transaction point within the collection to analysis chairs (e.g., whether specimen was recorded at collection, whether specimen was recorded at imaging, whether specimen was recorded at analysis, etc.). Thus, the fog can contain data corresponding to eac recordation (e.g., check-in) at each transaction point within the chairi. IS^' the server 102 determines that the specimen was properly recorded at each transaction point, the server 102 generates a first metric, and if the server 102 determines that the specimen was not properly recorded at each transaction point the server 102 generates a second metric that is lower than the first metric,

J00$0j Furthermore. Turn around Time (TAT) is based on a computer-generated time from the case accession time and the pathology case sign out time, in some instances, the time may be measured in seconds, minutes, or hours. Turn around time may Include ease accession, the time the specimen is collected to pathology case sign out time, and/or the time the submitting pathologist provides a diagnosis. A an absolute value, TAT is normalized by grouping in reports of laboratories with similar service hours and case complexity. In one or more implementations, the server 102 generates a TAT metric by measuring one or more time characteristics associated with the case. As described above, the client device 104 provides a log corresponding to the case. The log can contain data representing a time characteristic associated with each portion of the case. Thus, the client device 104 can measure and record one or more time characteristics corresponding to case accession time to sign out time. The processor 1 16 is configured to generate a Turn around time metric based upon t e recorded time characteristics,

180511 one embodiment, the quality measurement module 148 aggregates the automated assessment and objective assessment (e.g., input data) provided by the reviewer of the ease comparatively against a predefined set of criteria. The module 148 provides functionality that can support the pathologist through a clinical decision support ft eifc that guide the pathologist through the process of making an interpretation or opinion in order to arrive at a concordance or discordance for the slide .(e.g., image data representing the anatomic pathology specimen) and overall case.

08521 In one embodiment of HG. I A, quality assurance module 1 $ generates & unique Professional Practice Score (PPS) utilised to benchmark quality performance in patholog and for medical practice continuous improvement. The PPS score comprises five (5) quality metrics that have a role in reducing diagnostic error. For example, ne or more of the metrics may comprise confidence levels, diagnostic accuracy, image attribute, applied time, and completeness of information (e.g., whether required data for a case has been completed by the pathology practice or received at the server 102).

|0053] In one or more implementations, the server 102 is communicatively connected with the client devices 104. 1¾e el lent devices 104, in part, serve to. provide access to the quality measurement module 148- for the sub-specialist reviewing pathologist such that the subspecisllst reviewing pathologist can upload the input data representing one or more of the quality mettles. Thus* the server 102 can prov de functionality to: ( I) Host ease data information and digital slide images; (2) Serve as an application host site for sub-specialist to complete the case review; (3) Generate the graphical representations and provide clinical alerts for Major Discordant Reviews; and (4) Create observational data storage for future data minin and longitudinal analytics. Additionally, as described above, the uality measurement module 1 8 accesses the metrics generated by the server 102 as described above.

|0ftS4 With regards to metrics having Major Discordant Reviews from- the reviewing pathologist, the quality measurement module 148 can automatically generate an alert: 200 (see FIG. I B) indicating that a. Major Discordant Review has been received for the respective metric and automatically issue the alert to the appropriate client device 104. The client device 104, upon receiving- the alert. 200, can automatically. enerate a window 202 at an unobseured portion 204 (e.g.. Information^■ within the window 202 is viewable to the end user) of the display 132 such that an end user can be notified of the Major Discordant Review and employ necessary steps to remedy the situation for the patient. In an implementation, the alert ^'may -comprise textual informatio 206 regarding the Major Discordant Review and relevant identifying information regarding the case.

fOOSSj Irs one embodiment, the quality measurement module 148 applies a process for weighting of clinical concordance or discordance for both the submitting pathologist Input of objective numeric and clerical values and subspeeialist reviewer contribution of interpretation values of concordance and discordance to calculate and generate a Practice Proficiency Score. Discordance is applied through a weighted value based on a risk factor of contribution to clinical discordance and effect on patient care, A grading scheme is applied by the quality measurement module 148 that assigns a discrete value relative to what harm or potential harm may result from the interpretive error. Consideration can be given to communicating to risk management any error that has significant harm or impact on patient care. Fo example, the grading scale may comprise; No harm or impact on patient care (Labeled as "Minor Discordance*) and grade ~ 3; slight harm or impact on patient care (Labeled as "Discordance") and grade ^~ 5; and significant harm or alteration of clinical management (Labeled as "Major Discordance") and grade - iih The. Practice Proficiency Score- comprises_, -the normalized weighted mean of the five ( 5) quality metrics Identified above. The Practice Proficiency Score .can be determined s follows:

EQN- i

( S6| By detecting and analyzing variability in the Practice Proficiency Score, health care providers can access pathologist proficiency and guide continuous improvement initiatives to reduce diagnostic discordance. The Practice Proficiency Score Is uniquely applied to the pathologist and/or laboratory under review by plotting the Practice Proficiency Score In a benchmark funnel graph against a uniquely created experience curve based upon years of experience and monthly caseload by tissue type (see FIG. 2).

00571 Once the Practice Proficiency Score has been generated by the module 148, the module 148 can. transmit an alert 200 to the corresponding · pathologists) (e.g._* the pathologists) corresponding to the case). In one or more implementations, as shown in PIG^, f C the alert 200 comprises a Uniform Resource Locator (URL) link 210 that provides access to the Practice Proficiency Score and one or more reports (as described below and shown in FIGS, 2 through 5} that reside on the server 102. Once the respective pathologist interfaces with the URL link 210, the URL Jink 210 causes display of the one or more reports corresponding to the Practice Proficiency Score at the display 132 of the client device 104. In some implementations, once the client device 104 has communicatively connected to the server Ί 02 via the network (e.g.,. establishes a communication link, etc.), the module- 148 may automatically cause one or more reports corresponding t the Practice Proficiency Score to be displayed at the display 132 (e.g., user interface 142) of the client device 104.

[O05S1 The Quality measurement module 148 can also fee representative of generating and providing reports and constructi e feedback to the pathologist and/or pathology group. The reports corresponding ^'to. the anatomic pathology specimen are generated based on predefined. -criteria and/or- can be created ad-hoc based on queries generated fey th -end user through the user interface 142 of the client device 104. in some implementations, the quality measurement module 1 8 automatically generates s report in response to a request from the client device 104. In some instances, the report is based upon pre-defined parameters set forth it* a user profile 154. For instance, a user profile 1 54 ma he retained at the client device 104. and the user profile 154 may store one o more parameters defining reporting criteria for report generation. This user profile 1 S4 may also be stored in the server 102. In some embodiments, each time the client device 104 interfaces the server 102, the quality measurement module 148 determines whether an updates have been made to parameters set forth in the user profile 154. If so. the quality measurement module 1 8 automatically updates the corresponding parameters in the user profile 154 stored in the server 1 2. In other embodiments, the quality measurement module 148 generates reports dynamically (e.g., ad-hoe) based upon one or more requests from an end user. For instance, an end user may generate a first request for a first report by submitting the first request through a client device 104 to the server 102. in response, the module 148 generates a report based upon the first request (e.g.. according to the parameters set forth in the first request), in another instance-, an end user (e,g„ a different end user or the same end user) may generate a second request for a second report by submitting the second request through a client device 104 to the server 102, In response, die m dule 148 generates a report based upon the second request (e.g., according to the parameters set forth in the second request), |0059| In an em o iment_* the quality measurement module 148 generates one or more visual representations of the Practice Proficiency Score. In some embodiments, the reports may, in part, comprise the visual representations of the Practise- Proficiency Score. for nstance, the module 1 8 ca generate a multivariate graph of case revie metrics and individual assessment metrics corresponding to the Practice Proficiency Score, which can be stored longitudinally for qualitative and -comparative analysis (see FIG. 3% The visual representations may comprise a graphical me h d of displaying multivariate data, metrics, in the form of a two-dimensiona! chart of three or more quantitative variables represented on axes starting from the same point The star graph displays the performance metrics of the ongoin quality assurance program. At a glance, the pathologist member can interpret the following information based on plus or minus two {2} standard deviations (·*·/- 2SD) from the norm of the group: diagnostic accuracy, clerical error, esse complexity, diagnostic confidence, and image attributes which are relative to their risk adjusted peer group. 100601 The server 102 includes a benchmarking module 56, which is stored in the memory 120- and executable by the processor 1 16, The benchmarking, module 156 provides a comparative analysis; of the Practice Proficiency Score of one Pathologist or laboratory with corresponding (e.g., "bes match") peers of the same category, experience level, and specialty:. A Pathologist's mean Practice Proficiency Score is determined and compared to the peer group, +/- 2SD, using a Stock Graph or Pathologist's Resume, The Resume provides longitudinal comparison hy. specialty peer group.

f.8061| Furthermore, as shown in FIG, 4, the comparative analysis generated by the benchmarking, module 56 is applied to. turn around tim (TAT) versus ease complexity. This is a Kaplan-Meier plot of multivariate analysis based on case complexity and Turn araund-Time, Case complexity, for example, relates to the type of specimen analyzed. Case complexity Is determined by the highest assigned CFT code and broken out In the following manner: Qualifiers for comparatives are based on -five (5) day or seven (?) day laboratory operations. Additional variables are applied to calculate case complexity, which may include Imrounohistochemical <IHC) stains -and molecular tests. Bone decalcification cases are omitted as qualifiers -for complexity. The cas is calculated using the site TAT times for all cases sybm «ed.. regardless If the site is made up of a single pathologist or a group of pathologists.

β62) In one or more implementations, the benchmarking module 156^' provides benchmarking for pathologist and or group Practice Proficiency Score at six {¾} month intervals. On a six-month basis, longitudinal quality .assurance reports demonstrate changes in the PPS of e individual practitioners total cases submitted, which allows an objective erformance evaluation over time. Pathologists can track their performance levels on a timelier basis, and support the institution's efforts in creating an evidence-based process for credentiaiing. Interpretation based on longitudinal Practice^' Proficiency Score for each member Practice Proficiency Score is displayed graphically over time, and a trend line is generated. Pathoiogists can review the graph to evaluate which area had the greatest effect on the change of their longitudinal performance. The mean of the peer group, and the individual axes are adjusted to generate a symmetrical square for ease of interpretation, |O063| PIG, 6 illustrates an example method 600 for generating a" Practice Proficiency Score corresponding to a specimen in accordance with m example implementation of the present disclosure. The Professional Practice Score is generated based upon the metrics described herein. As described above, each metric measures a objective aspect of a case corresponding to a specimen based upon the pathologist submitting the case. As discussed above, the specimen may comprise an anatomic pathology specimen, a histologic specimen, a clinical specimen, a Mood film, a microbiology specimen, or the like. As shown in FIG. 6, the method 600 includes receiving an assessment of an image attribute metric of a digital image of a specimen (Block 602). For example, data representing a digital image and/or data representing an assessment relating to an image attribute of the disital image is received at a server 102 from a client device 104 over a network 106. In an implementation, the data representing the digital image is generated by an image capture device 146. As discussed above, an assessment related to (e.g., pertaining to, corresponding to) the digital image is provided to the server 102. The assessment may comprise a diagnosis pertaining to the specimen. The quality measurement module 1 8 may utilize this assessment, as described above, to determine a Professional Practice Score relating to the specimen. Additionally, as described above, the server 102 determines an assessment relating to the Image attribute. For Instance, the server 102 can compare image characteristics of the digital image to one or more basel n image characteristics of baseline image character! sties.

{80641 As shown, in FIG. 6, a ia nostic- confidence metric corresponding to the specimen is received (Block 604), In one or more implementations, a diagnostic confidence relating to the specimen is received at the server 102, Diagnostic confidence comprises data indicating whether the case had a second review prior to sign-out which may have been a formal consult or an informal review.

|0065| A diagnostic accuracy metric corresponding to the specimen is received (Block 606), in on or more implementations, diagnostic accuracy comprises data representing an interpretation by a reviewing pathologist of information regarding the case {e.g., the specimen). For ins ce,, the reviewing pathologist may provide a diagnostic accuracy that comprises (! ) Concordant: Preferred diagnosis is substantially identical with the targe diagnosis; (2) Concordant wi h Comments: Would like to add a comment or provide some constructive feedback to the case; (3) Minor Discordant: Disagreement not clinical ly relevant: (4) Discordant; Disagreement, clinically relevant, but does not change the original diagnosis; and (5) Major Discordance: Disagreement that may result in a change in the initial diagnostic report and impact patient care.

|0666j As shown in FIG. -6, a turn around time metric corresponding to the specimen is received (Block 60S), As discussed above, the turn around time comprises the time from the case accession time and the pathology case sign out time and is received at the server 102 from the client device 1.04. As described above, the server 102 and/or the client device 104 are configured to measure the turn- around time. A. clerical accuracy metric corresponding to the specimen is received (Block 610). In one or more implementations, the clerical accuracy comprises accurate tracking of the anatomic pathology specimen from collection to analysis ensuring no mix-up of the specimen occurs. Additionally, clerical accuracy may also include accurately recording the diagnosis from the specimen Into the appropriate file. Clerical Accuracy parameters are recorded and stored in the database 152 by the reviewing subspcciaiist (e.g., reviewing pathologist). Additionally, the server 102 is configured- to iterate through information pertaining to the specific case and determine a clerical accuracy based upon one or more defined characteristics^' within the Information. fO TJ A score representing a proficiency corresponding to the specimen is generated (Block 612). in one or mope implementations, the quality measurement module 148 generates a score that represents a proficiency corresponding to the specimen. As described above, ⁽he score comprises a Professional Practice Score (hat me s es proficiency of a pathologist (or pathologist group) with respect to a similar pathologist (or similar pathologist groups), and the score is based upon the metrics described above. In some implementations, the quality measurement module 148, once a client device 104 of a respective pathologist communicatively connects with the server, automatically causes display of information including a Uniform Resource Locator (URL) that provides access to the Practice Proficiency Score and one or more reports representing a -graphical visualization of the Practice Proficiency Score..

|t⁾ 68) Generally, any of the functions described herein can be implemented using hardware {e.g., fixed logic^■circuitry such as integrated circuits), software, firmware, or a combination of these embodiments. Thus, the blocks discussed in the above disclosure generally represent hardware (e.g., fixed logic circuitry .such as integrated circuits}, software, firmware, or a combination thereof In the instance of a hardware embodiment, for instance, the various blocks discussed the above disclosure may be implemented as integrated .circuits along with other functionality, Such integrated circuits ma include ail of the functions of a given block, system or circuit, or a portion of the functions of the block, system or circuit Further, elements of the blocks, systems or circuits may be implemented across multiple integrated^' circuits. Such integrated circuits ma comprise various integrated circuits including, but not necessarily limited to: a monolithic integrated circuit a flip chip integrated- circuit a. ntuUiehip module integrated circuit, and/or a mixed signal integrated circuit. In the instance of a software embodiment for instance, the various blocks discussed in the above disclosure represent executable instructions (e.g„ program code) that perform specified tasks when, executed on a processor. These executable instructions can be stored in one or more tangible computer readable media. In some such instances, the entire system, block or circuit may be implemented using its software or firmware .equivalent. In other instances, one part of a given system, block, or circuit may be implemented in software or firmware, while other parts are implemented in hardware, |006 ) Although the invention has been described in language specific to structural features and/or metodclogscat acts, if is to be understood that the invention defined In the appended claims is not neeessarl ly I ini ed to the sped fie features or acts described. Rather, the specific features n acts are disclosed as example forms of Im lementing, the claimed invention.

Claims

What is claimed is:

1 , A method comprising:

determining, via a processor, s first Ima e attribute o a digital image of a specimen based upon at least one pixel characteristic of the digital image, the digital image captured by an image capture device;

generating an assessment metric based upon a comparison of the first image attribute with a second Image attribute provided by a client device;

receiving a diagnostic confidence metri corresponding to the specimen, the diagnostic confidence metric comprising as indication of whether a second review of the specimen occurred;

receiving a diagnostic accuracy metric corresponding to the specimen, the diagnostic accuracy metric comprising an accuracy Indication of a diagnosis of the anatomic pathology specimen;

receiving a turn around time metric corresponding to the specimen, the turn around time metric comprising a time ranging from a case accession time to a case sign out time; receiving a clerical accuracy metric corresponding to the spec men, the clerical accuracy metric comprising a clerical accurac parameter pertaining to the specimen? and causing the processor to -generate a score representing a proficiency corresponding to the specimen, the score based upon the assessment metric the diagnostic confidence metric, the diagnostic accuracy metric, the tur around time metric and the- clerical accuracy metric,

2, The method as recited in claim 1, further comprising determining, via a processor_* an image quality of the digital image; causing th Image capture dev ce to generate a second digital image of the specimen when the image quality is below a threshold.

3, The method as recited in claim 2, further comprising causing a staining device to apply a stain to the specimen when the image quality is below the threshold.

4, The method as reeked In claim 2, wherein determining the ima e quality further comprises identifying a region of Interest of the specimen within the digital image; and comparing an image -characteristics of the region of interest with an image characteristic of the baseline region of interest within a baseline digital image.

5. The method recited in claim 4, further comprising comparing a hoe characteristic of a pixel within the region of interest with a hue characteristic of a pixel within the baseline region of interest.

The method as recited in claim I, further comprising causing the processor to generate an alert; causing transmission of the alert to -a client device, the aler comprising a Uniform Resource Locator link providing access to the score,

?. Th method as recited in claim I , wherein die specimen comprises at leas one of an anatomic pathology specimen or a clinical laboratory specimen.

8^, A co put ng device comprising:

a memory operable to store one or more modules; and

a processor communicatively coupled to the memory arid operatively coupled to so image capture device, the processor operable to execute the one or more modules to:

determine a. first image attribute of a digital image of a specimen based u n at least one pixel characteristic of the digital image, the digital image captured by an image capture device;

generate an assessment metric based upon a comparison of the first image attribute with a second image -attribute provided by a cl ient device;

receive a diagnostic confidence metric corresponding to the specimen,, the diagnostic confidence metric comprising an indication of whether a second review of the specimen occurred;

receive a- diagnostic accuracy metric corresponding to the specimen, the diagnostic accuracy metric comprising an accuracy indication of a diagnosis of the anatomic pathology specimen;

receive a turn around tim metric corresponding to the specimen, the torn around time metric comprising a time ranging from a case accession time to case sign out time;

receive a clerical accuracy metric corresponding to the specimen, the clerical accuracy metric comprising a clerical accuracy parameter pertaining to the specimen; and

generate a score representing a proficiency corresponding to the specimen, the. score based upon the assessment metric, the diagnostic confidence metric, the diagnostic accuracy metric, the turn around time metric, and the clerical accuracy metric_*

9. The computing device as recited in claim 8, wherei the processor is further operable to execute the one or more modules to determine an image quality of the digital image and causes the image capture device to generate a second digital image of the specimen when the image quality is below a threshold.

10. The computing device as recited in . claim wherein the processor is operahly coupled to a staining device, ihs processor further operable So execute the one or more modules to cause th staining device to apply a stain to the specimen when the image quality is below the threshold,

1 1. The computing device as recited in claim 9, wherein the processor s furthe operable to execut the one or mor modules to identify 8 region of interest of the specimen, within the digital image* and compare an image characteristics oflhe region of Interest with an mage characteristic of the baseline region of interest within a baseline digital image,

I2> The computing device as recited in claim^■ wherein the processor is further operable to execute the one or more modules to compare a hue characteristic of a pixel within the region of interest with, a hue characteristic of a pixel within the baseline region of interest.

13. The computing device as recited. In claim wherei the processor is further operable to execute the one or more modules to generate an alert and cause transmission of the alert to a client device, the alert comprising a Uniform Resourc Locator link providing access to the score,

14. The computing device as recited in claim S. wherein the specimen comprises at least one of an anatomic patholog specimen or a clinical laboratory specimen.

15. A system comprising:

an image ca t re device;

a . client device;

a server configured to communicatively connect to the client dev ice and operative!}' coupled to the image capture device over a network, the server comprising;

a memory operable to store one or more modules; and

a processor communicatively coupled to the memory. Ac processor operable to execute the one or more modules to:

determine a first image attribute of a digital image of a specimen based upon at least one pixel characteristic of rise digital image, the digital image captured by an image capture device;

generate art assessment metric based upon a comparison of the first image attribute with a second image attribute provided by a client device;

receive a diagnostic confidence metric corresponding to the specimen from the client device, the diagnostic confidence metric comprising an indication of whether a second review of the specimen occurred;

receive a diagnostic accuracy metric corresponding to the specimen from the client device, the diagnostic accuracy metric comprising an accuracy indication of a diagnosis of the anatomic pathology specimen:

receive a turn around time metric corresponding to the specimen from the client device, the turn around time metric comprising a time ranging from a case accession time to a case sign out time;

receive a clerical accuracy metric corresponding to the specimen from the client device, the clerical accuracy metric comprising a clerical accuracy parameter pertaining to the specimen; and

generate a score representing a proficiency corresponding to the specimen from the client device, the score, based upon the assessment metric, the diagnostic confidence metric, the diagnostic accuracy metric, the torn around time metric, and the clerical accuracy metric. 16, The system as recked in claim 15. wherein the processor is further operable to execute the one or more modules to determine m image quality of the digital Image and causes the image capture device to generate a second digital image of the specimen when the image quality is below a threshold,

! 7, The system as recited in claim 16, further comprising a staining device, the processor further operable to execute the one or more modules to cause the staining device to apply a stain to the spec men when the image quality is below the threshold,

IS. The system as recited i« claim 16. ^'wherein the processor is further operable to execute the one or more modules to identify a region ofmterest of the specimen within the digital image; and compare an image characteristics of the region of interest with art image characteristic of the baseline region of interest within a baseline digital image.

19, The system as recited in claim 15, wherein the processor is further operable to execute the one or more modules to generate an alert and to cause transmission of the alert to a client device, the alert comprising a Uniform Resource Locator link providing access to the score.

20, The system as recited in claim 35, wherein the specimen comprises at least one of an anatomic pathology specimen or a clinical laboratory specimen.